Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, rou...
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Although a large number of query processing algorithms in spatial network database (SNDB) have been studied, there exists little research on route-based queries. Since moving objects move only in spatial networks, route-based queries, like in-route nearest neighbor (IRNN), are essential for Location-based Service (LBS) and Telematics applications. However, the existing IRNN query processing algorithm has a problem in that it does not consider time and space constraints. Therefore, we, in this paper, propose IRNN query processing algorithms which take both time and space constraints into consideration. Finally, we show the effectiveness of our IRNN query processing algorithms considering time and space constraints by comparing them with the existing IRNN algorithm.
The widespread use of location-aware services and technologies which retrieve or answer spatial queries has received much interest in today's society. An increasing number of popular applications, such as digital ...
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The widespread use of location-aware services and technologies which retrieve or answer spatial queries has received much interest in today's society. An increasing number of popular applications, such as digital maps, make use of spatial databases and associated technologies. One of the most important branches of traditional spatial queries is the reverse nearest neighbour (RNN) search. This search retrieves points of interest that consider the query facility as the nearest facility. Most of the existing works on spatial databases only focus on point of interest retrieval. There is barely any work on a region of interest or neighbourhood retrieval. In this paper, we introduce the concept of a group version of reverse nearest neighbour queries called reverse nearest neighbourhood (RNNH) queries. The RNNH query finds all possible reverse nearest neighbourhoods where all the neighbourhoods consider the query facility as the nearest facility. We propose an efficient algorithm for processing snapshot RNNH queries by using R-tree index. The proposed algorithm incrementally retrieves all reverse nearest neighbourhoods of the query facility. We have conducted exhaustive experiments on both real and synthetic datasets to demonstrate the superiority of the proposed algorithm.
Because moving objects usually move on spatial networks in location-based service applications, their locations are updated frequently, leading to the degradation of retrieval performance. To manage the frequent updat...
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ISBN:
(数字)9789401796187
ISBN:
(纸本)9789401796187;9789401796170
Because moving objects usually move on spatial networks in location-based service applications, their locations are updated frequently, leading to the degradation of retrieval performance. To manage the frequent updates of moving objects' locations in an efficient way, we propose a new distributed grid scheme which utilizes node-based pre-computation technique to minimize the update cost of the moving objects' locations. Because our grid scheme manages spatial network data separately from the POIs (Point of Interests) and moving objects, it can minimize the update cost of the POIs and moving objects. Using our grid scheme, we propose a new k-nearest neighbor (k-NN) query processing algorithm which minimizes the number of accesses to adjacent cells during POIs retrieval in a parallel way. Finally, we show from our performance analysis that our k-NN query processing algorithm is better on retrieval performance than that of the existing S-GRID.
Optimal location (OL) queries are a type of spatial queries particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to buil...
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ISBN:
(纸本)9781424489589
Optimal location (OL) queries are a type of spatial queries particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an L-p space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their L-p distance. Motivated by the deficiency of the existing techniques, this paper presents the first study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We demonstrate the efficiency of our solutions through extensive experiments with real data.
In this demonstration paper, we present a powerful distributed framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, exploits opportun...
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ISBN:
(纸本)9781424489589
In this demonstration paper, we present a powerful distributed framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, exploits opportunistic and participatory sensing in order to quickly answer queries of the form: "Report objects (i.e., trajectories) that follow a similar spatio-temporal motion to Q, where Q is some query trajectory." SmartTrace, relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and privacy reasons. SmartTrace then deploys an efficient top-K query processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. Our demonstration shows how the SmartTrace algorithmics are ported on a network of Android-based smartphone devices with impressive query response times. To demonstrate the capabilities of SmartTrace during the conference, we will allow the attendees to query local smartphone networks in the following two modes: i) Interactive Mode, where devices will be handed out to participants aiming to identify who is moving similar to the querying node;and ii) Trace-driven Mode, where a large-scale deployment can be launched in order to show how the K most similar trajectories can be identified quickly and efficiently. The conference attendees will be able to appreciate how interesting spatio-temporal search applications can be implemented efficiently (for performance reasons) and without disclosing the complete user traces to the query processor (for privacy reasons)(1). For instance, an attendee might be able to determine other attendees that have participated in common sessions, in order to initiate new discussions and collaborations, without knowing their trajectory or revealing his/her own trajectory either.
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