Using a distributed quadtree index in peer-to-peer networks

Tanin, Egemen; Harwood, Aaron; Samet, Hanan

doi:10.1007/s00778-005-0001-y

Cited by 130 publications

(72 citation statements)

References 27 publications

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“…Quad trees have been previously used for a large number of goals, including for addressing geographical-related problems concerning the monitoring and tracking of moving objects [20] and for querying data in peer-to-peer networks [19]. QTree is inspired by these works, building a query specific tree during query dissemination.…”

Section: Related Workmentioning

confidence: 99%

Scalable Data Processing for Community Sensing Applications

Ferreira¹,

Duarte²,

Preguiça³

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Participatory Sensing is a new computing paradigm that aims to turn personal mobile devices into advanced mobile sensing networks. For popular applications, we can expect a huge number of users to both contribute with sensor data and request information from the system. In such scenario, scalability of data processing becomes a major issue. In this paper, we present a system for supporting participatory sensing applications that leverages cluster or cloud infrastructures to provide a scalable data processing infrastructure. We propose and evaluate three strategies for data processing in this architecture.

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Section: Related Workmentioning

confidence: 99%

Scalable Data Processing for Community Sensing Applications

Ferreira¹,

Duarte²,

Preguiça³

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…The index builds a multidimensional Cartesian space based on the Grid resource attributes. The logical index assigns regions of space [30] to GFAs in the resource discovery system. If a GFA is assigned a region in the multidimensional space, then it is responsible for handling all the activities related to RLQs and RUQs associated with that region.…”

Section: Resource Discoverymentioning

confidence: 99%

The Gridbus Middleware for Market‐Oriented Computing

Buyya

Venugopal

Ranjan

et al. 2009

Market‐Oriented Grid and Utility Computing

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“…However, complex queries like range and top-k are particularly challenging in DHT systems. There have been recent works addressing range [3,11,14] and top-k [1] queries for spatial databases, but as far as we know, spatial-temporal queries have not been proposed using DHT systems.…”

Section: Introductionmentioning

confidence: 99%

Complex Queries for Moving Object Databases in DHT-Based Systems

Hernández

Rodríguez

2008

Lecture Notes in Computer Science

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Abstract. Distributed moving object database servers are a feasible solution to the scalability problem of centralized database systems. In this paper we propose a distributed indexing method, using the Distributed Hash Table (DHT) paradigm, devised to efficiently support complex spatio temporal queries. We assume a setting in which there is a large number of database servers that keep track of events associated with a highly dynamic system of moving objects deployed in a spatial area. We present a technique for properly keeping the index up to date and efficiently processing range and top-k queries for moving object databases. We evaluated our system using event-driven simulators with demanding spatio temporal workloads and the results show good performance in terms of response time and network traffic.

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Using a distributed quadtree index in peer-to-peer networks

Cited by 130 publications

References 27 publications

Scalable Data Processing for Community Sensing Applications

Scalable Data Processing for Community Sensing Applications

The Gridbus Middleware for Market‐Oriented Computing

Complex Queries for Moving Object Databases in DHT-Based Systems

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