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Simulating Our LifeSteps by Example

Published: 03 October 2016 Publication History

Abstract

During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “shopping,” “driving,” and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such “spatio-temporal-textual” sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolisby-example, follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.

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    Published In

    cover image ACM Transactions on Spatial Algorithms and Systems
    ACM Transactions on Spatial Algorithms and Systems  Volume 2, Issue 3
    October 2016
    129 pages
    ISSN:2374-0353
    EISSN:2374-0361
    DOI:10.1145/3001646
    • Editor:
    • Hanan Samet
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 03 October 2016
    Accepted: 01 May 2016
    Revised: 01 January 2016
    Received: 01 August 2015
    Published in TSAS Volume 2, Issue 3

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    Author Tags

    1. Mobility data
    2. moving object trajectories
    3. semantic trajectories

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    • European Union's Horizon 2020 research and innovation program

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    • (2021)i4sea: a big data platform for sea area monitoring and analysis of fishing vessels activityGeo-spatial Information Science10.1080/10095020.2021.197105525:2(132-154)Online publication date: 19-Oct-2021
    • (2021)Data perspective on environmental mobile crowd sensingIntelligent Environmental Data Monitoring for Pollution Management10.1016/B978-0-12-819671-7.00012-9(269-288)Online publication date: 2021
    • (2016)On querying and mining semantic-aware mobility timelinesInternational Journal of Data Science and Analytics10.1007/s41060-016-0030-12:1-2(29-44)Online publication date: 1-Nov-2016

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