Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases. It is an emerging field that has attracted much attention in a very short period of time. This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational communities might reasonably provide synergy for further progress in data analysis.

In bio-medical domains there are many applications involving the modelling of multivariate time series (MTS) data. One area that has been largely overlooked so far is the particular type of time series where the data set consists of a large number of variables but with a small number of observations. In this paper we describe the development of a novel computational method based on genetic algorithms that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the order and associated parameters as a whole step. We apply this method to the prediction and modelling of glaucomatous visual field deterioration.

Abstract: OLAP applications are widely used in business applications. They are often (implicitly) defined on top of OLTP systems and extensively use aggregation and transformation functions. The main OLAP data structure is a multidimensional table with three kinds of attributes: so-called dimension attributes, implicit attributes given by aggregation functions and fact attributes. Domains of dimension attributes are structured and thus support a variety of aggregations. These aggregations are used to generate new values for the fact attributes. In this paper we systematically develop a theory for OLAP applications. We first define aggregation functions and use these to introduce an OLAP algebra. Based on these foundations we derive properties that guarantee or contradict correctness of OLAP computations. Finally, for pragmatical treatment of OLAP applications the OLTP-OLAP specification frame is introduced.

Technological advances mean that vast data sets are increasingly common. Such data sets provide us with unparallelled opportunities for modelling and predicting the likely outcome of future events. However, such data sets may also bring with them new challenges and difficulties. An awareness of these, and of the weaknesses as well as the possibilities of these large data sets, is necessary if useful forecasts are to be made. This paper looks at some of these difficulties, using illustrations with applications from various areas.