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Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction.

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Online market intelligence (OMI), in particular competitive intelligence for product pricing, is a very important application area for Web data extraction. However, OMI presents non-trivial challenges to data extraction technology. Sophisticated and highly parameterized navigation and extraction tasks are required. On-the-fly data cleansing is necessary in order two identify identical products from different suppliers. It must be possible to smoothly define data flow scenarios that merge and filter streams of extracted data stemming from several Web sites and store the resulting data into a data warehouse, where the data is subjected to market intelligence analytics. Finally, the system must be highly scalable, in order to be able to extract and process massive amounts of data in a short time. Lixto (www.lixto.com), a company offering data extraction tools and services, has been providing OMI solutions for several customers. In this paper we show how Lixto has tackled each of the above challenges by improving and extending its original data extraction software. Most importantly, we show how high scalability is achieved through cloud computing. This paper also features a case study from the computers and electronics market.

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