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Segmentation methods used in marketing literature range from those based on demographic variables to those relying on more sophisticated clustering techniques. Grouping consumers into relatively homogeneous classes is of great importance for marketers since it enables them to develop specific products and marketing strategies to better cater these segments. A new segmentation method based on the heterogeneity among consumers' perceptions of underlying product-related factors, is proposed. Specifically, variation of factor loadings, coupled with a highly intensive computer technique (bootstrap) is used to classify consumers into homogeneous groups. The validity of this segmentation scheme is compared to those of traditional segmentation methods, by examining the forecasting abilities of a choice model under these different scenarios.

Technical report TR95-17. Information Superhighway environments such as the Internet have brough us ready access to large amounts of information. However, Internet data is notoriously unorganized and autonomously managed in a distributed fashion. Large scale information monitoring in the Internet environment requires support beyond traditional database techniques. Two of the key issues are the increasing reward in monitoring a fast growing information base and the similarly increasing processing cost. To improve the expressiveness of queries for information monitoring, we define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support effiecient processing of continual queries, we propose a differential re-evaluation algorithm (DRA), which exploits the structure and information contained in both the query expressions and the database update operations. The DRA design can be seen as a synthesis of previous research on differential files, incrementala view maintenance, and active databases. | TRID-ID TR95-17

Several studies have recently concentrated on the generation of wrappers for extracting data from Web data sources. The ROADRUNNER system aims at automating the tedious and expensive process of writing wrappers in an unsupervised, domain-independent, and scalable manner. The system is based on a grammar inference algorithm, called MATCH, which has been designed in a sound theoretical framework. However, in its original definition MATCH lacks in expressivity; that is, in many cases when MATCH runs over real-life Web pages, it is not able to produce a solution. In this paper we address the challenging issue of developing techniques that allow us to build upon MATCH an effective and efficient system, without renouncing to the original formal background. First, we analyze the main limitations of MATCH; then we illustrate the techniques we have developed to overcome such limitations. Finally we report on the results of some experiments, that show the efficacy of the introduced techniques and demonstrate the improvements of the overall system.