Distributed deviation detection in sensor networks
ACM SIGMOD Record
Mining approximate top-k subspace anomalies in multi-dimensional time-series data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
LACO: A location-aware cooperative query system for securely personalized services
Expert Systems with Applications: An International Journal
On space constrained set selection problems
Data & Knowledge Engineering
Cooperative query answering by abstract interpretation
SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
A knowledge mining framework for business analysts
ACM SIGMIS Database
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Abstract: Much research has been devoted to the efficient computation of relational aggregations and specifically the efficient execution of the datacube operation. In this paper we consider the inverse problem, that of deriving (approximately) the original data from the aggregates. We motivate this problem in the context of two specific application areas, that of approximate query answering and data analysis. We propose a framework based on the notion of information entropy, that enables us to estimate the original values in a data set, given only aggregated information about it. We also describe an alternate utility of the proposed framework, that enables us to identify values that deviate from the underlying data distribution, suitable for data mining purposes. Finally, we present a detailed performance study of the algorithms using both real and synthetic data, highlighting the benefits of our approach as well as the efficiency of the proposed solutions.