2011 IEEE 12th International Conference on Mobile Data Management

Volume 1 • 6-9 June 2011

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  • [Front cover - Vol 1]

    Publication Year: 2011, Page(s): C1
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  • [Title page i]

    Publication Year: 2011, Page(s): i
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  • [Title page iii]

    Publication Year: 2011, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011, Page(s): iv
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  • Table of contents

    Publication Year: 2011, Page(s):v - x
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  • Message from General Chairs

    Publication Year: 2011, Page(s):xi - xii
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  • Message from Program Chairs

    Publication Year: 2011, Page(s):xiii - xiv
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  • Message from Panel Chairs

    Publication Year: 2011, Page(s): xv
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  • Message from Industrial Track Chairs

    Publication Year: 2011, Page(s): xvi
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  • Message from Demonstation Program Chairs

    Publication Year: 2011, Page(s): xvii
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  • IEEE MDM 2011 Conference Officers

    Publication Year: 2011, Page(s): xviii
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  • IEEE MDM 2011 Program Committee

    Publication Year: 2011, Page(s):xix - xx
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  • IEEE MDM 2011 Reviewers

    Publication Year: 2011, Page(s):xxi - xxii
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  • IEEE MDM 2011 Sponsors

    Publication Year: 2011, Page(s): xxiii
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  • Mobile Data - Essential Part of a Smarter Planet

    Publication Year: 2011, Page(s):1 - 2
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (283 KB) | HTML iconHTML

    A couple of trends are going to be of significant importance in the next decennium. There will be a confluence of developments which are already visible today: ubiquitous, world wide deployment and access to data through a world wide infrastructure, using mobile devices as well as wired devices; the need for every enterprise in the world to look ahead, in order to stay competitive and even survive... View full abstract»

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  • Mobile Data Management - A Dozen Years Later

    Publication Year: 2011, Page(s): 3
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (256 KB) | HTML iconHTML

    In 1999 I had the honor of presenting a Keynote talk entitled "Databases Unplugged: Challenges in Ubiquitous Data Management" at the inaugural MDM conference in Hong Kong. In that talk I spoke about the anticipated ubiquity of mobile devices and mobile applications and predicted some of the data management research challenges that could be foreseen from this sea change in the computational landsca... View full abstract»

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  • Mobility, Data Mining and Privacy Understanding Human Movement Patterns from Trajectory Data

    Publication Year: 2011, Page(s):4 - 5
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (243 KB) | HTML iconHTML

    The technologies of mobile communications and ubiquitous computing pervade our society, and wireless net works sense the movement of people and vehicles, generating large volumes of mobility data. This is a scenario of great opportunities and risks. On one side, mining this data can produce useful knowledge. On the other side, individual privacy is at risk, as the mobility data contain sensitive p... View full abstract»

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  • Challenges for Mobile Data Management in the Era of Cloud and Social Computing

    Publication Year: 2011, Page(s): 6
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (261 KB) | HTML iconHTML

    The mobile data management community is experiencing a rapid evolutionary change due to the worldwide diffusion of always-on mobile devices and to the increased popularity of location and context-aware mobile applications. Accordingly to recent studies, in two years from now one fourth of the total mobile data will come from audio and video streaming and nearly all the rest from other Internet ser... View full abstract»

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  • MD-HBase: A Scalable Multi-dimensional Data Infrastructure for Location Aware Services

    Publication Year: 2011, Page(s):7 - 16
    Cited by:  Papers (61)  |  Patents (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (798 KB) | HTML iconHTML

    The ubiquity of location enabled devices has resulted in a wide proliferation of location based applications and services. To handle the growing scale, database management systems driving such location based services (LBS) must cope with high insert rates for location updates of millions of devices, while supporting efficient real-time analysis on latest location. Traditional DBMSs, equipped with ... View full abstract»

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  • DiSCO: A Distributed Semantic Cache Overlay for Location-Based Services

    Publication Year: 2011, Page(s):17 - 26
    Cited by:  Papers (4)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (653 KB) | HTML iconHTML

    Location-based services (LBS) have gained tremendous popularity with millions of simultaneous users daily. LBS handle very large data volumes and face enormous query loads. Both the data and the queries possess high locality: spatial data is distributed very unevenly around the globe, query load is different throughout the day, and users often search for similar things in the same places. This cau... View full abstract»

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  • Multi-objective Query Optimization in Smartphone Social Networks

    Publication Year: 2011, Page(s):27 - 32
    Cited by:  Papers (6)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1369 KB) | HTML iconHTML

    The bulk of social network applications for smart phones (e.g., Twitter, Face book, Foursquare, etc.) currently rely on centralized or cloud-like architectures in order to carry out their data sharing and searching tasks. Unfortunately, the given model introduces both data-disclosure concerns (e.g., disclosing all captured media to a central entity) and performance concerns (e.g., consuming precio... View full abstract»

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  • Towards Online Multi-model Approximation of Time Series

    Publication Year: 2011, Page(s):33 - 38
    Cited by:  Papers (5)  |  Patents (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (358 KB) | HTML iconHTML

    The increasing use of sensor technology for various monitoring applications (e.g. air-pollution, traffic, climate-change, etc.) has led to an unprecedented volume of streaming data that has to be efficiently aggregated, stored and retrieved. Real-time model-based data approximation and filtering is a common solution for reducing the storage (and communication) overhead. However, the selection of t... View full abstract»

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  • Finding Regions of Interest from Trajectory Data

    Publication Year: 2011, Page(s):39 - 48
    Cited by:  Papers (11)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (1926 KB) | HTML iconHTML

    We show how to find regions of interest (ROIs) in trajectory databases. ROIs are regions where a large number of moving objects remain for at least a given time interval. Previous techniques use somewhat restrictive definitions for ROIs, and are parameter-dependent. They require sequential scanning of the entire dataset to find ROIs when the ROI parameters change. Our approach is parameter indepen... View full abstract»

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  • Efficient Online Algorithms for the Polygonal Approximation of Trajectory Data

    Publication Year: 2011, Page(s):49 - 57
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (439 KB) | HTML iconHTML

    The paper deals with the problem of how most efficiently to represent trajectory data with a piecewise -- constant velocity model of the object movement. Firstly, we experimented with fast and efficient offline algorithms based on heuristic and optimal algorithms for the approximation of planar and spatial curves for a given constraint on the maximum of the approximation error. Secondly, we develo... View full abstract»

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  • Exploring Spatial-Temporal Trajectory Model for Location Prediction

    Publication Year: 2011, Page(s):58 - 67
    Cited by:  Papers (21)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (601 KB) | HTML iconHTML

    Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict locations. Association relationships amon... View full abstract»

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