Yellow Tree: A Distributed Main-memory Spatial Index Structure for Moving Objects

Mobile devices equipped with wireless technologies to communicate and positioning systems to locate objects of interest are common place today, providing the impetus to develop location-aware applications. At the heart of location-aware applications are moving objects or objects that continuously change location over time, such as cars in transportation networks or pedestrians or postal packages. Location-aware applications tend to support the tracking of very large numbers of such moving objects as well as many users that are interested in finding out about the locations of other moving objects. Such location-aware applications rely on support from database management systems to model, store, and query moving object data. The management of moving object data exposes the limitations of traditional (spatial) database management systems as well as their index structures designed to keep track of objects\u27 locations. Spatial index structures that have been designed for geographic objects in the past primarily assume data are foremost of static nature (e.g., land parcels, road networks, or airport locations), thus requiring a limited amount of index structure updates and reorganization over a period of time. While handling moving objects however, there is an incumbent need for continuous reorganization of spatial index structures to remain up to date with constantly and rapidly changing object locations. This research addresses some of the key issues surrounding the efficient database management of moving objects whose location update rate to the database system varies from 1 to 30 minutes. Furthermore, we address the design of a highly scaleable and efficient spatial index structure to support location tracking and querying of large amounts of moving objects. We explore the possible architectural and the data structure level changes that are required to handle large numbers of moving objects. We focus specifically on the index structures that are needed to process spatial range queries and object-based queries on constantly changing moving object data. We argue for the case of main memory spatial index structures that dynamically adapt to continuously changing moving object data and concurrently answer spatial range queries efficiently. A proof-of concept implementation called the yellow tree, which is a distributed main-memory index structure, and a simulated environment to generate moving objects is demonstrated. Using experiments conducted on simulated moving object data, we conclude that a distributed main-memory based spatial index structure is required to handle dynamic location updates and efficiently answer spatial range queries on moving objects. Future work on enhancing the query processing performance of yellow tree is also discussed

Adaptive schemes for location update generation in execution location-dependent continuous queries

Cataloged from PDF version of article.An important feature that is expected to be owned by today's mobile computing systems is the ability of processing location-dependent continuous queries on moving objects. The result of a location-dependent query depends on the current location of the mobile client which has generated the query as well as the locations of the moving objects on which the query has been issued. When a location-dependent query is specified to be continuous, the result of the query can continuously change. In order to provide accurate and timely query results to a client, the location of the client as well as the locations of moving objects in the system has to be closely monitored. Most of the location generation methods proposed in the literature aim to optimize utilization of the limited wireless bandwidth. The issues of correctness and timeliness of query results reported to clients have been largely ignored. In this paper, we propose an adaptive monitoring method (AMM) and a deadline-driven method (DDM) for managing the locations of moving objects. The aim of our methods is to generate location updates with the consideration of maintaining the correctness of query evaluation results without increasing location update workload. Extensive simulation experiments have been conducted to investigate the performance of the proposed methods as compared to a well-known location update generation method, the plain dead-reckoning (pdr). © 2005 Elsevier Inc. All rights reserved