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Scalable location-temporal range query processing for structured peer-to-peer networks

Abstract : Indexing and retrieving data by location and time allows people to share and explore massive geotagged datasets observed on social networks such as Facebook, Flickr, and Twitter. This scenario known as a Location Based Social Network (LBSN) is composed of millions of users, sharing and performing location-temporal range queries in order to retrieve geotagged data generated inside a given geographic area and time interval. A key challenge is to provide a scalable architecture that allow to perform insertions and location-temporal range queries from a high number of users. In order to achieve this, Distributed Hash Tables (DHTs) and the Peer-to-Peer (P2P) computing paradigms provide a powerful building block for implementing large scale applications. However, DHTs are ill-suited for supporting range queries because the use of hash functions destroy data locality for the sake of load balance. Existing solutions that use a DHT as a building block allow to perform range queries. Nonetheless, they do not target location-temporal range queries and they exhibit poor performance in terms of query response time and message traffic. This thesis proposes two scalable solutions for indexing and retrieving geotagged data based on location and time.
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Rudyar Cortés. Scalable location-temporal range query processing for structured peer-to-peer networks. Distributed, Parallel, and Cluster Computing [cs.DC]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066106⟩. ⟨tel-01552377v2⟩

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