Simonas Saltenis

Indexing the positions of continuously moving objects

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.

Nearest neighbor and reverse nearest neighbor queries for moving objects

With the proliferation of wireless communications and the rapid advances in technologies for tracking the positions of continuously moving objects, algorithms for efficiently answering queries about large numbers of moving objects increasingly are needed. One such query is the reverse nearest neighbor (RNN) query that returns the objects that have a query object as their closest object. While algorithms have been proposed that compute RNN queries for non-moving objects, there have been no proposals for answering RNN queries for continuously moving objects. Another such query is the nearest neighbor (NN) query, which has been studied extensively and in many contexts. Like the RNN query, the NN query has not been explored for moving query and data points. This paper proposes an algorithm for answering RNN queries for continuously moving points in the plane. As a part of the solution to this problem and as a separate contribution, an algorithm for answering NN queries for continuously moving points is also proposed. The results of performance experiments are reported.

Indexing the past, present, and anticipated future positions of moving objects

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 Moores 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 objects 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.

Efficient indexing of the historical, present, and future positions of moving objects

Although significant effort has been put into the development of efficient spatio-temporal indexing techniques for moving objects, little attention has been given to the development of techniques that efficiently support queries about the past, present, and future positions of objects. The provisioning of such techniques is challenging, both because of the nature of the data, which reflects continuous movement, and because of the types of queries to be supported. This paper proposes the BBx -index structure, which indexes the positions of moving objects, given as linear functions of time, at any time. The index stores linearized moving-object locations in a forest of B+ -trees. The index supports queries that select objects based on temporal and spatial constraints, such as queries that retrieve all objects whose positions fall within a spatial range during a set of time intervals. Empirical experiments are reported that offer insight into the query and update performance of the proposed technique.

The islands approach to nearest neighbor querying in spatial networks

Much research has recently been devoted to the data management foundations of location-based mobile services. In one important scenario, the service users are constrained to a transportation network. As a result, query processing in spatial road networks is of interest. We propose a versatile approach to k nearest neighbor computation in spatial networks, termed the Islands approach. By offering flexible yet simple means of balancing re-computation and pre-computation, this approach is able to manage the trade-off between query and update performance. The result is a single, efficient, and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance, for both queries and updates.

A Location Privacy Aware Friend Locator

A location-based service called friend-locator notifies a user if the user is geographically close to any of the users 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 users 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.

Trees or grids?: indexing moving objects in main memory

New application areas, such as location-based services, rely on the efficient management of large collections of mobile objects. Maintaining accurate, up-to-date positions of these objects results in massive update loads that must be supported by spatial indexing structures and main-memory indexes are usually necessary to provide high update performance. Traditionally, the R-tree and its variants were used for indexing spatial data, but most of the recent research assumes that a simple, uniform grid is the best choice for managing moving objects in main memory. We perform an extensive experimental study to compare the two approaches on modern hardware. As the result of numerous design-and-experiment iterations, we propose the update- and query-efficient variants of the R-tree and the grid. The experiments with these indexes reveal a number of interesting insights. First, the coupling of a spatial index, grid or R-tree, with a secondary index on object IDs boosts the update performance significantly. Next, the R-tree, when combined with such a secondary index, can provide update performance competitive with the grid. Finally, the grid can compete with the R-tree in terms of the query performance and it is surprisingly robust to varying parameters of the workloads. In summary, the study shows that, in most cases, the choice of the index boils down to the issues such as the ease of implementation or the support for spatially extended objects.

Developing a DataBlade for a new index

In order to better support current and new applications, the major DBMS vendors are stepping beyond uninterpreted binary large objects, termed BLOBs, and are beginning to offer extensibility features that allow external developers to extend the DBMS with, e.g., their own data types and accompanying access methods. Existing solutions include DB2 extenders, Informix DataBlades, and Oracle cartridges. Extensible systems offer new and exciting opportunities for researchers and third party developers alike. The paper reports on an implementation of an Informix DataBlade for the GR-tree, a new R-tree based index. This effort represents a stress test of the perhaps currently most extensible DBMS, in that the new DataBlade aims to achieve better performance, not just to add functionality. The paper provides guidelines for how to create an access method DataBlade, describes the sometimes surprising challenges that must be negotiated during DataBlade development, and evaluates the extensibility of the Informix Dynamic Server.

Parallel main-memory indexing for moving-object query and update workloads

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 location-related 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 long-running 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.

Thread-level parallel indexing of update intensive moving-object workloads

Modern processors consist of multiple cores that each support parallel processing by multiple physical threads, and they offer ample main-memory storage. This paper studies the use of such processors for the processing of update-intensive moving-object workloads that contain very frequent updates as well as contain queries. The non-trivial challenge addressed is that of avoiding contention between long-running queries and frequent updates. Specifically, the paper proposes a grid-based indexing technique. A static grid indexes a near up-to-date snapshot of the data to support queries, while a live grid supports updates. An efficient cloning technique that exploits the memcpy system call is used to maintain the static grid. An empirical study conducted with three modern processors finds that very frequent cloning, on the order of tens of milliseconds, is feasible, that the proposal scales linearly with the number of hardware threads, and that it significantly outperforms the previous state-of-the-art approach in terms of update throughput and query freshness.