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.

The World Wide Web has become part of our everyday life and information retrieval and data mining on the Web are now of enormous practical interest. The algorithms supporting these activities combine the view of the Web as a text repository and as a graph, induced in various ways by links among pages, links among hosts, or other similar networks. The aim of the 4th Workshop on Algorithms and Models for the Web-Graph (WAW 2006)wasto further the understanding of these Web-induced graphsand stimulate the developmentofhigh-performancealgorithmsandapplicationsthat use the graph structure of the Web. The workshop was meant both to foster an exchange of ideas among the diverse set of researchers already involved in this topic and to act as an introduction for the larger community to the state of the art in this area. The workshop program included invited keynote talks by Fan Chung-Graham (UCSD), Soumen Chakrabarti (IITB), Walter Willinger (ATT Research) and Filippo Menczer (Indiana). WAW 2006tookplaceonNovember30 andDecember 1 at the Ban?Inter- tionalResearchInstitute(BIRS),inBan?,Alberta(Canada).Itwasthefourthin a seriesof verysuccessful workshopson the Web graph.W AW 2002 (Vancouver) and 2004 (Rome) were held in conjunction with the Annual IEEE Symposium on Foundations of Computer Science (FOCS). WAW 2003 (Budapest) was held in conjunction with the 12th International World Wide Web Conference.

This book constitutes the refereed proceedings of the 28th Canadian Conference on Artificial Intelligence, Canadian AI 2015, held in Halifax, Nova Scotia, Canada, in June 2015.The 15 regular papers and 12 short papers presented together with 8 papers from the Graduate Student Symposium were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections such as agents, uncertainty and games; AI applications; NLP, text and social media mining; data mining and machine learning.