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The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R £ -tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

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The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R £ -tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

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's Law, it becomes increasingly feasible to store online 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 the essence in this scenario.Existing indexing techniques come in two forms. Some techniques capture the position of an object up until the time of the most recent position sample, while other techniques represent an object's position as a constant or linear function of time and capture the position from the current time and into the (near) future. This article offers an indexing technique capable of capturing the positions of moving objects at all points in time. The index substantially modifies partial persistence techniques, which support transaction time, to support valid time for monitoring applications. The performance of a timeslice query is independent of the number of past position samples stored for an object. No existing indices exist with these characteristics.

We are witnessing a proliferation of Internet-worked, geo-positioned mobile devices such as smartphones and personal navigation devices. Likewise, location-related services that target the users of such devices are proliferating. Consequently, server-side infrastructures are needed that are capable of supporting the locationrelated query and update workloads generated by very large populations of such moving objects.This paper presents a main-memory indexing technique that aims to support such workloads. The technique, called PGrid, uses a grid structure that is capable of exploiting the parallelism offered by modern processors. Unlike earlier proposals that maintain separate structures for updates and queries, PGrid allows both longrunning queries and rapid updates to operate on a single data structure and thus offers up-to-date query results. Because PGrid does not rely on creating snapshots, it avoids the stop-the-world problem that occurs when workload processing is interrupted to perform such snapshotting. Its concurrency control mechanism relies instead on hardware-assisted atomic updates as well as object-level copying, and it treats updates as non-divisible operations rather than as combinations of deletions and insertions; thus, the query semantics guarantee that no objects are missed in query results.Empirical studies demonstrate that PGrid scales near-linearly with the number of hardware threads on four modern multi-core processors. Since both updates and queries are processed on the same current data-store state, PGrid outperforms snapshot-based techniques in terms of both query freshness and CPU cycle-wise efficiency.

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Abstract.A location-based service called friend-locator notifies a user if the user is geographically close to any of the user's friends. Services of this kind are getting increasingly popular due to the penetration of GPS in mobile phones, but existing commercial friend-locator services require users to trade their location privacy for quality of service, limiting the attractiveness of the services. The challenge is to develop a communication-efficient solution such that (i) it detects proximity between a user and the user's friends, (ii) any other party is not allowed to infer the location of the user, and (iii) users have flexible choices of their proximity detection distances. To address this challenge, we develop a client-server solution for proximity detection based on an encrypted, grid-based mapping of locations. Experimental results show that our solution is indeed efficient and scalable to a large number of users.