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

An Easy Way For Data Extraction

There are so many data scraping tools are available in internet. With these tools you can you download large amount of data without any stress. From the past decade, the internet revolution has made the entire world as an information center. You can obtain any type of information from the internet. However, if you want any particular information on one task, you need search more websites. If you are interested in download all the information from the websites, you need to copy the information and pate in your documents. It seems a little bit hectic work for everyone. With these scraping tools, you can save your time, money and it reduces manual work.

The Web data extraction tool will extract the data from the HTML pages of the different websites and compares the data. Every day, there are so many websites are hosting in internet. It is not possible to see all the websites in a single day. With these data mining tool, you are able to view all the web pages in internet. If you are using a wide range of applications, these scraping tools are very much useful to you.

The data extraction software tool is used to compare the structured data in internet. There are so many search engines in internet will help you to find a website on a particular issue. The data in different sites is appears in different styles. This scraping expert will help you to compare the date in different site and structures the data for records.

And the web crawler software tool is used to index the web pages in the internet; it will move the data from internet to your hard disk. With this work, you can browse the internet much faster when connected. And the important use of this tool is if you are trying to download the data from internet in off peak hours. It will take a lot of time to download. However, with this tool you can download any data from internet at fast rate.There is another tool for business person is called email extractor. With this toll, you can easily target the customers email addresses. You can send advertisement for your product to the targeted customers at any time. This the best tool to find the database of the customers.

 Source  URL : http://ezinearticles.com/?An-Easy-Way-For-Data-Extraction&id=3517104

Web Scraping : To Scrape or Not to Scrape?

Web scraping is on the rise and its legality is being debated. The future of big data could hang in the balance.

I decided that I might want to stop writing about all these successful dot-com businesses and get into the act myself. I mean, how hard could it be, aside from the fact that I have no expertise in any particular vertical, no technological knowledge, and no money? That last was a bit of a problem because I was going to need big data, and big data doesn't come cheap.

So, one day I'm talking to a direct-tech wizard and he says, “Why don't you just find a business you want to be in, find the most successful company, go to their website, and scrape some of their data?”

Scrape? My dad was a house painter. I used to help him during summers. The only scraping I knew was done with a putty knife. But that's what God invented the Internet for. Google turned up endless Web scraping services and I went to one called Automation Anywhere.

Its homepage told me not to try scraping on my own, that I could pay them as little as $1,995 for a program that would have me scraping away in minutes with no programming expertise. A video showed me how. Suppose I wanted to locate all the assisted living facilities in Detroit? (Bedpans! There's a wide-open Web business!) Automation Anywhere showed me how I could request a data pattern—name, address, phone, and service area of targets—apply their program to a rehab facility listing, and minutes later be in possession of tidy customer list on a spreadsheet. A box popped up asking if I had any questions for a live account manager. I did.

“I'm interested in this, but is it totally legal?” I asked Shine, the account rep.

Shine was slow to respond and came back disappointingly noncommittal: “You need to install the software at your end. Hence, you will need to check at your end for legal documents for the website.”

Shine had obviously been reading the same European news sites that I had. Last year Irish airline Ryanair filed suit against PR Aviation, a Dutch airfare comparison site, charging it with copyright infringement and breach of contract for scraping flight data from its site. The Court of Justice of the European Union (ECJ) dismissed the suit, saying the scraping amounted to “normal use” of a website. However, the ECJ did potentially leave the door open for businesses with unprotected databases, such as Ryanair, to establish contractual limitations on use of their databases by third parties. That opening, should it be entered into by airlines, might have businesses such as Expedia, Orbitz, and Priceline reimagining their business plans.

And it could have any other businesses that load up third-party data files through scraping activities doing the same. The reality, however, is that that's a big “and.”

“The airlines have been halfway successful taking travel agents to court, but it can take five years and then they lose,” said Gus Cunningham, CEO of ScrapeSentry, one of only “three and a half” companies, in his words, that block scrapers from websites.

Professional scrapers are not only out-front and plentiful—as the Google search demonstrated—they're also nimble and expensive to chase away. Basically, Cunningham said, it's a matter of stopping scrapers at the website door among the airlines, e-coms, real estate sites, and online gambling companies that are scraped the most. Cunningham's company monitors inbound Web traffic and uses an analysis engine to block suspect visitors per parameters set down by clients. ScanSentry has a nine-year-old database that helps it identify bad actors, much like Interpol with its criminal database. Then a human element must enter the process.

Some of Cunningham's clients feed bogus information to competitors identified as scrapers to ferret them out. In many cases, though, they opt to turn their heads. “Airlines have some flights where they just want to get as many butts as they can in the seats, so they won't concentrate their blocking efforts on those. They'll concentrate on the routes that are always jammed,” Cunningham said.

Unlike botnets that steal money by, say, serving bogus websites to siphon off programmatic ad dollars, scraping is not overtly criminal. In the wide sphere of digital commerce, it's probably most common that the scrap-ee is also a scrap-er. How vigilant vulnerable industries become, and how protective courts and law enforcement agencies grow, will depend on how much scraping activity increases. Cunningham said it's growing fast. More than one fifth of visitors to client websites last year were scrapers, according to a ScanSentry study. Among travel companies, meanwhile, scrapers doubled from 15% in 2013 to 33% last year.

“And,” Cunningham noted, “It is stealing.”

Source: http://www.dmnews.com/direct-line-blog/to-scrape-or-not-to-scrape/article/422662/

Data Scraping - About Hand Scraped Flooring

Hand scraped hardwood flooring is one of the best floors that you can install in your house.

Advantages of Hand Scraped Hardwood Flooring

The product comes with a number of advantages which include:

Antique and modern technology: The floor professionally brings out the best elements of both antique and modern technology. The modern elements are in the quality of the product.

Unique patterns: Who doesn't want to be unique? These floors allow you to create your unique design. If you are going to use a machine, all you need to do is to set the machine such that it creates the pattern that you want. If the floor will be scraped by a craftsman, you should ask the craftsman to craft your desired pattern.

Character: The different depths in the floor provide you with character and color that you can't find in other types of floors. As the sun changes its angle during the day, the nooks and valleys on the board lit differently thus providing your board with an endless rich appearance.

Durability: Experts have been able to show that hand-scraped hardwood retains its look for a long time. If your kid or pet hits the floor, the dent just blends with the rest of the character making it hard for people to tell that there is a dent.

Making the floors shine again

Although, the scraped floors are designed to look worn and aged, they are made from modern wood which needs to be taken care of in order to retain its original look.

To make the floors shine again you need to remove all the dust and dirt that might be causing the wood to look dull.

After doing this you should mix 1 gallon of warm water with ½ teaspoon of dishwashing detergent and use it to clean the surface of the floor. The aim of doing this is to remove any stains that might be on the floor. When you complete doing this you should dampen the piece of cloth with club soda and then use another piece of cloth to buff the wood until it shines.

Conclusion

This is what you need to know about hand scraped hardwood flooring. When cleaning the floors you should avoid using oil based soaps as they dull the surface making your efforts worthless.

If the above method of shining the floor doesn't work, you should mix one part white vinegar and one part of cooking oil and use it to clean the floor.

Source: http://ezinearticles.com/?About-Hand-Scraped-Flooring&id=8990255

Rvest: easy web scraping with R

Rvest is new package that makes it easy to scrape (or harvest) data from html web pages, by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%
  html_text() %>%
  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%
  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%
  .[[3]] %>%
  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

    Detect and repair text encoding problems with guess_encoding() and repair_encoding().
    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://www.r-bloggers.com/rvest-easy-web-scraping-with-r/

Web Scraping : Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Getting Data from the Web Scraping

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.

Figure 57. The International Atomic Energy Agency’s (IAEA) portal (news.iaea.org)

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

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.

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).

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.

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