On the book’s webpage, there is a brief description of the meaning of business intelligence (BI): “to large organizations, business intelligence promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels.”

The editor then updates the concept: “BI is now impacted by the ‘Big Data’ phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often web-based) sources, e.g., from social networks; blogs; competitors’, suppliers’, or distributors’ data; governmental or NGO-based analysis and papers; or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data.” This book is part of the “Lecture Notes in Business Information Processing (LNBIP)” collection. It is intended for those who want to deepen their knowledge of BI and its applications, and there is no prerequisite to understand all of the chapters that are described below.

The first chapter is about pattern mining. It contains examples and algorithms for mining frequent itemsets, and also explains the various classes of patterns that are studied in the typical literature, including one of the biggest problems in pattern mining, the “pattern explosion problem.”

The second chapter is a complete tutorial on process mining, including some examples of problems around decomposing process mining. At the end of this chapter, there is a tip about a tool called ProM that can be downloaded from a website and is particularly useful for understanding and practicing the main concepts of process mining. This tool is built into some commercial tools (it is not only for academic applications).

In the third chapter, “Ontology-Driven Business Intelligence for Comparative Data Analysis,” the authors present an approach for comparative data analysis that was developed in a joint research project. Data warehouses, use cases, and comparative data analysis projects are discussed in detail. This is a very thorough chapter; the authors present an integrated and coherent approach to ontology-driven comparative data analysis.

Chapter 4, “Open Access Semantic Aware BI,” is a conceptual chapter in which the concept of BI is presented. It includes an overview of the past and present and what to expect for the future, including the technological challenges to overcome according to the typical life cycles of IT systems. Important considerations include requirements engineering, modeling, and physical deployment.

The fifth chapter, “Transparent Forecasting Strategies in Database Management Systems,” provides a review of existing work and discusses its application in a transparent model-based database system focused on forecast models (it can also be applied to other statistical models).

The penultimate chapter, “On Index Structures for Star Query Processing in Data Warehouses,” gives an overview of the main concepts of data warehousing (DW) and basic index structures: bitmap index, jointed index, and bitmap join index. Based on these, the authors show how to build another index called Time-HOBI. In this chapter, they show some additional contributions as extensions of Time-HOBI and compare it to the Oracle bitmap and bitmap join indexes, as well as to materialized views for snowflake and star DW schema.

The last chapter, “Intelligent Wizard for Human Language Interaction in BI,” is about a novel approach that allows users to interact with a BI system via natural human language. It is presented with some screen shots. The conclusion is that the intelligent wizard is a useful step toward allowing everyone to use BI and analytics.

This is an excellent book for anyone who wants to understand issues in pattern and process mining, forecasting, and data warehousing. It can be useful at the upper-undergraduate and graduate levels. It is also an excellent resource for engineers, analysts, researchers, and practitioners working with DW in the fields of marketing, computer science, and information technology.