In this paper, we present the system design and evaluation of a Space Filling Curve (SFC)-based P2P information discovery system OID. The OID system uses multiple SFCs to significantly optimize the performance of multi-attribute range queries, particularly for applications with a large number of data attributes where a single big SFC-based index is inefficient. The basic idea is to have multiple SFCbased indices and select the best one to perform a query. We also introduce two tree-based query optimizations that increase the scalability of the system.
In this paper, we present a Peer-to-Peer (P2P) spatial information discovery system that enables spatial range queries over Distributed Hash Tables (DHTs). Our system utilizes a less-distorting octahedral map projection in contrast to the quadrilateral projections used by majority of the previously proposed systems, to represent the spatial information. We also introduce a Space-Filling Curve (SFC)-based data placement strategy that reduces the probability of data hot-spots in the network. Moreover, we show that our system achieves scalable resolution of location-based range queries, by utilizing a tree-based query optimization algorithm. Compared to the basic query resolution algorithm, the query optimization algorithm reduces the average number of parallel messages used to resolve a query, by a factor of 96%.
Distributed Hash Table (DHT)-based peer-to-peer information discovery systems have emerged as highly scalable systems for information storage and discovery in massively distributed networks. Originally DHTs supported only point queries. However, recently they have been extended to support more complex queries, such as multiattribute range (MAR) queries. Generally, the support for MAR queries over DHTs has been provided either by creating an individual index for each data attribute or by creating a single index using the combination of all data attributes. In contrast to these approaches, we propose to create and modify indices using the attribute combinations that dynamically appear in MAR queries in the system.In this paper, we present an adaptive information discovery system that adapts the set of indices according to the dynamic set of MAR queries in the system. The main contribution of this paper is a four-phase index adaptation process. Our evaluations show that the adaptive information discovery system continuously optimizes the overall system performance for MAR queries. Moreover, compared to a non-adaptive system, our system achieves several orders of magnitude improved performance.
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