Utilizing Business Intelligence and Social Media Streams for optimized Web Service Compositions

Abstract

The sharp rise of Web 2.0 in the last decade has dramatically changed the way people communicate through the explosion of the usage of services and applications. Social media (SM) have emerged both as new sources of information and as popular means for rapid users' communication. Nowadays, there are numerous people who dedicate a large part of their time to interact with others via SM, expressing their opinions and thoughts on various topics. As a result, huge streams of content are generated which hide valuable information with high levels of noise. In order to extract the information we need, we have to separate the relevant from the useless information. Both researchers and practitioners have made major steps and have established methods and algorithms that manage appropriately this huge content and provide filtered information which is proved useful to know. Another revolutionary technology that has emerged during the past decades is the Web Services (WS) technology, which provides the means for loosely coupled distributed systems to interact and exchange information seamlessly. More and more enterprises rely on WS for the attainment of business goals. The reason behind this wide adoption of WS, is their ability to operate both as standalone services and as parts of value-Added compositions. And as business environments rapidly change, the need for dynamic changes in WS compositions is more than ever a necessity. It is of great importance for enterprises to be able to attain information regarding the suitability of WS as a mean for the fulfillment of their business needs. Going one step further, we may ask ourselves if the extracted information from SM can provide additional knowledge for businesses and help analysts to have a better understanding for unstructured data available in the Web, compared with the structured data they use internally in their firm. In this paper, we examine the connection between Business Intelligence (BI) extracted from SM and the impact it can have on the selection of WS. In addition we explore the uses of traditional OLAP (online analytical processing) methods and propose a theoretical framework for Web Service composition, based on the aforementioned ideas and technologies.