ECJ clarifies Database Directive scope in screen scraping case

EC on the legal protection of databases (Database Directive) in a case concerning the extraction of data from a third party’s website by means of automated systems or software for commercial purposes (so called 'screen scraping').

Flight data extracted

The case, Ryanair Ltd vs. PR Aviation BV, C-30/14, is of interest to a range of companies such as price comparison websites. It stemmed from  Dutch company PR Aviation operation of a website where consumers can search through flight data of low-cost airlines  (including Ryanair), compare prices and, on payment of a commission, book a flight. The relevant flight data is extracted from third-parties’ websites by means of ‘screen scraping’ practices.

Ryanair claimed that PR Aviation’s activity:

• amounted to infringement of copyright (relating to the structure and architecture of the database) and of the so-called sui generis database right (i.e. the right granted to the ‘maker’ of the database where certain investments have been made to obtain, verify, or present the contents of a database) under the Netherlands law implementing the Database Directive;

• constituted breach of contract. In this respect, Ryanair claimed that a contract existed with PR Aviation for the use of its website. Access to the latter requires acceptance, by clicking a box, of the airline’s general terms and conditions which, amongst others, prohibit unauthorized ‘screen scraping’ practices for commercial purposes.

Ryanair asked Dutch courts to prohibit the infringement and order damages. In recent years the company has been engaged in several legal cases against web scrapers across Europe.

The Local Court, Utrecht, and the Court of Appeals of Amsterdam dismissed Ryanair’s claims on different grounds. The Court of Appeals, in particular, cited PR Aviation’s screen scraping of Ryanair’s website as amounting to a “normal use” of said website within the meaning of the lawful user exceptions under Sections 6 and 8 of the Database Directive, which cannot be derogated by contract (Section 15).

Ryanair appealed

Ryanair appealed the decision before the Netherlands Supreme Court (Hoge Raad der Nederlanden), which decided to refer the following question to the ECJ for a preliminary ruling: “Does the application of [Directive 96/9] also extend to online databases which are not protected by copyright on the basis of Chapter II of said directive or by a sui generis right on the basis of Chapter III, in the sense that the freedom to use such databases through the (whether or not analogous) application of Article[s] 6(1) and 8, in conjunction with Article 15 [of Directive 96/9] may not be limited contractually?.”

The ECJ’s ruling

The ECJ (without the need of the opinion of the advocate general) ruled that the Database Directive is not applicable to databases which are not protected either by copyright or by the sui generis database right. Therefore, exceptions to restricted acts set forth by Sections 6 and 8 of the Directive do not prevent the database owner from establishing contractual limitations on its use by third parties. In other words, restrictions to the freedom to contract set forth by the Database Directive do not apply in cases of unprotected databases. Whether Ryanair’s website may be entitled to copyright or sui generis database right protection needs to be determined by the competent national court.

The ECJ’s decision is not particularly striking from a legal standpoint. Yet, it could have a significant impact on the business model of price comparison websites, aggregators, and similar businesses. Owners of databases that could not rely on intellectual property protection may contractually prevent extraction and use (“scraping”) of content from their online databases. Thus, unprotected databases could receive greater protection than the one granted by IP law.

Antitrust implications

However, the lawfulness of contractual restrictions prohibiting access and reuse of data through screen scraping practices should be assessed under an antitrust perspective. In this respect, in 2013 the Court of Milan ruled that Ryanair’s refusal to grant access to its database to the online travel agency Viaggiare S.r.l. amounted to an abuse of dominant position in the downstream market of information and intermediation on flights (decision of June 4, 2013 Viaggiare S.r.l. vs Ryanair Ltd). Indeed, a balance should be struck between the need to compensate the efforts and investments made by the creator of the database with the interest of third parties to be granted with access to information (especially in those cases where the latter are not entitled to copyright protection).

Additionally, web scraping triggers other issues which have not been considered by the ECJ’s ruling. These include, but are not limited to trademark law (i.e., whether the use of a company’s names/logos by the web scraper without consent may amount to trademark infringement), data protection (e.g., in case the scraping involves personal data), or unfair competition.

Source: http://www.globallegalpost.com/blogs/global-view/ecj-clarifies-database-directive-scope-in-screen-scraping-case-128701/

Data Scraping - What Are Hand-Scraped Hardwood Floors and What Are the Benefits?

If you love the look of hardwood flooring with lots of character, then you may want to check out hand-scraped hardwood flooring. Hand-scraped wood provides a warm vintage look, providing the floor instant character. These types of scraped hardwoods are suitable for living rooms, dining rooms, hallways and bedrooms. But what exactly is hand-scraped hardwood flooring?

Well, it is literally what you think it is. Hand-scraped hardwood flooring is created by hand using specialized wood working tools to make each board unique and giving an overall "old worn" appearance.

At Innovation Builders we offer solid wood floors finished on site with an actual hand-scraping technique followed by stain and sealer. Solid wood floors are installed by an expert team of technicians who work each board with skilled craftsman-like attention to detail. Following the scraping procedure the floor is stained by hand with a customer selected stain color, and then protected with multiple coats of sealing and finishing polyurethane. This finishing process of staining, sealing and coating the wood floors contributes to providing the look and durability of an old reclaimed wood floor, but with today's tough, urethane finishes.

There are many, many benefits to hand-scraped wood flooring. Overall, these floors are extremely durable and hard wearing, providing years of trouble-free use. These wood floors remain looking newer for longer because the texture that the process provides hides the typical dents, dings and scratches that other floors can't hide so easily. That's great news for households with kids, dogs, and cats.

These types of wood flooring have another unique advantage as well. When you do scratch these floors during their lifetime, the scratches are easily repaired. As long as the scratch isn't too deep you can make them practically disappear without ever having to hire a professional. It's simple to hide the scratch by using a color-matched stain marker or repair kit that is readily available through local flooring distributors. These features make hand-scraped hardwood flooring a lot more durable and hassle-free to maintain than other types of wood flooring.

The expert processes utilized in the creation of these floors provides a custom look of worn wood with deep color and subtle highlights. When the light hits the wood at different times during the day, it provides an understated but powerful effect of depth and beauty. They instantly offer your rooms a rustic look full of character, allowing your home to become a warm and inviting environment. The rustic look of this wood provides a texture, style and rustic appeal that cannot be matched by any other type of flooring.

Hand-Scraped Hardwood Flooring is a floor that says welcome and adds a touch of elegance to any home. If you are looking to buy a new home and you haven't had the opportunity to see or feel hand scraped hardwoods, stop in any of the model homes at Innovation Builders in Keller, North Richland Hills or Grand Prairie, Texas and check it out!

Source: http://ezinearticles.com/?What-Are-Hand-Scraped-Hardwood-Floors-and-What-Are-the-Benefits?&id=6026646

Web Scraping: working with APIs

APIs present researchers with a diverse set of data sources through a standardised access mechanism: send a pasted together HTTP request, receive JSON or XML in return. Today we tap into a range of APIs to get comfortable sending queries and processing responses.

These are the slides from the final class in Web Scraping through R: Web scraping for the humanities and social sciences

This week we explore how to use APIs in R, focusing on the Google Maps API. We then attempt to transfer this approach to query the Yandex Maps API. Finally, the practice section includes examples of working with the YouTube V2 API, a few ‘social’ APIs such as LinkedIn and Twitter, as well as APIs less off the beaten track (Cricket scores, anyone?).

I enjoyed teaching this course and hope to repeat and improve on it next year. When designing the course I tried to cram in everything I wish I had been taught early on in my PhD (resulting in information overload, I fear). Still, hopefully it has been useful to students getting started with digital data collection, showing on the one hand what is possible, and on the other giving some idea of key steps in achieving research objectives.

Download the .Rpres file to use in Rstudio here

A regular R script with code-snippets only can be accessed here

Slides from the first session here

Slides from the second session here

Slides from the third session here

Source: http://www.r-bloggers.com/web-scraping-working-with-apis/

Web Scraping Services : Making Modern File Formats More Accessible

Data scraping is the process of automatically sorting through information contained on the internet inside html, PDF or other documents and collecting relevant information to into databases and spreadsheets for later retrieval. On most websites, the text is easily and accessibly written in the source code but an increasing number of businesses are using Adobe PDF format (Portable Document Format: A format which can be viewed by the free Adobe Acrobat software on almost any operating system. See below for a link.). The advantage of PDF format is that the document looks exactly the same no matter which computer you view it from making it ideal for business forms, specification sheets, etc.; the disadvantage is that the text is converted into an image from which you often cannot easily copy and paste. PDF Scraping is the process of data scraping information contained in PDF files. To PDF scrape a PDF document, you must employ a more diverse set of tools.

There are two main types of PDF files: those built from a text file and those built from an image (likely scanned in). Adobe's own software is capable of PDF scraping from text-based PDF files but special tools are needed for PDF scraping text from image-based PDF files. The primary tool for PDF scraping is the OCR program. OCR, or Optical Character Recognition, programs scan a document for small pictures that they can separate into letters. These pictures are then compared to actual letters and if matches are found, the letters are copied into a file. OCR programs can perform PDF scraping of image-based PDF files quite accurately but they are not perfect.

Once the OCR program or Adobe program has finished PDF scraping a document, you can search through the data to find the parts you are most interested in. This information can then be stored into your favorite database or spreadsheet program. Some PDF scraping programs can sort the data into databases and/or spreadsheets automatically making your job that much easier.

Quite often you will not find a PDF scraping program that will obtain exactly the data you want without customization. Surprisingly a search on Google only turned up one business, that will create a customized PDF scraping utility for your project. A handful of off the shelf utilities claim to be customizable, but seem to require a bit of programming knowledge and time commitment to use effectively. Obtaining the data yourself with one of these tools may be possible but will likely prove quite tedious and time consuming. It may be advisable to contract a company that specializes in PDF scraping to do it for you quickly and professionally.

Let's explore some real world examples of the uses of PDF scraping technology. A group at Cornell University wanted to improve a database of technical documents in PDF format by taking the old PDF file where the links and references were just images of text and changing the links and references into working clickable links thus making the database easy to navigate and cross-reference. They employed a PDF scraping utility to deconstruct the PDF files and figure out where the links were. They then could create a simple script to re-create the PDF files with working links replacing the old text image.

A computer hardware vendor wanted to display specifications data for his hardware on his website. He hired a company to perform PDF scraping of the hardware documentation on the manufacturers' website and save the PDF scraped data into a database he could use to update his webpage automatically.

PDF Scraping is just collecting information that is available on the public internet. PDF Scraping does not violate copyright laws.

PDF Scraping is a great new technology that can significantly reduce your workload if it involves retrieving information from PDF files. Applications exist that can help you with smaller, easier PDF Scraping projects but companies exist that will create custom applications for larger or more intricate PDF Scraping jobs.

Source: http://ezinearticles.com/?PDF-Scraping:-Making-Modern-File-Formats-More-Accessible&id=193321

Scraping the Royal Society membership list

To a data scientist any data is fair game, from my interest in the history of science I came across the membership records of the Royal Society from 1660 to 2007 which are available as a single PDF file. I’ve scraped the membership list before: the first time around I wrote a C# application which parsed a plain text file which I had made from the original PDF using an online converting service, looking back at the code it is fiendishly complicated and cluttered by boilerplate code required to build a GUI. ScraperWiki includes a pdftoxml function so I thought I’d see if this would make the process of parsing easier, and compare the ScraperWiki experience more widely with my earlier scraper.

The membership list is laid out quite simply, as shown in the image below, each member (or Fellow) record spans two lines with the member name in the left most column on the first line and information on their birth date and the day they died, the class of their Fellowship and their election date on the second line.

Later in the document we find that information on the Presidents of the Royal Society is found on the same line as the Fellow name and that Royal Patrons are formatted a little differently. There are also alias records where the second line points to the primary record for the name on the first line.

pdftoxml converts a PDF into an xml file, wherein each piece of text is located on the page using spatial coordinates, an individual line looks like this:

<text top="243" left="135" width="221" height="14" font="2">Abbot, Charles, 1st Baron Colchester </text>

This makes parsing columnar data straightforward you simply need to select elements with particular values of the “left” attribute. It turns out that the columns are not in exactly the same positions throughout the whole document, which appears to have been constructed by tacking together the membership list A-J with that of K-Z, but this can easily be resolved by accepting a small range of positions for each column.

Attempting to automatically parse all 395 pages of the document reveals some transcription errors: one Fellow was apparently elected on 16th March 197 – a bit of Googling reveals that the real date is 16th March 1978. Another fellow is classed as a “Felllow”, and whilst most of the dates of birth and death are separated by a dash some are separated by an en dash which as far as the code is concerned is something completely different and so on. In my earlier iteration I missed some of these quirks or fixed them by editing the converted text file. These variations suggest that the source document was typed manually rather than being output from a pre-existing database. Since I couldn’t edit the source document I was obliged to code around these quirks.

ScraperWiki helpfully makes putting data into a SQLite database the simplest option for a scraper. My handling of dates in this version of the scraper is a little unsatisfactory: presidential terms are described in terms of a start and end year but are rendered 1st January of those years in the database. Furthermore, in historical documents dates may not be known accurately so someone may have a birth date described as “circa 1782? or “c 1782?, even more vaguely they may be described as having “flourished 1663-1778? or “fl. 1663-1778?. Python’s default datetime module does not capture this subtlety and if it did the database used to store dates would need to support it too to be useful – I’ve addressed this by storing the original life span data as text so that it can be analysed should the need arise. Storing dates as proper dates in the database, rather than text strings means we can query the database using date based queries.

ScraperWiki provides an API to my dataset so that I can query it using SQL, and since it is public anyone else can do this too. So, for example, it’s easy to write queries that tell you the the database contains 8019 Fellows, 56 Presidents, 387 born before 1700, 3657 with no birth date, 2360 with no death date, 204 “flourished”, 450 have birth dates “circa” some year.

I can count the number of classes of fellows:

select distinct class,count(*) from `RoyalSocietyFellows` group by class

Make a table of all of the Presidents of the Royal Society

select * from `RoyalSocietyFellows` where StartPresident not null order by StartPresident desc

…and so on. These illustrations just use the ScraperWiki htmltable export option to display the data as a table but equally I could use similar queries to pull data into a visualisation.

Comparing this to my earlier experience, the benefits of using ScraperWiki are:

•    Nice traceable code to provide a provenance for the dataset;

•    Access to the pdftoxml library;

•    Strong encouragement to “do the right thing” and put the data into a database;

•    Publication of the data;

•    A simple API giving access to the data for reuse by all.

My next target for ScraperWiki may well be the membership lists for the French Academie des Sciences, a task which proved too complex for a simple plain text scraper…

Source: https://scraperwiki.wordpress.com/2012/12/28/scraping-the-royal-society-membership-list/

Data Scraping Services - Scraping Yelp Business Data With Python Scraping Script

Yelp is a great source of business contact information with details like address, postal code, contact information; website addresses etc. that other site like Google Maps just does not. Yelp also provides reviews about the particular business. The yelp business database can be useful for telemarketing, email marketing and lead generation.

Are you looking for yelp business details database? Are you looking for scraping data from yelp website/business directory? Are you looking for yelp screen scraping software? Are you looking for scraping the business contact information from the online Yelp? Then you are at the right place.

Here I am going to discuss how to scrape yelp data for lead generation and email marketing. I have made a simple and straight forward yelp data scraping script in python that can scrape data from yelp website. You can use this yelp scraper script absolutely free.

I have used urllib, BeautifulSoup packages. Urllib package to make http request and parsed the HTML using BeautifulSoup, used Threads to make the scraping faster.

Yelp Scraping Python Script

import urllib from bs4 import BeautifulSoup import re from threading import Thread #List of yelp urls to scrape url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg'] i=0 #function that will do actual scraping job def scrape(ur): html = urllib.urlopen(ur).read() soup = BeautifulSoup(html) title = soup.find('h1',itemprop="name") saddress = soup.find('span',itemprop="streetAddress") postalcode = soup.find('span',itemprop="postalCode") print title.text print saddress.text print postalcode.text print "-------------------" threadlist = [] #making threads while i<len(url): t = Thread(target=scrape,args=(url[i],)) t.start() threadlist.append(t) i=i+1 for b in
threadlist: b.join()

import urllib

from bs4 import BeautifulSoup

import re

from threading import Thread

 #List of yelp urls to scrape

url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg']

 i=0

#function that will do actual scraping job

def scrape(ur):

           html = urllib.urlopen(ur).read()

          soup = BeautifulSoup(html)

       title = soup.find('h1',itemprop="name")

          saddress = soup.find('span',itemprop="streetAddress")

          postalcode = soup.find('span',itemprop="postalCode")

          print title.text

          print saddress.text

          print postalcode.text

          print "-------------------"

 threadlist = []

#making threads

while i<len(url):

          t = Thread(target=scrape,args=(url[i],))

          t.start()

          threadlist.append(t)

          i=i+1

for b in threadlist:

          b.join()

Recently I had worked for one German company and did yelp scraping project for them and delivered data as per their requirement. If you looking for scraping data from business directories like yelp then send me your requirement and I will get back to you with sample.

Source: http://webdata-scraping.com/scraping-yelp-business-data-python-scraping-script/

Data Extraction Services

Are you finding it tedious to perform your routine tasks as well as finding time to research for some information? Don't worry; all you have to do is outsource data extraction requirements to reliable service providers such as Hi-Tech BPO Services.

We can assist you in finding, extracting, gathering, processing and validating all the required data through our effective data extraction services. We can extract data from any given source such as websites, databases, printed documents, directories, etc.

With a whole plethora of data extraction services solutions; we are definitely a one stop solution to all your data extraction services requirements.

For utilizing our data extraction services, all you have to do is outsource data extraction requirements to us, and we will create effective strategies and extract the required data from all preferred sources. Then we will arrange all the extracted data in a systematic order.

Types of data extraction services provided by our data extraction India unit:

The data extraction India unit of Hi-Tech BPO Services can attend to all types of outsource data extraction requirements. Following are just some of the data extraction services we have delivered:

•    Data extraction from websites
•    Data extraction from databases
•    Extraction of data from directories
•    Extracting data from books
•    Data extraction from forms
•    Extracting data from printed materials

Features of Our Data Extraction Services:

•    Reliable collection of resources for data extraction
•    Extensive range of data extraction services
•    Data can be extracted from any available source be it a digital source or a hard copy source
•    Proper researching, extraction, gathering, processing and validation of data
•    Reasonably priced data extraction services
•    Quality and confidentiality ensured through various strict measures

Our data extraction India unit has the competency to handle any of your data extraction services requirements. Just provide us with your specific requirements and we will extract data accordingly from your preferred resources, if particularly specified. Otherwise we will completely rely on our collection of resources for extracting data for you.

Source: http://www.hitechbposervices.com/data-extraction.php

Improving performance for web scraping code

2 down vote favorite

I have a website in which the code scrapes other websites for getting the accurate data. While the code works good but there a decent lag in performance because the code firsts downloads the html stream from various sites(some times 9 websites), extracts the relative part and then renders the html page.

What should I do to get an optimal performance. Should I change from shared hosting (godaddy) to my own server or it has nothing to do with my hosting and I need to make changes to my code?

1 Answer

API/CSV

Ask those websites if they provide an API, or, if you don't need an up-to-date information or the information you need doesn't change frequently, if they can sell/give you for free the data itself (for example in an CSV file). Some small websites may have fancier ways to access data, like a CSV file for the older information, and an RSS feed for the changed one.

Those websites would probably be happy to help you, since providing you with an API would reduce their own CPU and bandwidth usage by you.

Profile

Screen scrapping is really ugly when it comes to performance and scaling. You may be limited by:

    your machine performance, since parsing, sometimes an invalid HTML file, takes time,

    your network speed,

    their network speed usage, i.e. how fast can you access the pages of their website depending on the restrictions they set, like the DOS protection and the number of requests per second for screen scrappers and search engine crawlers,

    their machine performance: if they spend 500 ms. to generate every page, you can't do anything to reduce this delay.

If, despite your requests to them, those websites cannot provide any convenient way to access their data, but they give you a written consent to screen scrape their website, then profile your code to determine the bottleneck. It may be the internet speed. It may be your database queries. It may be anything.

For example, you may discover that you spend too much time finding with regular expressions the relevant information in the received HTML. In that case, you would want to stop doing it wrong and use a parser instead of regular expressions, then see how this improve the performance.

You may also find that the bottleneck is the time the remote server spends generating every page. In this case, there is nothing to do: you may have the fastest server, the fastest connection and the most optimized code, the performance will be the same.

Do things in parallel:

Remember to use parallel computing wisely and to always profile what you're doing, instead of doing premature optimization, in hope that you're smarter than the profiler.

Especially when it comes to using network, you may be very surprised. For example, you may believe that making more requests in parallel will be faster, but as Steve Gibson explains in episode 345 of Security Now, this is not always the case.

Legal aspects

Also note that screen scrapping is explicitly forbidden by the conditions of use (like on IMDB) on many websites. And if nothing is said on this subject in conditions of use, it doesn't mean that you can screen scrape those websites.

The fact that the information is available publicly on the internet doesn't give you the right to copy and reuse it this way neither.

Why? you may ask. For two reasons:

    Most websites are relying on advertisement and marketing. When people use one of those websites directly, they waste some CPU/network bandwidth of the website, but in response, they may click on an ad or buy something sold on the website. When you screen scrape, your bot waste their CPU/network bandwidth, but will never click on an ad or buy something.

    Displaying the information you screen scrapped on your website can have even worse effects. Example: in France, there are two major websites selling hardware. The first one is easy and fast to use, has a nice visual design, better SEO, and in general is very well done. The second one is a crap, but the prices are lower. If you screen scrape them and give the raw results (prices with links) to your users, they will obviously click on the lower price every time, which means that the website with pretty design will have less chances to sell the products.

    People made an effort in collecting, processing and displaying some data. Sometimes they paid to get it. Why would they enjoy seeing you pulling this data conveniently and for free?

Source: http://programmers.stackexchange.com/questions/141403/improving-performance-for-web-scraping-code/141406#141406

Web scraping using Python without using large frameworks like Scrapy

scrapy-big-logoIf you need publicly available data from scraping the Internet, before creating a webscraper, it is best to check if this data is already available from public data sources or APIs. Check the site’s FAQ section or Google for their API endpoints and public data.

Even if their API endpoints are available you have to create some parser for fetching and structuring the data according to your needs.

Scrapy is a well established framework for scraping, but it is also a very heavy framework. For smaller jobs, it may be overkill and for extremely large jobs it is very slow.

So if you would like to roll up your sleeves and build your own scraper, continue reading.

Here are some basic steps performed by most webspiders:

1) Start with a URL and use a HTTP GET or PUT request to access the URL
2) Fetch all the contents in it and parse the data
3) Store the data in any database or put it into any data warehouse
4) Enqueue all the URLs in a page
5) Use the URLs in queue and repeat from process 1
Here are the 3 major modules in every web crawler:
1) Request/Response handler.
2) Data parsing/data cleansing/data munging process.
3) Data serialization/data pipelines.

Lets look at each of these modules and see what they do and how to use them.

Request/Response handler

Request/response handlers are managers who make http requests to a url or a group of urls, and fetch the response objects as html contents and pass this data to the next module. If you use Python for performing request/response url-opening process libraries such as the following are most commonly used

1) urllib(20.5. urllib – Open arbitrary resources by URL – Python v2.7.8 documentation) -Basic python library yet high-level interface for fetching data across the World Wide Web.

2) urllib2(20.6. urllib2 – extensible library for opening URLs – Python v2.7.8 documentation) – extensible library of urllib, which would handle basic http requests, digest authentication, redirections, cookies and more.

3) requests(Requests: HTTP for Humans) – Much advanced request library

which is built on top of basic request handling libraries.

Data parsing/data cleansing/data munging process

This is the module where the fetched data is processed and cleaned. Unstructured data is transformed into structured during this processing. Usually  a set of Regular Expressions (regexes) which perform pattern matching and text processing tasks on the html data are used for this processing.

In addition to regexes, basic string manipulation and search methods are also used to perform this cleaning and transformation. You must have a thorough knowledge of regular expressions and so that you could design the regex patterns.

Data serialization/data pipelines

Once you get the cleaned data from the parsing and cleaning module, the data serialization module will be used to serialize the data according to the data models that you require. This is the final module that will output data in a standard format that can be stored in databases, JSON/CSV files or passed to any data warehouses for storage. These tasks are usually performed by libraries listed below

1) pickle (pickle – Python object serialization) –  This module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure

2) JSON (JSON encoder and decoder)

3) CSV (https://docs.python.org/2/library/csv.html)

4) Basic database interface libraries like pymongo (Tutorial – PyMongo),mysqldb ( on python.org), sqlite3(sqlite3 – DB-API interface for SQLite databases)

And many more such libraries based on the format and database/data storage.

Basic spider rules

The rules to follow while building a spider are to be nice to the sites you are scraping and follow the rules in the site’s spider policies outlined in the site’s robots.txt.

Limit the  number of requests in a second and build enough delays in the spiders so that  you don’t adversely affect the site.

It just makes sense to be nice.

We will cover more techniques in future articles

Source: http://learn.scrapehero.com/webscraping-using-python-without-using-large-frameworks-like-scrapy/

How Web Data Extraction Services Impact Startups

Starting a business has its fair share of ebbs and flows – it can be extremely challenging to get a new business off the blocks, and extremely rewarding when everything goes according to plan and yields desired results. For startups, it is important to get the nuances of running a business right from day one. To succeed in an immensely competitive space, startups need to perform above and beyond expectation right from the start, and one of the factors that can be of great help during the growing years of a startup is web data extraction.

Web data extraction through crawling and scraping, a highly efficient information gathering process, can be used in many creative ways to bring about major change in the performance graph of a startup. With effective web data extraction services acquired by outsourcing to a reputed company, the business intelligence gathered and the numerous possibilities associated with it, web crawling and extraction services can indeed become the difference maker for a startup, propelling it to the heights of success.

What drives the success of web data extraction?

When it comes to figuring out the perfect, balanced web data collection methodology for startups, there are a lot of crucial factors that come into play. Some of these are associated with the technical aspects of data collection, the approach used, the time invested, and the tools involved. Others have more to do with the processing and analysis of collected information and its judicious use in formulating strategies to take things forward.

Web Crawling Services & Web Scraping Services

With the advent of highly professional web data extraction services providers, massive amounts of structured, relevant data can be gathered and stored in real time, and in time, productively used to further the business interests of a startup. As a new business owner, it is important to have a high-level knowledge of the modern and highly functional web scraping tools available for use. This will help to utilize the prowess of competent data extraction services. This in turn can assist both in the immediate and long-term revenue generation context.

Web Data Extraction for Startups

From the very beginning, the dynamics of startups is different from that of older, well-established businesses. The time taken by the new business entity in proving its capabilities and market position needs to be used completely and effectively. Every day of growth and learning needs to add up to make a substantial difference. In this period, every plan and strategy, every execution effort, and every move needs to be properly thought out.

In such a trying situation where there is little margin for error, it pays to have accurate, reliable, relevant and actionable business intelligence. This can put you in firm control of things by allowing you to make informed business decisions and formulate targeted, relevant and growth oriented business strategies. With powerful web crawling, the volume of data gathered is varied, accurate and relevant. This data can then be studied minutely, analyzed in detail and arranged into meaningful clusters. With this weapon in your arsenal, you can take your startup a long way with smart decisions and clever implementations.

Web data extraction is a task best handled by professionals who have had rich experience in the field. Often, in-house web scraping teams are difficult to assemble and not economically viable to maintain, especially for startups. For a better solution, you can outsource your web scraping needs to a reliable web data extraction service for data collection. This way, you can get all the relevant intelligence you need without overstraining your workforce or having to employ additional personnel to handle web scraping. The company you outsource your work to can easily scrape data from multiple sources as per your requirements, and furnish you with actionable business intelligence that can help you take a lead in a competitive market.

Different Ways for Startups to use Web Data Extraction

Web scraping can be employed for many different purposes to yield different kinds of relevant data that generate actionable insights. For a startup, the important decision is how to use this powerful technique to provide valuable information that can make a difference for the future prospects of the company. Here are some interesting possibilities when it comes to impactful web data extraction for startups –

Fishing for Social Rankings and Backlinks

One of the most important business processes for a startup is competition analysis. This is one area where web data extraction can come across as an invaluable enabler. In the past, many startups have effectively used web scraping to fish for backlinks and social rankings related to competing companies.

Backlinks are important to reach a greater mass of better-targeted audiences, which can go on to increase customer base with minimal efforts. Social ranking is also an immensely important factor, as social actions on the internet are building blocks of opinion and reputation generation in this day and age. Keeping this in mind, you can use web data extraction to scrape for social rankings and backlinks related to content generated by your competing companies. After careful analysis, it is possible to arrive at concrete conclusions regarding what your competitors are doing well, and what sells the best.

This information is gold for marketers and sales personnel, and can be used to discern exactly what needs to be done to increase social buzz, generate favorable opinion, and win over customers from your competitors. You can also use this technique to develop high authority backlinks that help with SEO, targeted reach and organic traffic for your business website. For competition analysis, web scraping is a formidable tool.

Sourcing Contact Information

Another important aspect of business that startups can never ignore is good networking. Whether it is with customers, prospective customers, industry peers, partners, or competitors, excellent networking and open, transparent communication is essential for the success of your startup. For effective communication and networking, you need a large, solid list of contact information pertaining to your exact requirements.

Scraping data from multiple web sources gives you the perfect method of achieving this. With automated, fast web scraping, you can in a short time collect a wealth of important contact information that can be leveraged in many different ways. Whether it is the formation of lasting business relationships or making potential customers aware of what you have on offer, this information has the power to propel your startup to new levels of recognition.

For Ecommerce

If you sell your products and services online and want to stay on top of the competition when it comes to variety, pricing analysis, and special deals and offers, web scraping is the way to go. For many e-commerce startups, the problem of high CTR and low conversion is a stumbling block to higher bottom lines. To remedy problems like these and to ensure better sales, it is always a good idea to have a clear insight about your competition.

Future of Retail Industry

With web data extraction, you can be always aware of what competing companies are doing in terms of pricing strategies, product diversity and special customer offers. By considering that information while evaluating and cementing your own strategies, you can always ensure that you provide better value and range of products and services than your competitors, and therefore stay ahead of the competition.

For Marketing, Brand Promotion and Advertisement

For startups, the first wave of promotion and marketing is the one that holds the key to your long-term business success. It is during this phase that the first and most important public perception of your company is formed, and the rudiments of public opinion start taking shape. For this reason, it is crucial to be on point with your marketing and promotion during the early, formative years of your business.

To achieve this, you need a clear, in-depth understanding of your target audience. You need to categorize your target audience on the basis of many factors like age, gender, demographics, income groups and tastes and preferences. Such detailed understanding can only be possible when you have a large wealth of social data pertaining to your target audience. There is no better way of achieving this than by web data extraction.

Love your brand

With the help of data extraction services, you can gather large chunks of relevant data regarding your target audience which can help you accurately evaluate the potential of each prospective customers as a possible addition to your business family. To ensure that you have a steady, early wave of customers to take your business off the blocks at a rapid pace, you need to devise marketing campaigns, promotional strategies and advertisements in accordance with the customer knowledge you drive through your web scraping efforts. This is a foolproof strategy to have marketing and promotional plans in place that achieve goals, bring in new business and provide your company with enough initial momentum to carry it through the later years of success.

To conclude, web data extraction can be a veritable tool in the hands of a startup. With the proper use and leveraging of this technique, your startup can gather the required business intelligence to shine in a competitive market and become a favorite with the customer base. Working with the right web data extraction company can be one of the most important business decisions you make as a startup owner.

Source: https://www.promptcloud.com/blog/web-data-extraction-services-for-startups/

Scraping Twitter Lists To Boost Social Outreach (+ Free Tool!)

I published a post a few weeks ago describing how to build your own twitter custom audience list, outlining a variety of techniques to build up your list.

This post outlines another method (hat tip to Ade Lewis for the idea) which requires you to scrape Twitter directly.

If you want to skip all the explanations and just want to download the Twitter List Scraper tool, here you go…

Download the Twitter Scraper Tool for Windows or Mac (completely free)

Disclaimer: Scraping Twitter is against their Terms of Service, so if you decide to do this you do it at your own risk.

Some Benchmarks

Building custom audiences on Twitter requires you to identify Twitter usernames that might be interested in your service or product.

In my previous posts, one of the methods I employed was to pull a competitor’s link profile and scrape social accounts from the linking domains.

Once you upload a custom list, Twitter goes through a process of ‘matching’ against profiles in their system, to make sure the user exists and hasn’t opted out of tailored ads.

As our data was scraped from a list of unqualified websites, the data matching wasn’t likely to be perfect.

Experiments

Since I published that post, I have been experimenting a fair bit with list building, and have built up around 10 custom audience lists. I‘ve uploaded a total of 48,857 Twitter usernames using this method, but only 29,260 were matched by Twitter (just less than 60% match rate).

From some other experiments where I have had better control over the input data, this match rate was between 70-80%.

Since we’ll be scraping Twitter directly, I expect our match rate to be much higher – 90%+

Finding Relevant Twitter Lists

So, we’re going to scrape Twitter, and the first step is to find Twitter lists that will contain users potentially interested in what we have to offer.

As an example, we’ll pretend we’re marketing a music website, and we’ve produced a survey we want to collect responses for.

An advanced Google query can give us lists of music bloggers: site:twitter.com inurl:lists inurl:members inurl:music “music blogger”

Source: http://urlprofiler.com/blog/scraping-twitter/

Web Scraping: Startups, Services & Market

I got recently interested in startups using web scraping in a way or another and since I find the topic very interesting I wanted to share with you some thoughts. [Note that I’m not an expert. To correct me / share your knowledge please use the comment section]

Web scraping is everything but a new technique. However with more and more data shared on internet (from user generated content like social networks & review websites to public/government data and the growing number of online services) the amount of data collected and the use cases possible are increasing at an incredible pace.

We’ve entered the age of “Big Data” and web scraping is one of the sources to feed big data engines with fresh new data, let it be for predictive analytics, competition monitoring or simply to steal data.

From what I could see the startups and services which are using “web scraping” at their core can be divided into three categories:

•    the shovel sellers (a.k.a we sell you the technology to do web scraping)

•    the shovel users (a.k.a we use web scraping to extract gold and sell it to our users)

•    the shovel police (a.k.a the security services which are here to protect website owners from these bots)

The shovel sellers

From a technology point of view efficient web scraping is quite complicated. It exists a number of open source projects (like Beautiful Soup) which enable anyone to get up and running a web scraper by himself. However it’s a whole different story when it has to be the core of your business and that you need not only to maintain your scrapers but also to scale them and to extract smartly the data you need.

This is the reason why more and more services are selling “web scraping” as a service. Their job is to take care about the technical aspects so you can get the data you need without any technical knowledge. Here some examples of such services:

    Grepsr
    Krakio
    import.io
    promptcloud
    80legs
    Proxymesh (funny service: it provides a proxy rotator for web scraping. A shovel seller for shovel seller in a way)
    scrapingHub
    mozanda

The shovel users

It’s the layer above. Web scraping is the technical layer. What is interesting is to make sense of the data you collect. The number of business applications for web scraping is only increasing and some startups are really using it in a truly innovative way to provide a lot of value to their customers.

Basically these startups take care of collecting data then extract the value out of it to sell it to their customers. Here some examples:

Sales intelligence. The scrapers screen marketplaces, competitors, data from public markets, online directories (and more) to find leads. Datanyze, for example, track websites which add or drop javascript tags from your competitors so you can contact them as qualified leads.

Marketing. Web scraping can be used to monitor how your competitors are performing. From reviews they get on marketplaces to press coverage and financial published data you can learn a lot. Concerning marketing there is even a growth hacking class on udemy that teaches you how to leverage scraping for marketing purposes.

Price Intelligence. A very common use case is price monitoring. Whether it’s in the travel, e-commerce or real-estate industry monitoring your competitors’ prices and adjusting yours accordingly is often key. These services not only monitor prices but with their predictive algorithms they can give you advice on where the puck will be. Ex: WisePricer, Pricing Assistant.

Economic intelligence, Finance intelligence etc. with more and more economical, financial and political data available online a new breed of services, which collect and make sense of it, are rising. Ex: connotate.

The shovel police

Web scraping lies in a gray area. Depending on the country or the terms of service of each website, automatically collecting data via robots can be illegal. Whatever the laws say it becomes crucial for some services to try to block these crawlers to protect themselves. The IT security industry has understood it and some startups are starting to tackle this problem. Here are 3 services which claim to provide solutions to stop bots from crawling your website:

•    Distil
•    ScrapeSentry
•    Fireblade

From a market point of view

A couple of points on the market to conclude:

•    It’s hard to assess how big the “web scraping economy” is since it is at the intersection of several big industries (billion dollars): IT security, sales, marketing & finance intelligence. This technique is of course a small component of these industries but is likely to grow in the years to come.

•    A whole underground economy also exists since a lot of web scraping is done through “botnets” (networks of infected computers)

•    It’s a safe bet to say that more and more SaaS (like Datanyze pr Pricing Assistant) will find innovative applications for web scraping. And more and more startups will tackle web scraping from the security point of view.

•    Since these startups are often entering big markets through a niche product / approach (web scraping is a not the solution to everything, there are more a feature) they are likely to be acquired by bigger players (in the security, marketing or sales tools industries). The technological barrier are there.

Source: http://clementvouillon.com/article/web-scraping-startups-services-market/

Web Scraping - Data Collection or Illegal Activity?

Web Scraping Defined

We've all heard the term "web scraping" but what is this thing and why should we really care about it?  Web scraping refers to an application that is programmed to simulate human web surfing by accessing websites on behalf of its "user" and collecting large amounts of data that would typically be difficult for the end user to access.  Web scrapers process the unstructured or semi-structured data pages of targeted websites and convert the data into a structured format.  Once the data is in a structured format, the user can extract or manipulate the data with ease.  Web scraping is very similar to web indexing (used by most search engines), but the end motivation is typically much different.  Whereas web indexing is used to help make search engines more efficient, web scraping is typically used for different reasons like change detection, market research, data monitoring, and in some cases, theft.

Why Web Scrape?

There are lots of reasons people (or companies) want to scrape websites, and there are tons of web scraping applications available today.  A quick Internet search will yield numerous web scraping tools written in just about any programming language you prefer.  In today's information-hungry environment, individuals and companies alike are willing to go to great lengths to gather information about all sorts of topics.  Imagine a company that would really like to gather some market research on one of their leading competitors...might they be tempted to invoke a web scraper that gathers all the information for them?  Or, what if someone wanted to find a vulnerable site that allowed otherwise not-so-free downloads?  Or, maybe a less than honest person might want to find a list of account numbers on a site that failed to properly secure them.  The list goes on and on.

I should mention that web scraping is not always a bad thing.  Some websites allow web scraping, but many do not.  It's important to know what a website allows and prohibits before you scrape it.

The Problem With Web Scraping

Web scraping rides a fine line between collecting information and stealing information.  Most websites have a copyright disclosure statement that legally protects their website information.  It's up to the reader/user/scraper to read these disclosure statements and follow along legally and ethically.  In fact, the F5.com website presents the following copyright disclosure:  "All content included on this site, such as text, graphics, logos, button icons, images, audio clips, and software, including the compilation thereof (meaning the collection, arrangement, and assembly), is the property of F5 Networks, Inc., or its content and software suppliers, except as may be stated otherwise, and is protected by U.S. and international copyright laws."  It goes on to say, "We reserve the right to make changes to our site and these disclaimers, terms, and conditions at any time."

So, scraper beware!  There have been many court cases where web scraping turned into felony offenses.  One case involved an online activist who scraped the MIT website and ultimately downloaded millions of academic articles.  This guy is now free on bond, but faces dozens of years in prison and $1 million if convicted.  Another case involves a real estate company who illegally scraped listings and photos from a competitor in an attempt to gain a lead in the market.  Then, there's the case of a regional software company that was convicted of illegally scraping a major database company's websites in order to gain a competitive edge.  The software company had to pay a $20 million fine and the guilty scraper is serving three years probation.  Finally, there's the case of a medical website that hosted sensitive patient information.  In this case, several patients had posted personal drug listings and other private information on closed forums located on the medical website.  The website was scraped by a media-rese
arch firm, and all this information was suddenly public.

While many illegal web scrapers have been caught by the authorities, many more have never been caught and still run loose on websites around the world.  As you can see, it's increasingly important to guard against this activity.  After all, the information on your website belongs to you, and you don't want anyone else taking it without your permission.

The Good News

As we've noted, web scraping is a real problem for many companies today.  The good news is that F5 has web scraping protection built into the Application Security Manager (ASM) of its BIG-IP product family.  As you can see in the screenshot below, the ASM provides web scraping protection against bots, session opening anomalies, session transaction anomalies, and IP address whitelisting.

The bot detection works with clients that accept cookies and process JavaScript.  It counts the client's page consumption speed and declares a client as a bot if a certain number of page changes happen within a given time interval.  The session opening anomaly spots web scrapers that do not accept cookies or process JavaScript.  It counts the number of sessions opened during a given time interval and declares the client as a scraper if the maximum threshold is exceeded.  The session transaction anomaly detects valid sessions that visit the site much more than other clients.  This defense is looking at a bigger picture and it blocks sessions that exceed a calculated baseline number that is derived from a current session table.  The IP address whitelist allows known friendly bots and crawlers (i.e. Google, Bing, Yahoo, Ask, etc), and this list can be populated as needed to fit the needs of your organization.

I won't go into all the details here because I'll have some future articles that dive into the details of how the ASM protects against these types of web scraping capabilities.  But, suffice it to say, ASM does a great job of protecting your website against the problem of web scraping.

I'm sure as you studied the screenshot above you also noticed lots of other protection capabilities the ASM provides...brute force attack prevention, customized attack signatures, Denial of Service protection, etc.  You might be wondering how it does all that stuff as well.  Give us a little feedback on the topics you would like to see, and we'll start posting some targeted tech tips for you!

Thanks for reading this introductory web scraping article...and, be sure to come back for the deeper look into how the ASM is configured to handle this problem. For more information, check out this video from Peter Silva where he discusses ASM botnet and web scraping defense.

Source: https://devcentral.f5.com/articles/web-scraping-data-collection-or-illegal-activity

Customized Web Data Extraction Solutions for Business

As you begin leading your business on the path to success, competitive analysis forms a major part of your homework. You have already mobilized your efforts in finding the appropriate website data scrapping tool that will help you to collect relevant data from competitive websites and shape them up into useable information. There is however a need to look for a customized approach in your search for Data Extraction tools in order to leverage its benefits in the best possible way.

Off-the-shelf Tools Impede Data Extraction

 In the current scenario, Internet Technologies are evolving in abundance. Every organization leverages this development and builds their websites using a different programming language and technology. Off-the-shelf Website Data extraction tools are unable to interpret this difference. They fail to understand the data elements that need to be captured and end up in gathering data without any change in the software source codes.

As a result of this incapability in their technology, off-the-shelf solutions often deliver unclean, incomplete and also inaccurate data. Developers need to contribute a humungous effort in cleaning up and structuring the data to make it useable. However, despite the time-consuming activity, data seldom metamorphoses into the desired information. Also the personnel dealing with the clean-up process needs to have sufficient technical expertise in order to participate in the activities. The endeavor however results in an impediment to the whole process of data extraction leaving you thirsting for the required information to augment business growth.

Understanding how Web Extraction tools work

Web Scrapping tools are designed to extract data from the web automatically. They are usually small pieces of code written using programming languages such as Python, Ruby or PHP depending upon the expertise of the community building it. There are however several single-click models available which tends to make life easier for non-technical personnel.

The biggest challenge faced by a successful web extractor tool is to know how to tackle the right page and the right elements on that page in order to extract the desired information. Consequently, a web extractor needs to be designed to understand the anatomy of a web page in order to accomplish its task successfully. It should be designed to interpret the meaning of HTML elements like , table rows () within those tables, and table data (<td>) cells within those rows in order to extract the exact data. It will also be interfacing with the

element which are blocks of text and know how to extract the desired information from it.

Customized Solutions for your business

 Customized Solutions are provided by most Data Scraping experts. These software's help to minimize the cumbersome effort of writing elaborate codes to successfully accomplish the feat of data extraction. They are designed to seamlessly search competitive websites,identify relevant data elements, and extract appropriate data that will be useful for your business. Owing to their focused approach, these tools provide clean and accurate data thereby eliminating the need to waste valuable time and effort in any clean-up effort.

Most customized data extraction tools are also capable of delivering the extracted data in customized formats like XML or CSV. It also stores data in local databases like Microsoft Access, MySQL, or Microsoft SQL.

Customized Data scraping solutions therefore help you take accurate and informed decisions in order to define effective business strategies.

Source: http://scraping-solutions.blogspot.in/2014_07_01_archive.html 

Web Scraping – An Illegal Activity or Simple Data Collection?

Gone are the days when skillful extraction of information pertaining to real estate such as foreclosures, homes for sale, or mortgage records was considered difficult. Now, it is not only easy to extract data from real estate websites but also scrape real estate data on a consistent basis to add more value to your portal, or ensure that updated data is available to your visitors at all times. From downloading actual scanned documents in the form of PDF files to scraping websites for deeds or mortgages, smartly designer data extraction tools can do it all.

However, the one question that still manages to come to the front in the minds of those who scrape real estate listings and others are whether the act is illegal in nature or a simple way of collecting data.

Take a look.

Web Scraping—What is it?

Generally speaking, web scraping refers to programs that are designed to simulate human internet surfing and access websites on behalf of their users. These tools are effective in collecting large quantities of data that are otherwise difficult for end users to access. They process semi-structured or unstructured data pages of targeted websites and transform available data into a more structured format that can be extracted or manipulated by the user easily.

Quite similar to web indexing that is used by search engines, the end motivation of web scraping is much different. While web indexing makes search engines far more efficient, the latter is used for reasons like market research, change detection, data monitoring, or in some events, theft. But then, it is not always a bad thing. You just need to know if a website allows web scraping before proceeding with the act.

Fine Line between Stealing and Collecting Information

Web scraping rides an extremely fine line between the acts of collecting relevant information and stealing the same. The websites that have copyright disclosure statements in place to protect their website information are offended by outsiders raiding their data without due permission. In other words, it amounts to trespassing on their portal, which is unacceptable—both ethically and legally. So, it is very important for you to read all disclosure statements carefully and follow along in the right way. As web scraping cases may turn into felony offenses, it is best to guard against any kind of scrupulous activity and take permission before scraping data.

The Good News

However, all is not grey in data extraction processes. Reputed agencies are helping their clients scrape valuable data for gaining more value through legal means and carefully used tools. If you are looking for such services, then do get in touch with a reliable web scraping company of your choice and take your business to the next levels of success.

Source: https://3idatascraping.wordpress.com/2015/03/11/web-scraping-an-illegal-activity-or-simple-data-collection/

Hard-Scraped Hardwood Flooring: Restoration of History

Throughout History hardwood flooring has undergone dramatic changes from the meticulous hard-scraped hardwood polished floors of majestic plantations of the Deep South, to modern day technology providing maintenance free wood flooring designed for comfort and appearance. The hand-scraped hardwood floors of the South, depicted charm with old rustic nature and character that was often associated with this time era. To date, hand-scraped hardwood flooring is being revitalized and used in up-scale homes and places of businesses to restore the old country charm that once faded into oblivion.

As the name implies, hand-scraped flooring involves the retexturing the top layer of flooring material by various methods in an attempts to mimic the rustic appearance of flooring in yesteryears. Depending on the degree of texture required, hand scraping hardwood material is often accomplished by highly skilled craftsmen with specialized tools and years of experience perfecting this procedure. When properly done, hand-scraped hardwood floors add texture, richness and uniqueness not offered in any similar hardwood flooring product.

Rooted with history, these types of floors are available in finished or unfinished surfaces. The majority of the individuals selecting hand-scraped hardwood flooring elect a prefinished floor to reduce costs per square foot in installation and finishing labor charges, allowing for budget guidelines to bend, not break. As expected, hand-scraped flooring is expensive and depending on the grade and finish selected, can range from $15-40$ per square foot and beyond for material only. Preparation of the material is labor intensive adding to the overall cost per square foot dramatically. Recommended professional installation can and often does increase the cost per square foot as well, placing this method of hardwood flooring well out of reach of the average hardwood floor purchaser.

With numerous selections of hand-scraped finishes available, each finish is designed to bring out a different appearance making it a one-of-a-kind work of art. These numerous finish selections include:

• Time worn aged, dark coloring stain application bringing out grain characteristics

• Wire brushed, providing a highlighted "grainy" effect with obvious rough texture

• Hand sculpted, smoother distressed uniform appearance

• French Bleed, staining of edges and side joints with a much darker stain to give a bleeding effect to the wood

• Hand Hewn or Rough Sawn, with visible and noticeable saw marks

Regardless of the selection made, scraped flooring cannot be compared to any other available flooring material based on durability, strength and visual appearance. Limited by only the imagination and creativity, several wood species can be used to create unusual floor patterns, highlighting main focal points of personal libraries and art collections.

The precise process utilized in the creation of scraped floors projects a custom look with deep color and subtle warm highlights. With radiant natural light reflecting off this type of floor, the effect of beauty and depth is radiated in a fashion that fills the room with solitude and serenity encompassing all that enter. Hand-scraped hardwood floors speak of the past, a time of decent, a time or war and ambiguity towards other races and the blood- shed so that all men could be treated as equals. More than exquisite flooring, hand-scraped hardwood flooring is the restoration of History.

Source: http://ezinearticles.com/?Hard-Scraped-Hardwood-Flooring:-Restoration-of-History&id=6333218

What is HTML Scraping and how it works

There are many reasons why there may be a requirement to pull data or information from other sites, and usually the process begins after checking whether the site has an official API. There are very few people who are aware about the presence of structured data that is supported by every website automatically. We are basically talking about pulling data right from the HTML, also referred to as HTML scraping. This is an awesome way of gleaning data and information from third party websites.

Any webpage content that can be viewed can be scraped without any trouble. If there is any way provided by the website to the browser of the visitor to download content and use the same in a highly structured manner, in that case, accessing of the content programmatically is possible. HTML scraping works in an amazing manner.

Before indulging in HTML scraping, one can inspect the browser for network traffic. Site owners have a couple of tricks up their sleeve to thwart this access, but majority of them can be worked around.

Before moving on to how HTML scraping works, we must understand the reasons behind the same. Why is scraping needed? Once you get a satisfactory answer to this question, you can start looking for RSS or API feeds or various other traditional structured data forms. It is significant to understand that when compared with APIs, websites are more significant.

The most important advantage of the same is the maintenance of their websites where a lot of visitors visit rather than safeguarding structured data feeds. With Tweeter, the same has been publicly seen when it clamps down on the developer ecosystem. Many times, API feeds change or move without any prior warning. Many times, it can also be a deliberate attempt, but mostly, such issues or problems erupt as there is no authority or an organization that maintains or takes care of the structured data. It is rarely noticed, if the same gets severely mangled or goes offline. In case the website has certain issues or the website no longer works, the problem is more in the form of a ball in your court requiring dealing with the same without losing any time. api-comic-image

Rate limiting is another factor that needs a lot of thinking and in case of public websites, it virtually doesn’t exist. Besides some occasional sign up pages or captchas, many business websites fail to create and built defenses against any unwarranted automated access. Many times, a single website can be scraped for four hours straight without anyone noticing. There are chances that you would not be viewed under DDOS attack unless concurrent requests are being made by you. You will be seen just as an avid visitor or an enthusiast in the logs, that too, in case anyone is looking.

Another factor in HTML scraping is that one can easily access any website anonymously. Behavior tracking can be done with a few ways by the administrator of the website and this turns out to be beneficial if you want to privately gather the data. Many times, registration is imperative with APIs in order to get key and with any request being sent, this key also needs to be sent. But, in case of simple and straightforward HTTP requests, the visitor can stay anonymous besides cookies and IP address, which can again be spoofed.

The availability of HTML scraping is universal and there is no need to wait for the opening of the site for an API or for contacting anyone in the organization. One simply needs to spend some time and browse websites at a leisurely pace until the data you want is available and then find out the basic patterns to access the same.

Now you need to don a hat of a professional scraper and simply dive in. Initially, it may take some time to work up figuring out the way the data have been structured and the way it can be accessed just as we read APIs. If there is no documentation unlike APIs, you need to be a little more smart about it and use clever tricks.

Some of the most used tricks are

Data Fetching

The first thing that is required is data fetching. Find endpoints to begin with, that is the URLs that can help in returning the data that is required. If you are pretty sure about the data and the way it should be structured so as to match your requirements, you will require a particular subset for the same and later you can indulge in site browsing using the navigation tools.

GET Parameter

The URLs must be paid attention to and see the way it changes as you indulge in clicking between the sections and the way they divide into various subsections. Before starting, the other option that can be used is to straight away go to the search functionality of the site. Certain terms can be typed and the URL needs to be focused again for watching the changes on the basis of what is being searched. A GET parameter will be probably seen like q which changes on the basis of the search term used by you. Other GET parameters that are not being used can be removed from the URL until only the ones that are needed are left for data loading. Before a query string, there must always be a “?” beginning.

Now the time has come when you would have started to come across the data that you would like to see and want to access, but sometimes, there may be certain pagination issues that require to be dealt with. Due to these issues, you may not be able to see the data in its entirety. Single requests are kept away by many APIs as well from database slamming. Many times, clicking the next page can add some offset parameter that helps in data visibility on the page. All these steps will help you succeed in HTML scraping.

Source: https://www.promptcloud.com/blog/what-is-html-scraping-and-how-it-works/

The Nasty Problem with Scraping Results from the Engines

One theme that I've been concerned with this week centers around data transparency in the search engine world. Search engines provide information that is critical to the business of optimizing and growing a business on the web, yet barriers to this data currently force many companies to use methods of data extraction that violate the search engines' terms of service.

Specifically, we're talking about two pieces of information that no large-scale, successful web operation should be without. These include rankings (the position of their site(s) vs. their competitors) for important keywords and link data (currently provided most accurately through Yahoo!, but also available through MSN and in lower quality formats from Google).

Why do marketers and businesses need this data so badly? First we'll look at rankings:

•    For large sites in particular, rankings across the board will go up or down based on their actions and the actions of their competition. Any serious company who fails to monitor tweaks to their site, public relations, press and optimization tactics in this way will lose out to competitors who do track this data and, thus, can make intelligent business decisions based on it.

•    Rankings provide a benchmark that helps companies estimate their global reach in the search results and make predictions about whether certain areas of extension or growth make logical sense. If a company must decide on how to expand their content or what new keywords to target or even if they can compete in new markets, the business intelligence that can be extracted from large swaths of ranking data is critical.

•    Rankings can be mapped directly to traffic, allowing companies to consider advertising, extending their reach or forming partnerships

And, on the link data side:

•    Temporal link information allows marketers to see what effects certain link building, public relations and press efforts have on a site's link profile. Although some of this data is available through referring links in analytics programs, many folks are much more interested in the links that search engines know about and count, which often includes many more than those that pass traffic (and also ignores/doesn't count some that do pass traffic).

•    Link data may provide references for reputation management or tracking of viral campaigns - again, items that analytics don't entirely encompass.

•    Competitive link data may be of critical importance to many marketers - this information can't be tracked any other way.

I admit it. SEOmoz is a search engine scraper - we do it for our free public tools, for our internal research and we've even considered doing it for clients (though I'm seriously concerned about charging for data that's obtained outside TOS). Many hundreds of large firms in the search space (including a few that are 10-20X our size) do it, too. Why? Because search engine APIs aren't accurate.

Let's look at each engine's abilities and data sources individually. Since we've got a few hundred thousand points of data (if not more) on each, we're in a good position to make calls about how these systems are working.

Google (all APIs listed here):

•    Search SOAP API - provides ranking results that are massively different from almost every datacenter. The information is often less than useless, it's actually harmful, since you'll get a false sense of what's happening with your positions.

•    AJAX Search API - This is really designed to be integrated with your website, and the results can be of good quality for that purpose, but it really doesn't serve the job of providing good stats reporting.

•    AdSense & AdWords APIs - In all honesty, we haven't played around with these, but the fact that neither will report the correct order of the ads, nor will they show more than 8 ads at a time tells me that if a marketer needed this type of data, the APIs wouldn't work.

Yahoo! (APIs listed here):

•    Search API - Provides ranking information that is a somewhat accurate map to Yahoo!'s actual rankings, but is occassionally so far off-base that they're not reliable. Our data points show a lot more congruity with Yahoo!'s than Google's, but not nearly enough when compared with scraped results to be valuable to marketers and businesses.

•    Site Explorer API - Shows excellent information as far as number of pages indexed on a site and the link data that Yahoo! knows about. We've been comparing this information with that from scraped Yahoo! search results (for queries like linkdomain: and site:) and those at the Site Explorer page and find that there's very little quality difference in the results returned, though the best estimate numbers can still be found through a last page search of results.

•    Search Marketing API - I haven't played with this one at all, so I'd love to hear comments from those who have.

MSN:

•    Doesn't mind scraping as long as you use the RSS results. We do, we love them and we commend MSN for giving them out - bravo! They've also got a web search SDK program, but we've yet to give it a whirl. The only problem is the MSN estimates, which are so far off as to be useless. The links themselves, though, are useful.

Ask.com

•    Though it's somewhat hidden, the XML.Teoma.com page allows for scraping of results and Ask doesn't seem to mind, though they haven't explicitly said anything. Again, bravo! - the results look solid, accurate and match up against the Ask.com queries. Now, if Ask would only provide links

I know a lot of you are probably asking:

•    "Rand, if scraping is working, why do you care about the search engines fixing the APIs?"

•    The straight answer is that scraping hurts the search engines, hurts their users and isn't the most practical way to get the data. Let me give you some examples:

•    Scraped queries have to look as much like real users as possible to avoid detection and banning - thus, they affect the query data that search engineers use to improve web search.

•    These queries also hit advertisers - falsifying the number of "real" impressions that advertisers see and lowering their CTRs unnaturally.

•    They take up search engine resources and though even the heaviest scraping barely impacts their server loads, it's still an annoyance.

•    With all these negative elements, and so many positive incentives to have the data, it's clear what's needed - a way for marketers/businesses to get the data they need without hurting the search engines. Here's how they can do it:

•    Provide the search ranking position of a site in the referral string - this works for ranking data, but not for link data and since Yahoo! (and Google) both send referrals through re-directs at times, it wouldn't be a hard piece to add.

•    Make the API's accurate, complete and unlimited

•    If the last option is too ambitious, the search engines could charge for API queries - anyone who needs the data would be more than happy to pay for it. This might help with quality control, too.

•    For link data - serve up accurate, wholistic data in programs like Google Sitemaps and Yahoo! Search Submit (or even, Google Analytics). Obviously, you'd only get information about your own site after verifying.

I've talked to lots of people at the search engine level about making changes this week (including Jeremy, Priyank, Matt, Adam, Aaron, Brett and more). I can only hope for the best...

Source: http://moz.com/blog/the-nasty-problem-with-scraping-results-from-the-engines

Some Traps to know and avoid in Web Scraping

In the present day and age, web scraping comes across as a handy tool in the right hands. In essence, web scraping means quickly crawling the web for specific information, using pre-written programs. Scraping efforts are designed to crawl and analyze the data of entire websites, and saving the parts that are needed. Many industries have successfully used web scraping to create massive banks of relevant, actionable data that they use on a daily basis to further their business interests and provide better service to customers. This is the age of the Big Data, and web scraping is one of the ways in which businesses can tap into this huge data repository and come up with relevant information that aids them in every way.

Web scraping, however, does come with its own share of problems and roadblocks. With every passing day, a growing number of websites are trying to actively minimize the instance of scraping and protect their own data to stay afloat in today’s situation of immense competition. There are several other complications which might arise and several traps that can slow you down during your web scraping pursuits. Knowing about these traps and how to avoid them can be of great help if you want to successfully accomplish your web scraping goals and get the amount of data that you require.

Complications in Web Scraping

Over time, various complications have risen in the field of web scraping. Many websites have started to get paranoid about data duplication and data security problems and have begun to protect their data in many ways. Some websites are not generally agreeable to the moral and ethical implications of web scraping, and do not want their content to be scraped. There are many places where website owners can set traps and roadblocks to slow down or stop web scraping activities. Major search engines also have a system in place to discourage scraping of search engine results. Last but not the least, many websites and web services announce a blanket ban on web scraping and say the same in their terms and conditions, potentially leading to legal issues in the event of any scraping.

Here are some of the most common complications that you might face during your web scraping efforts which you should be particularly aware about –

•    Some locations on the intranet might discourage web scraping to prevent data duplication or data theft.

•    Many websites have in place a number of different traps to detect and ban web scraping tools and programs.

•    Certain websites make it clear in their terms and conditions that they consider web scraping an infringement of their privacy and might even consider legal redress.

•    In a number of locations, simple measures are implemented to prevent non-human traffic to websites, making it difficult for web scraping tools to go on collecting data at a fast pace.

To surmount these difficulties, you need a deeper and more insightful understanding of the way web scraping works and also the attitude of website owners towards web scraping efforts. Most major issues can be subverted or quietly avoided if you maintain good working practice during your web scraping efforts and understand the mentality of the people whose sites you are scraping.

Web Crawling Services & Web Scraping Services

Common Problems

With automated scraping, you might face a number of common problems. The behavior of web scraping programs or spiders presents a certain picture to the target website. It then uses this behavior to distinguish between human users and web scraping spiders. Depending on that information, a website may or may not employ particular web scraping traps to stop your efforts. Some of the commonly employed traps are –

Crawling Pattern Checks – Some websites detect scraping activities by analyzing crawling patterns. Web scraping robots follow a distinct crawling pattern which incorporates repetitive tasks like visiting links and copying content. By carefully analyzing these patterns, websites can determine that they are being caused by a web scraping robot and not a human user, and can take preventive measures.

Honeypots – Some websites have honeypots in their webpages to detect and block web scraping activities. These can be in the form of links that are not visible to human users, being disguised in a certain way. Since your web crawler program does not operate the way a human user does, it can try and scrape information from that link. As a result, the website can detect the scraping effort and block the source IP addresses.

Policies – Some websites make it absolutely apparent in their terms and conditions that they are particularly averse to web scraping activities on their content. This can act as a deterrent and make you vulnerable against possible ethical and legal implications.

Infinite Loops – Your web scraping program can be tricked into visiting the same URL again and again by using certain URL building techniques.

These traps in web scraping can prove to be detrimental to your efforts and you need to find innovative and effective ways to surpass these problems. Learning some web crawler tips to avoid traps and judiciously using them is a great way of making sure that your web scraping requirements are met without any hassle.

What you can do

The first and foremost rule of thumb about web scraping is that you have to make your efforts as inconspicuous as possible. This way you will not arouse suspicion and negative behavior from your target websites. To this end, you need a well-designed web scraping program with a human touch. Such a program can operate in flexible ways so as to not alert website owners through the usual traffic criteria used to spot scraping tools.

Web scraping for ecommerce data extraction

Some of the measures that you can implement to ensure that you steer clear of common web scraping traps are –

•    The first thing that you need to do is to ascertain if a particular website that you are trying to scrape has any particular dislike towards web scraping tools. If you see any indication in their terms and conditions, tread cautiously and stop scraping their website if you receive any notification regarding their lack of approval. Being polite and honest can help you get away with a lot.

•    Try and minimize the load on every single website that you visit for scraping. Putting a high load on websites can alert them towards your intentions and often might cause them to develop a negative attitude. To decrease the overall load on a particular website, there are many techniques that you can employ.

•    Start by caching the pages that you have already crawled to ensure that you do not have to load them again.

•    Also store the URLs of crawled pages.

•    Take things slow and do not flood the website with multiple parallel requests that put a strain on their resources.

•    Handle your scraping in gentle phases and take only the content you require.

•    Your scraping spider should be able to diversify its actions, change its crawling pattern and present a polymorphic front to websites, so as not to cause an alarm and put them on the defensive.

•    Arrive at an optimum crawling speed, so as to not tax the resources and bandwidth of the target website. Use auto throttling mechanisms to optimize web traffic and put random breaks in between page requests, with the lowest possible number of concurrent requests that you can work with.

•    Use multiple IP addresses for your scraping efforts, or take advantage of proxy servers and VPN services. This will help to minimize the danger of getting trapped and blacklisted by a website.

•    Be prepared to understand the respect the express wishes and policies of a website regarding web scraping by taking a good look at the target ‘robots.txt’ file. This file contains clear instructions on the exact pages that you are allowed to crawl, and the requisite intervals between page requests. It might also specify that you use a pre-determined user agent identification string that classifies you as a scraping bot. adhering to these instructions minimizes the chance of getting on the bad side of website owners and risking bans.

Use an advanced tool for web scraping which can store and check data, URLs and patterns. Whether your web scraping needs are confined to one domain or spread over many, you need to appreciate that many website owners do not take kindly to scraping. The trick here is to ensure that you maintain industry best practices while extracting data from websites. This prevents any incident of misunderstanding, and allows you a clear pathway to most of the data sources that you want to leverage for your requirements.

Hope this article helps in understanding the different traps and roadblocks that you might face during your web scraping endeavors. This will help you in figuring out smart, sensible ways to work around them and make sure that your experience remains smooth. This way, you can keep receiving the important information that you need with web scraping. Following these basic guidelines can help you prevent getting banned or blacklisted and stay in the good books of website owners. This will allow you continue with your web scraping activities unencumbered.

Source:https://www.promptcloud.com/blog/some-traps-to-avoid-in-web-scraping/

How does Web Scraping Identify the Data you Want

The Web is one of the biggest sources of data that should be leveraged for your business. Be it an email, an URL or even a hyperlink text you are looking at, it comprises data that could be translated into useful information for your business. The challenge however lies in identifying the data that is relevant for your needs and enabling access to the required data. Web Scraping tools, however, are geared to help you address this need and leverage the benefit of this huge information repository.

Web Scraping and how it Works?

 Web Scraping is the practice followed to extract data from relevant sources on the Web and transforming them into crucial information packages for use in your business. This is an automated process which is executed with the help of a host of intuitive Web Extraction tools, thus facilitating ease, accuracy and convenience in extracting vital data.

Scrapers also work by writing intelligent pieces of code that scour the web and extract data that you need for the benefit of your business. The languages used for coding these scrapers are Python, Ruby and PHP. The language you use will be determined by the community you have access to.

As mentioned earlier, the biggest challenge that web scraping is subjected to include the identification of the right URL, page and element in order to scrape out the required information. No matter how good you may be at coding scripts, no amount of that will help you achieve your objective if you fail to develop an understanding of the way the web is structured. It is this which will enable you to structure your code in a manner that will be the most effective in scraping the desired information.

Understanding a Web Site

 A Web Site appears on your browser owing to two technologies. These include:

•     HTTP – The language used to communicate with the server for requesting the retrieval of resources, namely, images, videos, and documents and so on.
•     HTML – The language that helps to display the retrieved information on the browser.

The display format of your website is therefore defined using the HTML. It is within the folds of its syntax, that you will find the data which you need to extract. It is, therefore, important that you understand the anatomy of a web site by studying the structure of an HTML Page.

The HTML Page Structure

 An HTML page comprises a stack of elements known as tags, each bearing a specific significance. The first among these being the header tags that comprises mostly all the elements within it. The table element, the most important so far as data containers are concerned, is a crucial element that you need to study. It comprises several table rows (TR) and table data (TD) elements that hold the vital data nuggets that you might need to train your scrapers to extract.

In addition to these, HTML pages comprise a series of other tags that act as vital data holders, namely, image tags (img src), hyperlinks (a href) and the div tags which essentially refer to a block of text.

The scraper code needs to be built around your understanding of the HTML elements. Knowing the elements will help you to understand the specific location where relevant data are stacked. This helps you to correctly define the code so as to enable the scraper to search and extract the right element in order to provide you with the most appropriate information.

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