The expansion of the share of online auctions in electronic trade causes exponential growth of theft and deception associated with this retail channel. Trustworthy reputation systems are a crucial factor in fighting dishonest and malicious users. Unfortunately, popular online auction sites use only simple reputation systems that are easy to deceive, thus offering users little protection against organized fraud. In this paper we present a new reputation measure that is based on the notion of the density of sellers. Our measure uses the topology of connections between sellers and buyers to derive knowledge about trustworthy sellers. We mine the data on past transactions to discover clusters of interconnected sellers, and for each seller we measure the density of the seller's neighborhood. We use discovered clusters both for scoring the reputation of individual sellers, and to assist buyers in informed decision making by generating automatic recommendations. We perform experiments on data acquired from a leading Polish provider of online auctions to examine the properties of discovered clusters. The results of conducted experiments validate the assumptions behind the density reputation measure and provide an interesting insight into clusters of dense sellers.

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The efficient execution of a method has a great impact on a system response time. Optimising access to data returned by methods is difficult as methods are written in high-level programming languages. Moreover, estimating a method's execution cost is another serious problem because of the complexity of a method's code. A promising technique to tackle the problem of optimising execution of methods is based on method materialisation. Within our project we developed, so called, hierarchical method materialisation technique. In this paper we present a prototype system for hierarchical materialisation of methods and for the management of their results in an object-oriented database.

Abstract. Traditional static cost-based query optimization approach uses data statistics to evaluate costs of potential query execution plans for a given query. Unfortunately, this approach cannot be directly applied to Web environment due to limited availability of statistics and unpredictable delays in access to data sources. To cope with lack or limited availability of statistics we propose a novel competitive query execution strategy. The basic idea is to initiate simultaneously several equivalent query execution plans and measure dynamically their progress. Processing of the most promising plan is continued, whereas processing of remaining plans is stopped. We also present in the paper results of performance evaluation of the proposed strategy.