Over the last decade we have been witnessing an increasing use of Business Intelligence (BI) solutions, which allow business people to query, understand, and analyze their business data in order to make better decisions. Traditionally, BI applications allowed business people to acquire useful knowledge from the data of their organization by means of a variety of technologies, such as data warehousing, data mining, business performance management, OLAP, periodical business reports, and the like.
Yet, in the very recent years, a new trend emerged: BI applications no longer limit their analysis to the data inside one company. Increasingly, they also source their data from the outside, i.e., from the Web, and complement company-internal data with value-adding information from the Web (e.g., retail prices of products sold by competitors), in order to provide richer insights into the dynamics of today's business.
In parallel to the move of data from the Web into BI applications, we are now assisting to the move of BI applications from company-internal information systems to the Web: BI as a service (e.g., hosted BI platforms for small- and medium-size companies) is the target of huge investments and the focus of large research efforts by industry. The idea is that of outsourcing the processing and analysis of large bodies of data and consuming BI from the cloud.
We associate the above dynamics in the BI landscape with the following research challenges:
- BI with Web data
In the last decade, the amount and complexity of data available on the Web has been growing rapidly. As a consequence, designers of BI applications making use of data from the Web have to deal with several issues. Among the most interesting challenges we find, for instance, the extraction and integration of heterogeneous data sources. But there are many other interesting research challenges that arise in the moment the Web is seen as data repository: how to develop Web warehousing solutions, how to tackle with data quality issues, how to leverage semantic Web technologies, how to employ Web mining, how to do BI with unstructured data (e.g., text) or semi-structured data (e.g., XML), and so on. Also, a recently emerged research challenge is Web Intelligence, which explores the use of Artificial Intelligence in conjunction with or relation to Web technologies. Other interesting topics arise when Web usage data (e.g., logs, data streams, click streams, etc.) are analyzed and used in BI applications, since these data can give support to the development of Web applications, for example to achieve advanced levels of adaptivity in websites.
- ENgineering Web-Enabled BI
The move of BI applications from company-internal information systems to applications that are accessible over the Web implies the need for web-specific design competencies. In this context, we strongly believe that (existing and future) Web engineering methodologies and technologies represent a large body of knowledge and expertise that could be very useful in the design of applications that allow decision makers to access BI data and functionalities over the Web. Good Web engineering is also the foundation of the design of real-time BI and business performance management applications, as through the Web applications provide access to data from anywhere, at anytime, and via any media. But BI on the Web also implies a plethora of new research challenges that are specific to the BI context, e.g., using Web mashups and RIA for BI development, usability and accessibility for BI applications, security issues in BI, etc.
The International Workshop on Business intelligencE and the WEB (BEWEB) intends to target the above two moves and creates an international forum for exchanging ideas on how to leverage the huge amount of data that is available on the Web in BI applications, and how to apply Web-related engineering methods and techniques to the design of BI applications, such as BI as a service.