Jensen, Christian Søndergaard

Pelanis, Mindaugas

Saltenis, Simonas

With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore&#39;s Law, it becomes increasingly feasible to store on-line all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of essence in this scenario. Past indexing techniques capture the position of an object up until the time of the most recent position sample, or they represent an object&#39;s position as a constant or linear function of time and capture the position from the current time and into the (near) future. This paper offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially extends partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore&#39;s Law, it becomes increasingly feasible to store on-line all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of essence in this scenario. Past indexing techniques capture the position of an object up until the time of the most recent position sample, or they represent an object&#39;s position as a constant or linear function of time and capture the position from the current time and into the (near) future. This paper offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially extends partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics

Indexing the Past, Present and Anticipated Future Positions of Moving Objects

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