Kumar Mehta

A Cost-Based Range Estimation for Mapping Top-k Selection Queries over Relational Databases

Finding efficient methods for supporting top-k relational queries has received significant attention in academic research. One of the approaches in the recent literature is query-mapping, in which top-k queries are mapped (translated) into equivalent range queries that relational database systems (RDBMSs) normally support. This approach combines the advantage of simplicity as well as practicality by avoiding the need for modifications to the query engine, or specialized data structures or indexing techniques to handle top-k queries separately. However, existing methods following this approach fall short of adequately modeling the problem environment and providing consistent results. In this article, the authors propose a cost-based range estimation model for the query-mapping approach. They provide a methodology for trading-off relevant query execution cost components and mapping a top-k query into a cost-optimal range query for efficient execution. Their experiments on real world and synthetic data sets show that the proposed strategy not only avoids the need to calibrate workloads on specific database contents, but also performs at least as well as prior methods.

Design, development and validation of an agent-based model of electronic auctions

This paper presents the design, development and validation methodology of an agent-based computational model of the B2C electronic auction marketplace. It aims at a comprehensive understanding of the varied issues governing a B2C electronic auction, incorporating the behavior of all relevant agents such as the auctioneer, the consumer and the retailer; and the environment in which these agents operate and interact. In contrast with conventional methods, agent based modeling employs a bottom-up modeling approach where behaviors of individual agents and rules for their interaction, specified at the micro level, give rise to emergent macro level phenomenon. The development methodology should ensure that agent models are aligned with theory, current knowledge of the field and observed phenomena, and output validity of the model also needs to be ascertained. Beginning with a general introduction to agent based computational modeling, this paper formalizes this alignment and validation methodology and elaborates each step, noting the rationale and means for achieving these. The manner in which this process was used in modeling B2C auctions is then described.

Evolutionary Induction of Trading Models

Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterizing rules obtained with alternate fitness criteria.

Range query estimation with data skewness for top-k retrieval

Top-k querying can significantly improve the performance of web-based business intelligence applications such as price comparison and product recommendation systems. Top-k retrieval involves finding a limited number of records in a relational database that are most similar to user-specified attribute-value pairs. This paper extends the cost-based query-mapping method for top-k retrieval by incorporating data skewness in range estimation. Experiments on real world and synthetic multi-attribute data sets show that incorporating data skewness provides a robust performance across different types of data sets, query sets, distance functions, and histograms.