This is the second volume of the Advances in Data Warehousing and Mining (ADWM) book series. ADWM publishes books in the areas of data warehousing and mining. The topic of this volume is data mining and knowledge discovery. This volume consists of 14 chapters in four section, contributed by authors and editorial board members from the International Journal of Data Warehousing and Mining, as wel...

In enterprises, a large volume of data has been collected and stored in data warehouses. Advances in data gathering, storage, and distribution have created a need for integrating data warehousing and data mining techniques. Mining data warehouses raises unique issues and requires special attention. Data warehousing and data mining are interrelated , and require holistic techniques from the two ...

XML is suitable for structuring complex data coming from different sources and supported by heterogeneous formats. It allows a flexible formalism capable to represent and store different types of data. Therefore, the importance of integrating XML documents in data warehouses is becoming increasingly high. In this paper, we propose an XMLbased methodology, named X-Warehousing, which designs ware...

Nowadays, many decision support applications need to exploit data that are not only numerical or symbolic, but also multimedia, multistructure, multisource, multimodal, and/or multiversion. We term such data complex data. Managing and analyzing complex data involves a lot of different issues regarding their structure, storage and processing, and metadata are a key element in all these processes...

The goal of the data warehousing project at Stanford (the WHIPS project) is to develop algorithms and tools for the efficient collection and integration of information from heterogeneous and autonomous sources, including legacy sources. In this paper we give a brief overview of the WHIPS project, and we describe some of the research problems being addressed in the initial phase of the project.