How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

Data scrape is the process of extracting data from web by using software program from proven website only. Extracted data any one can use for any purposes as per the desires in various industries as the web having every important data of the world. We provide best of the web data extracting software. We have the expertise and one of kind knowledge in web data extraction, image scrapping, screen scrapping, email extract services, data mining, web grabbing.

Who can use Data Scraping Services?

Data scraping and extraction services can be used by any organization, company, or any firm who would like to have a data from particular industry, data of targeted customer, particular company, or anything which is available on net like data of email id, website name, search term or anything which is available on web. Most of time a marketing company like to use data scraping and data extraction services to do marketing for a particular product in certain industry and to reach the targeted customer for example if X company like to contact a restaurant of California city, so our software can extract the data of restaurant of California city and a marketing company can use this data to market their restaurant kind of product. MLM and Network marketing company also use data extraction and data scrapping services to to find a new customer by extracting data of certain prospective customer and can contact customer by telephone, sending a postcard, email marketing, and this way they build their huge network and build large group for their own product and company.

We helped many companies to find particular data as per their need for example.

Web Data Extraction

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API to extract data from a web site. We help you to create a kind of API which helps you to scrape data as per your need. We provide quality and affordable web Data Extraction application

Data Collection

Normally, data transfer between programs is accomplished using info structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all. That's why the key element that distinguishes data scraping from regular parsing is that the output being scraped was intended for display to an end-user.

Email Extractor

A tool which helps you to extract the email ids from any reliable sources automatically that is called a email extractor. It basically services the function of collecting business contacts from various web pages, HTML files, text files or any other format without duplicates email ids.

Screen scrapping

Screen scraping referred to the practice of reading text information from a computer display terminal's screen and collecting visual data from a source, instead of parsing data as in web scraping.

Data Mining Services

Data Mining Services is the process of extracting patterns from information. Datamining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.

Web spider

A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular search engines, use spidering as a means of providing up-to-date data.

Web Grabber

Web grabber is just a other name of the data scraping or data extraction.

Web Bot

Web Bot is software program that is claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scrapping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.

Source: http://ezinearticles.com/?How-Web-Data-Extraction-Services-Will-Save-Your-Time-and-Money-by-Automatic-Data-Collection&id=5159023

Getting Data from the Web

Getting Data from the Web

You’ve tried everything else, and you haven’t managed to get your hands on the data you want. You’ve found the data on the web, but, alas — no download options are available and copy-paste has failed you. Fear not, there may still be a way to get the data out. For example you can:

Get data from web-based APIs, such as interfaces provided by online databases and many modern web applications (including Twitter, Facebook and many others). This is a fantastic way to access government or commercial data, as well as data from social media sites.

Extract data from PDFs. This is very difficult, as PDF is a language for printers and does not retain much information on the structure of the data that is displayed within a document. Extracting information from PDFs is beyond the scope of this book, but there are some tools and tutorials that may help you do it.

Screen scrape web sites. During screen scraping, you’re extracting structured content from a normal web page with the help of a scraping utility or by writing a small piece of code. While this method is very powerful and can be used in many places, it requires a bit of understanding about how the web works.

With all those great technical options, don’t forget the simple options: often it is worth to spend some time searching for a file with machine-readable data or to call the institution which is holding the data you want.

In this chapter we walk through a very basic example of scraping data from an HTML web page.
What is machine-readable data?

The goal for most of these methods is to get access to machine-readable data. Machine readable data is created for processing by a computer, instead of the presentation to a human user. The structure of such data relates to contained information, and not the way it is displayed eventually. Examples of easily machine-readable formats include CSV, XML, JSON and Excel files, while formats like Word documents, HTML pages and PDF files are more concerned with the visual layout of the information. PDF for example is a language which talks directly to your printer, it’s concerned with position of lines and dots on a page, rather than distinguishable characters.
Scraping web sites: what for?

Everyone has done this: you go to a web site, see an interesting table and try to copy it over to Excel so you can add some numbers up or store it for later. Yet this often does not really work, or the information you want is spread across a large number of web sites. Copying by hand can quickly become very tedious, so it makes sense to use a bit of code to do it.

The advantage of scraping is that you can do it with virtually any web site — from weather forecasts to government spending, even if that site does not have an API for raw data access.
What you can and cannot scrape

There are, of course, limits to what can be scraped. Some factors that make it harder to scrape a site include:

Badly formatted HTML code with little or no structural information e.g. older government websites.

Authentication systems that are supposed to prevent automatic access e.g. CAPTCHA codes and paywalls.

Session-based systems that use browser cookies to keep track of what the user has been doing.

A lack of complete item listings and possibilities for wildcard search.

Blocking of bulk access by the server administrators.

Another set of limitations are legal barriers: some countries recognize database rights, which may limit your right to re-use information that has been published online. Sometimes, you can choose to ignore the license and do it anyway — depending on your jurisdiction, you may have special rights as a journalist. Scraping freely available Government data should be fine, but you may wish to double check before you publish. Commercial organizations — and certain NGOs — react with less tolerance and may try to claim that you’re “sabotaging” their systems. Other information may infringe the privacy of individuals and thereby violate data privacy laws or professional ethics.
Tools that help you scrape

There are many programs that can be used to extract bulk information from a web site, including browser extensions and some web services. Depending on your browser, tools like Readability (which helps extract text from a page) or DownThemAll (which allows you to download many files at once) will help you automate some tedious tasks, while Chrome’s Scraper extension was explicitly built to extract tables from web sites. Developer extensions like FireBug (for Firefox, the same thing is already included in Chrome, Safari and IE) let you track exactly how a web site is structured and what communications happen between your browser and the server.

ScraperWiki is a web site that allows you to code scrapers in a number of different programming languages, including Python, Ruby and PHP. If you want to get started with scraping without the hassle of setting up a programming environment on your computer, this is the way to go. Other web services, such as Google Spreadsheets and Yahoo! Pipes also allow you to perform some extraction from other web sites.
How does a web scraper work?

Web scrapers are usually small pieces of code written in a programming language such as Python, Ruby or PHP. Choosing the right language is largely a question of which community you have access to: if there is someone in your newsroom or city already working with one of these languages, then it makes sense to adopt the same language.

While some of the click-and-point scraping tools mentioned before may be helpful to get started, the real complexity involved in scraping a web site is in addressing the right pages and the right elements within these pages to extract the desired information. These tasks aren’t about programming, but understanding the structure of the web site and database.

When displaying a web site, your browser will almost always make use of two technologies: HTTP is a way for it to communicate with the server and to request specific resource, such as documents, images or videos. HTML is the language in which web sites are composed.
The anatomy of a web page

Any HTML page is structured as a hierarchy of boxes (which are defined by HTML “tags”). A large box will contain many smaller ones — for example a table that has many smaller divisions: rows and cells. There are many types of tags that perform different functions — some produce boxes, others tables, images or links. Tags can also have additional properties (e.g. they can be unique identifiers) and can belong to groups called ‘classes’, which makes it possible to target and capture individual elements within a document. Selecting the appropriate elements this way and extracting their content is the key to writing a scraper.

Viewing the elements in a web page: everything can be broken up into boxes within boxes.

To scrape web pages, you’ll need to learn a bit about the different types of elements that can be in an HTML document. For example, the <table> element wraps a whole table, which has <tr> (table row) elements for its rows, which in turn contain <td> (table data) for each cell. The most common element type you will encounter is <div>, which can basically mean any block of content. The easiest way to get a feel for these elements is by using the developer toolbar in your browser: they will allow you to hover over any part of a web page and see what the underlying code is.

Tags work like book ends, marking the start and the end of a unit. For example <em> signifies the start of an italicized or emphasized piece of text and </em> signifies the end of that section. Easy.

An example: scraping nuclear incidents with Python

NEWS is the International Atomic Energy Agency’s (IAEA) portal on world-wide radiation incidents (and a strong contender for membership in the Weird Title Club!). The web page lists incidents in a simple, blog-like site that can be easily scraped.

To start, create a new Python scraper on ScraperWiki and you will be presented with a text area that is mostly empty, except for some scaffolding code. In another browser window, open the IAEA site and open the developer toolbar in your browser. In the “Elements” view, try to find the HTML element for one of the news item titles. Your browser’s developer toolbar helps you connect elements on the web page with the underlying HTML code.

Investigating this page will reveal that the titles are <h4> elements within a <table>. Each event is a <tr> row, which also contains a description and a date. If we want to extract the titles of all events, we should find a way to select each row in the table sequentially, while fetching all the text within the title elements.

In order to turn this process into code, we need to make ourselves aware of all the steps involved. To get a feeling for the kind of steps required, let’s play a simple game: In your ScraperWiki window, try to write up individual instructions for yourself, for each thing you are going to do while writing this scraper, like steps in a recipe (prefix each line with a hash sign to tell Python that this not real computer code). For example:

  # Look for all rows in the table
  # Unicorn must not overflow on left side.

Try to be as precise as you can and don’t assume that the program knows anything about the page you’re attempting to scrape.

Once you’ve written down some pseudo-code, let’s compare this to the essential code for our first scraper:

  import scraperwiki
  from lxml import html

In this first section, we’re importing existing functionality from libraries — snippets of pre-written code. scraperwiki will give us the ability to download web sites, while lxml is a tool for the structured analysis of HTML documents. Good news: if you are writing a Python scraper with ScraperWiki, these two lines will always be the same.

  url = "http://www-news.iaea.org/EventList.aspx"
  doc_text = scraperwiki.scrape(url)
  doc = html.fromstring(doc_text)

Next, the code makes a name (variable): url, and assigns the URL of the IAEA page as its value. This tells the scraper that this thing exists and we want to pay attention to it. Note that the URL itself is in quotes as it is not part of the program code but a string, a sequence of characters.

We then use the url variable as input to a function, scraperwiki.scrape. A function will provide some defined job — in this case it’ll download a web page. When it’s finished, it’ll assign its output to another variable, doc_text. doc_text will now hold the actual text of the website — not the visual form you see in your browser, but the source code, including all the tags. Since this form is not very easy to parse, we’ll use another function, html.fromstring, to generate a special representation where we can easily address elements, the so-called document object model (DOM).

  for row in doc.cssselect("#tblEvents tr"):
  link_in_header = row.cssselect("h4 a").pop()
  event_title = link_in_header.text
  print event_title

In this final step, we use the DOM to find each row in our table and extract the event’s title from its header. Two new concepts are used: the for loop and element selection (.cssselect). The for loop essentially does what its name implies; it will traverse a list of items, assigning each a temporary alias (row in this case) and then run any indented instructions for each item.

The other new concept, element selection, is making use of a special language to find elements in the document. CSS selectors are normally used to add layout information to HTML elements and can be used to precisely pick an element out of a page. In this case (Line. 6) we’re selecting #tblEvents tr which will match each <tr> within the table element with the ID tblEvents (the hash simply signifies ID). Note that this will return a list of <tr> elements.

As can be seen on the next line (Line. 7), where we’re applying another selector to find any <a> (which is a hyperlink) within a <h4> (a title). Here we only want to look at a single element (there’s just one title per row), so we have to pop it off the top of the list returned by our selector with the .pop() function.

Note that some elements in the DOM contain actual text, i.e. text that is not part of any markup language, which we can access using the [element].text syntax seen on line 8. Finally, in line 9, we’re printing that text to the ScraperWiki console. If you hit run in your scraper, the smaller window should now start listing the event’s names from the IAEA web site.

  figs/incoming/04-DD.png
  Figure 58. A scraper in action (ScraperWiki)

You can now see a basic scraper operating: it downloads the web page, transforms it into the DOM form and then allows you to pick and extract certain content. Given this skeleton, you can try and solve some of the remaining problems using the ScraperWiki and Python documentation:

Can you find the address for the link in each event’s title?

Can you select the small box that contains the date and place by using its CSS class name and extract the element’s text?

ScraperWiki offers a small database to each scraper so you can store the results; copy the relevant example from their docs and adapt it so it will save the event titles, links and dates.

The event list has many pages; can you scrape multiple pages to get historic events as well?

As you’re trying to solve these challenges, have a look around ScraperWiki: there are many useful examples in the existing scrapers — and quite often, the data is pretty exciting, too. This way, you don’t need to start off your scraper from scratch: just choose one that is similar, fork it and adapt to your problem.

Source: http://datajournalismhandbook.org/1.0/en/getting_data_3.html

Invest in Data Extraction to Grow Your Business

Invest in Data Extraction to Grow Your Business

Automating your employees’ processes can help you increase productivity while keeping the cost of used resources at a minimum. This can help you focus your time and money in much needed areas of your company so that you can thrive in your industry. Data extraction can help you achieve automation by targeting online data sources (websites, blogs, forums, etc) for information and data that can be useful to your business. By using software rather than your employees, you can oftentimes get more accurate data and more thorough information that people may miss. The software can handle the volume that you need and will deliver the results that you desire to help your company.
See the Power of Data Extraction Online

To see all of the ways that data extraction tools and software can benefit your business, There you can read about the features of the software, practical uses for businesses and also schedule a demo before you buy.

Source: http://www.connotate.com/invest-in-data-extraction-to-grow-your-business/

Scraping data from LinkedIn

Scraping data from LinkedIn

How to scrape data from LinkedIn public profile for marketing purposes?

You can scrape data from a LinkedIn public profile using data scraper software. LinkedIn data extraction is most beneficial for marketers and most medium size companies rely on LinkedIn for their marketing purpose.

I would recommend you to use "LinkedIn Lead Extractor" software, which helps to quickly scrape public profiles from LinkedIn. With this tool your can scrape profile link, First Name, Last Name, Email, Phone Address, Twitter id, Yahoo messenger id, Skype Id, Google Talk ID, Job Role, Company Name, Address, Country, Connections. This company has built this tool specially for LinkedIn marketers who are not satisfied with their drop ship supplier's digital data.

LinkedIn advance search provides you the targeted customers profiles list with your requirements like country, country, city, company, job title, and much more.

In few weeks you can developed new ways to set-up differently the sales teams and create a much more technologic environment in the strategy department. An internal platform that generated targeted leads can be of a very big help. You can easily execute go to market to any area or city in so much little time compared with some years ago.

Source: http://www.ahmadsoftware.com/blogs/4/scraping-data-from-linkedin.html

Extract Data from Multiple Web Pages into Excel using import.io

In this tutorial, i will show you how to extract data from multiple web pages of a website or blog and save the extracted data into Excel spreadsheet for further processing.There are various methods and tools to do that but I found them complicated and I prefer to use import.io to accomplish the task.Import.io doesn’t require you to have programming skills.The platform is quite powerful,user-friendly with a lot of support online and above all FREE to use.

You can use the online version of their data extraction software or a desktop application.The online version will be covered in this tutorial.

Let us get started.

Step 1:Find a web page you want to extract data from.
You can extract data such as prices, images, authors’ names, addresses,dates etc

Step 2:Enter the URL for that web page into the text box here and click “Extract data”.

Then click  “Extract data” Import.io will transform the web page into data in seconds.Data such as authors,images,posts published dates and posts title will be pulled from the web page as shown in the image below.

Import.io extracted only 40 posts or articles from the first page of the blog!.
If you visit bongo5.com you will notice that the web page is having a total of 600+ pages at the time of writing this article and each page has 40 posts or articles on it as can be shown by the image below.
Next step will show you how to extract data from multiple pages of the web page into excel.

Step 3:Extract Data from Multiple Web Pages into Excel

Using the import.io online tool you can extract data from 20 web pages maximum.Go to the bottom right corner of the import.io online tool page and click “Download CSV” to save the extracted data from those 20 pages into Excel.
Note:Using the import.io desktop application you can extract an unlimited number of pages and pin point only the data you want to extract.Check out this tutorial on how to use the desktop application.
Once you click “Download CSV” the following pop up window will appear.You can specify the number of pages you want to get data from up to a maximum of 20 pages then click “Go!”
You will need to Sign up for a free account to download that data as a CSV, or save it as an API.If you save it as an API you can go back to the API later to extract new data if the web page is updated without the need to repeat the steps we have done so far.Also, you can use the API for integration into other platforms.
Below image shows 20 rows out of 800 rows of data extracted from the 20 pages of the web page.

Conclusion

The online tool doesn’t offer much flexibility than the desktop application.For example, you can not extract more than 20 pages and you can not pin point the type of data you want to extract.For a more advanced tutorial on how to use the desktop application, you can check out this tutorial I created earlier.

Source URL : http://nocodewebscraping.com/extract-multiple-web-pages-data-into-excel/

4 Web Scraping Tools To Save You Time On Data Extraction

Either you are working on a product website, struggling to add live data feed to your app or merely need to pull out a huge amount of online data for analysis, an accurate web scraping tool can save you loads of time and keep you sane. Here are four powerful web scraping tools to save you from copy-pasting or spending time on writing your own scripts.

Uipath  specializes in developing various process automation software including web scraping and screen scraping software for desktop and web. Uipath web scraper is perfect for non-coders and easily surpasses most common data extraction challenges including page navigation, digging through flash and even scraping PDF files. All you need to do is open the web scraping wizard and simply highlight the data you need to extract. The tool will scrape all the data following this pattern at all pages you’ve chosen and sort it accordingly. You can add as many items for scraping as you like and have them sorted in respective columns. As a result, you receive a neat Excel or CSV document with all the data eliminated from duplicates.

Moreover, Uipath isn’t just about scraping. This software can be used not only for extracting data, but to manipulate the interface of another app, thus establishing data transfers among the two of them. Basically, this tool could be used to conduct any repetitive task a human could do, yet much faster and with higher accuracy.

Pros: You can automate form filling, clicking buttons, navigation etc. Uipath scraper is impressively accurate, fast and simple to use. It “reads” all types of data on screen (JS, HTML, Silverlight and more), plus you can train the software to emulate human actions of various complexity.

Cons: Premium software runs at a premium price. Uipath is an affordable professional solution, but may be a bit too pricey for personal use.

 Import.io  offers you a free desktop app to help you scrap all the data you need from an unlimited amount of web pages. The service treats each page as a potential data source to generate API from. If the page you’ve submitted has been previously processed, you can access its API and get some of the data. In other case, Import.io will guide you through the process of creating the scraping matrix by building connectors (for navigation) or extractors (to pull out the needed data). Afterwards, you submit a request for extraction and it’s typically processed within 24 hours. All the data is private and you can schedule auto refreshments at any chosen period of time.

Pros: The service is easy-to-use with no tech skills needed. It can  pages with data (those that needed login/pass), plus it’s free. Minimalistic effective design and simple navigation comes along.

Cons: Improt.io has hard times navigating through combinations of javascript/POST and cannot navigate from one page to another (e.g. click next, second page etc).  Sometimes, it takes over 24 hours to receive the report.  Besides, it’s a browser-only app, non-compatible with other applications.

Kimono is a popular web scraper among app developers who prefer to power up their products with live data and no additional code. It saves you tons of time when you need to fill up your app with mashing data. Install Kimono Browser bookmarklet; highlight page elements you need to and provide some positive/negative examples to train the tool. After labeling all the data you can download it in CSV/JSON/a web endpoint format. The APIs created for your pages are stored in the cloud and you can run them on schedule. So far, Kimono is free to use with pro and enterprise solutions to be launched soon.

Pros: The tool works pretty fast and works great with scraping newsfeeds and prices. The data is rather accurate.

Cons: No page navigation available and you need to spend quite a lot of time to train Kimono before it starts to pull out the multi items data accurate enough. In general, I’d say Kimono is more of an app mash-ups creator than a full-scale web scraper.

 Screen Scraper  is pretty neat and tackles a lot of difficult tasks including navigation and precise data extractions, however it requires a bit of programming/tokenization skills if you’d like to run it super smooth. Launch the software, add a proxy, start recording the list of your actions and creating extracting patterns (some coding required). Works great with HTML and Javascript, however you should test it with Citrix and other platforms. Basically, screen scraper helps you writing simple web scraping scripts and lets you download the extracted data in txt/csv/excel format.

Pros: When set correctly, there’s no data extraction tasks Screen scraper fails to handle.
Cons: The tool is pricey and you’ll have to go through documentation and have basic coding skills to use it.

Source URL :  http://tech.co/4-web-scraping-tools-save-time-data-extraction-2015-03

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 URL :http://yellowpagesdatascraping.blogspot.in/2015/07/ecj-clarifies-database-directive-scope.html