The aim stated by the editorial board of this text is “to present a framework, methods and tools for the integration of data mining and decision support as well as their application to business problems in a collaborative setting”. This is an ambitious aim for this text, given that it is quite clearly a product of significant European research project, the European “SolEUNet” project, “Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise”. The editors of this text appear to want to take the agenda further forward to address business and collaboration issues as well as the decision‐making stage of the problem‐solving process. I think they do well enough here, though it is difficult task and likely to have a relatively limited appeal.
Data mining, for those who are unclear about it, deals with the solution of problems by analysing data that already exists in databases. Decision support on the other hand can be interpreted in many different ways. The view emerging in this text seems to be that it is a broad, generic term that encompasses all aspects related to supporting people in making decisions. The key emphasis of the text, and its claim to uniqueness is its focus on integrating the two disciplines.
So does the book do it? It certainly appears to present the wider research constituency in either of these core areas, data mining and decision support, and students of knowledge management with some useful material. Indeed it goes further to build at the interface between these two areas in a potentially exciting way, offering those researching the area with further scope for their efforts. The text is very much a product, I hesitate to say “by‐product” of that substantial pan‐European research project. Those involved present their findings on different aspects of that research project in short, highly focused chapters. Each of these is reasonably well integrated, but each chapter could also be read on its own as a discrete piece of research. I'm not so convinced just how useful this text will be to practitioners however. Attempting to meet the needs and expectations of academics and business practitioners is, in my experience, a very difficult balance to obtain when writing a text or organising a reader such as this. Practitioners want a very different type of book to read in my opinion and I feel that this one would prove hard to digest for many. The abstracts at the start of each chapter are useful in giving the reader insights to what they can expect in the chapter itself.
The text is structured into four sections. Part 1 addresses “Basic Technologies” and is edited by one of the editorial team. In this section what are essentially introductory issues are developed. For example, in chapter one “Data Mining” is introduced and developed. Then chapter three considers “Decision Support” at its most basic level. Chapter 4 then introduces a discussion of the integration of the two subject areas.
Part 2, edited by one other of the editorial team, considers papers under the theme “Integration Aspects of Data Mining and Decision Support. There are four “chapters” in this section, and one example is “Decision support for data mining: an introduction to ROC analysis and its application to decision support”. ROC stands for “receiver operating characteristics”. Another example is “Processing for data mining and decision support” which focuses on data processing that can benefit from software support using a particular piece of software, Sumatra TT.
Part 3, again edited by a different member of the editorial board, deals with “Applications of data mining and decision support”. In this section the papers report on research carried in specific contexts. These range from traffic accidents in the UK, to web site access analysis for a national statistical agency, through to combining data mining and decision support to educational planning. There are in fact seven papers in this section, each giving brief insights to the work of the authors at the interface of the two core subject areas in this text – data mining and decision support.
The final section, edited by the final member of the editorial board, considers “Collaboration Aspects”. The final chapter was the most interesting for me, given its focus on the university‐industry relationship, but each of the five papers provides useful insights to other contexts such as prediction of resources for a health farm through to an environmental case study.
The text includes 271 pages, providing 22 papers; it is tough to refer to them as “chapters”. The editors do provide a brief note to guide the reader on how they might approach reading this text to get the best out of it, which is quite useful. The authors come from right across Europe, though the majority are based in Slovenia and many of the case material originates there. I found it a useful read which gave me further insights to aspects of decision making that were useful. It does have to be studied with attention, particularly in the later sections. The diagrams are well presented in most of the chapters, though in a couple the authors have not translated the words to English, which is a bit confusing. A useful text for any university library perhaps.
