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IEEE Internet Computing
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Information Systems
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Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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In this paper we describe HiPPIS, a system that enables efficient storage and on-line querying of multidimensional data organized into concept hierarchies and dispersed over a network. Our scheme utilizes an adaptive algorithm that automatically adjusts the level of indexing according to the granularity of the incoming queries, without assuming any prior knowledge of the workload. Efficient roll-up and drill-down operations take place in order to maximize the performance by minimizing query flooding. Extensive experimental evaluations show that, on top of the advantages that a distributed storage offers, our method answers the large majority of incoming queries, both point and aggregate ones, without flooding the network. At the same time, it manages to preserve the hierarchical nature of data. These characteristics are maintained even after sudden shifts in the workload.