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- W2123392557 abstract "Complexity of Adaptive Spatial Indexing for Robust Distributed Data by Matthew Vincent Mahoney Thesis Advisor: Philip K. Chan, Ph.D. A spatial index suitable for implementation of a multidimensionally keyed database (such as text retrieval system) in an unreliable, decentralized, distributed environment is shown to have complexity comparable to the Internet's Domain Name Service and better than USENET or Web search engines. The index is a graph mapped into Euclidean space with high smoothness, a property allowing efficient backtrack-free directed search techniques such as hill climbing. Updates are tested using random search, then edges are adaptively added to bypass local minima, network congestion, and hardware failures. Protocols are described. Empirical average case complexity for n data items are: storage, O(n log n); query, O(log n log log n); update, O(log n log log n), provided that the number of dimensions is fixed or grows no faster than O(log n)." @default.
- W2123392557 created "2016-06-24" @default.
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- W2123392557 date "1998-01-01" @default.
- W2123392557 modified "2023-09-26" @default.
- W2123392557 title "Complexity of Adaptive Spatial Indexing for Robust Distributed Data" @default.
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