In the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports that describe the state of business activities in the past, such as for questions like "How did our sales perform during the last quarter?" A decade later, there was a shift to more interactive content that presented how the business was performing at the present time, answering questions like "How are we doing right now?" Today the focus of BI users are looking into the future. "Given what I did before and how I am currently doing this quarter, how will I do next quarter?" Furthermore, fuelled by the demands of Big Data, BI systems are going through a time of incredible change. Predictive analytics, high volume data, unstructured data, social data, mobile, consumable analytics, and data visualization are all examples of demands and capabilities that have become critical within just the past few years, and are growing at an unprecedented pace. This book introduces research problems and solutions on various aspects central to next-generation BI systems. It begins with a chapter on an industry perspective on how BI has evolved, and discusses how game-changing trends have drastically reshaped the landscape of BI. One of the game changers is the shift toward the consumerization of BI tools. As a result, for BI tools to be successfully used by business users (rather than IT departments), the tools need a business model, rather than a data model. One chapter of the book surveys four different types of business modeling. However, even with the existence of a business model for users to express queries, the data that can meet the needs are still captured within a data model. The next chapter on vivification addresses the problem of closing the gap, which is often significant, between the business and the data models. Moreover, Big Data forces BI systems to integrate and consolidate multiple, and often wildly different, data sources. One chapter gives an overview of several integration architectures for dealing with the challenges that need to be overcome. While the book so far focuses on the usual structured relational data, the remaining chapters turn to unstructured data, an ever-increasing and important component of Big Data. One chapter on information extraction describes methods for dealing with the extraction of relations from free text and the web. Finally, BI users need tools to visualize and interpret new and complex types of information in a way that is compelling, intuitive, but accurate. The last chapter gives an overview of information visualization for decision support and text.

Autorentext

Dr. Raymond T. Ng is a Professor of Computer Science at the University of British Columbia.He received a Ph.D.in Computer Science from the University of Maryland in 1992. His main re search area for the past two decades is on data mining, with a specific focus on health informatics and text mining. He has pub lished over 150 peer-reviewed publications on data clustering, outlier detection, OLAP processing, health informatics, and text mining. He is the recipient of two best paper awards, from the 2001 ACM SIGKDD conference, which is the premier data min ing conference worldwide, and the 2005 ACM SIGMOD con ference, which is one of the top database conferences worldwide. He was a program co-chair of the 2009 International Conference on Data Engineering, and a program co-chair of the 2002 ACM SIGKDD conference. He was also one of the general co-chairs of the 2008 ACM SIGMOD conference. He was an editorial board member of the Very Large Database Journal and the IEEE Transactions on Knowledge and Data Engineering until 2008. Patricia C. Arocena is a Research Assistant in Computer Science at the University ofToronto.She received her M.Eng.in Electrical and Computer Engineering in 2001 and her Ph.D. in Computer Science (expected 2013), both from the University of Toronto. Her research focuses on developing techniques to support efficient and practical use of schema mappings in information integration, and in particular, on embracing incompleteness in the context of data-driven decision making. Denilson Barbosa is an Associate Professor of Computing Sci ence at the University of Alberta. He obtained a Ph.D. in 2005 from the University of Toronto, working on Web data manage ment. He received an IBM Faculty Award for his work on XML benchmarking, and an Alberta Ingenuity New Faculty Award for his work on extraction and integration of data from the Web. He received the Best Paper award at the 26th IEEE International Conference on Data Engineering (ICDE 2010). At the time of writing, he was a lead investigator on the NSERC Strategic Net work on Business Intelligence, through which the SONEX sys tem for large-scale relation extraction on the web is developed. Giuseppe Carenini is an Associate Professor of Computer Sci ence at the University of British Columbia.He is also an Associate member of the UBC Institute for Resources, Environment and Sustainability (IRES). Giuseppe has broad interdisciplinary in terests. His work on natural language processing and information visualization to support decision making has been published in over 80 peer-reviewed papers. Dr. Carenini was the area chair for "Sentiment Analysis, Opinion Mining, and Text Classification" of ACL 2009 and the area chair for "Summarization and Genera tion" of NAACL 2012. He has recently co-edited an ACM-TIST Special Issue on "Intelligent Visual Interfaces for Text Analysis." In July 2011, he published a co-authored book on Methods for Mining and SummarizingText Conver sations. In his work, Dr.Carenini has also extensively collaborated with industrial partners, including Microsoft and IBM. Giuseppe was awarded a Google Research Award and an IBM CASCON Best Exhibit Award in 2007 and 2010 respectively