7,089 research outputs found
Location-based indexing for mobile context-aware access to a digital library
Mobile information systems need to collaborate with each other to provide seamless information access to the user. Information about the user and their context provides the points of contact between the systems. Location is the most basic user context.
TIP is a mobile tourist information system that provides location-based access to documents in the digital library Greenstone. This paper identifies the challenges for providing effcient access to location-based information using the various access modes a tourist requires on their travels. We discuss our extended 2DR-tree approach to meet these challenges
HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces
Nearest neighbor searching of large databases in high-dimensional spaces is
inherently difficult due to the curse of dimensionality. A flavor of
approximation is, therefore, necessary to practically solve the problem of
nearest neighbor search. In this paper, we propose a novel yet simple indexing
scheme, HD-Index, to solve the problem of approximate k-nearest neighbor
queries in massive high-dimensional databases. HD-Index consists of a set of
novel hierarchical structures called RDB-trees built on Hilbert keys of
database objects. The leaves of the RDB-trees store distances of database
objects to reference objects, thereby allowing efficient pruning using distance
filters. In addition to triangular inequality, we also use Ptolemaic inequality
to produce better lower bounds. Experiments on massive (up to billion scale)
high-dimensional (up to 1000+) datasets show that HD-Index is effective,
efficient, and scalable.Comment: PVLDB 11(8):906-919, 201
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