A distributed hybrid index for processing continuous range queries over moving objects

Central to many location-based services is the problem of processing concurrent continuous range queries over a large scale of moving objects. Most relevant works to this problem mainly investigate the centralized search algorithms based on a single server for handling range queries. However, due to the limited resources of a single server, these algorithms hardly can deal with an ocean of objects and extensive concurrent queries. Moreover, these approaches usually suppose either objects or queries are static but seldom consider the scenario that objects and queries are both moving simultaneously, restricting the practicability of these approaches. To resolve the above issues, we propose a distributed hybrid index (DHI) that consists of a global grid index and extensive local VR-tree indexes. DHI is apt to be deployed on a cluster of servers, and owns a good scalability to maintain numerous moving objects and concurrent range queries. Based on DHI, we further design a distributed incremental search approach, which organizes multiple servers with a publish/subscribe mechanism to calculate and monitor the results for continuous range queries in a distributed pattern. Finally, we conduct extensive experiments to fully evaluate the performance of our paper.Peer ReviewedPostprint (author's final draft

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

Continuous Nearest Neighbor Queries over Sliding Windows

Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.

Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based e-coupon and ad-targeting systems enable advertisers to register millions of ads to millions of users. The number of users is typically very high and they are continuously moving, and the ads change frequently as well. Hence sending the right ad to the matching users is very challenging. Existing streaming systems are either centralized or are not spatial-keyword aware, and cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. This paper presents Tornado, a distributed spatial-keyword stream processing system. Tornado features routing units to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing units use the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. Tornado uses evaluators to process the data objects against the queries. The routing units minimize the redundant communication by not sending data updates for processing when these updates do not match any query. By applying dynamically evaluated cost formulae that continuously represent the processing overhead at each evaluator, Tornado is adaptive to changes in the workload. Extensive experimental evaluation using spatio-textual range queries over real Twitter data indicates that Tornado outperforms the non-spatio-textually aware approaches by up to two orders of magnitude in terms of the overall system throughput

Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012