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Communication Dans Un Congrès Année : 2020

Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

Résumé

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd-Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and analyzing this continuously growing data raise several challenges due not only to their volume, their velocity, and their complexity but also to the gap between raw data samples and the desired application view in terms of correlation between observations and in terms of granularity. In this paper, we put forward a proposal that offers an abstract view of any spatio-temporal data series as well as their manipulation. Our approach allows to support this high-level logical view and provides efficient processing by mapping both the representation and the manipulation to an internal physical model. We explore an implementation within a distributed framework and envision the adaptation of data organization methods combining aggressive indexing and partitioning over time and space. The mapping from the logical view and the actual data storage will lead to revisiting the traditional database query rewriting and optimization techniques. This proposal is a first step in the objective of coping with the complexity, the imperfection of large data sizes in the MCS context.
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Dates et versions

hal-02861695 , version 1 (07-06-2023)

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Mariem Brahem, Karine Zeitouni, Laurent Yeh, Hafsa El Hafyani. Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams. 1st International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019 held in Conjunction with the 19th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, 2019, Würzburg, Germany. pp.66-82, ⟨10.1007/978-3-030-38081-6_6⟩. ⟨hal-02861695⟩
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