Abstract
We identify and formalize a novel join operator for two spatial pointsets P and Q. The common influence join (CIJ) returns the pairs of points (p,q),p isin P,q isin Q, such that there exists a location in space, being closer to p than to any other point in P and at the same time closer to q than to any other point in Q. In contrast to existing join operators between pointsets (i.e., e-distance joins and fc-closest pairs), CIJ is parameter- free, providing a natural join result that finds application in marketing and decision support. We propose algorithms for the efficient evaluation of CIJ, for pointsets indexed by hierarchical multi-dimensional indexes. We validate the effectiveness and the efficiency of these methods via experimentation with synthetic and real spatial datasets. The experimental results show that a non-blocking algorithm, which computes intersecting pairs of Voronoi cells on-demand, is very efficient in practice, incurring only slightly higher I/O cost than the theoretical lower bound cost for the problem.
Original language | English |
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Journal | Proceedings / International Conference on Data Engeenering |
Pages (from-to) | 100-109 |
Number of pages | 10 |
ISSN | 1063-6382 |
DOIs | |
Publication status | Published - 2008 |
Event | The 24th IEEE International Conference on Data Engineering (ICDE) - Cancun, Mexico Duration: 7 Apr 2008 → 12 Apr 2008 Conference number: 24 |
Conference
Conference | The 24th IEEE International Conference on Data Engineering (ICDE) |
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Number | 24 |
Country/Territory | Mexico |
City | Cancun |
Period | 07/04/2008 → 12/04/2008 |