@article{WOS:000395099800005,
abstract = {Recent rapid development of wireless communication, mobile computing,
global navigation satellite systems (GNSS), and spatially enabled
sensors are leading to an exponential growth of available mobility data
produced continuously at high speed. Due to these advancements, a new
class of monitoring applications has come to the focus, including
real-time intelligent transportation systems, traffic monitoring and
mobile objects tracking. These new information flow processing (IFP)
application domains need to process huge volume of mobility data
arriving in the form of continuous data streams from mobile objects. IFP
applications are pushing traditional database technologies beyond their
limits due to their massively increasing data volumes and demands for
real-time processing. Mobility data, i.e. real-time, transient,
time-varying sequences of spatio-temporal data items, generated by
embedded positioning sensors demonstrates at least two Big Data core
features: volume and velocity. Existing distributed data stream
management systems (DSMS), real-time computing systems (RTCS) and their
processing models are dominantly based on relational paradigm and
continuous operator model. Thus, they have rudimentary spatio-temporal
capabilities, provide expensive fault recovery requiring either hot
replication or long recovery times, and do not handle faults and slow
nodes. The framework proposed in this paper is a cornerstone towards
efficient real-time managing and monitoring of mobile objects through
distributed spatio-temporal streams processing on large clusters. A
prototype implementation is rooted in a new stream processing model that
overcomes the challenges of current distributed stream processing models
and enable seamless integration with batch and interactive processing
like MapReduce.},
address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},
author = {Galic, Zdravko and Meskovic, Emir and Osmanovic, Dario},
doi = {10.1007/s10707-016-0264-z},
issn = {1384-6175},
journal = {GEOINFORMATICA},
keywords = {Big data; Data stream architectures; GeoStreaming;},
month = apr,
number = {2},
pages = {263--291},
publisher = {SPRINGER},
title = {{Distributed processing of big mobility data as spatio-temporal data streams}},
type = {Article},
volume = {21},
year = {2017}
}

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IFP applications are pushing traditional database technologies beyond their limits due to their massively increasing data volumes and demands for real-time processing. Mobility data, i.e. real-time, transient, time-varying sequences of spatio-temporal data items, generated by embedded positioning sensors demonstrates at least two Big Data core features: volume and velocity. Existing distributed data stream management systems (DSMS), real-time computing systems (RTCS) and their processing models are dominantly based on relational paradigm and continuous operator model. Thus, they have rudimentary spatio-temporal capabilities, provide expensive fault recovery requiring either hot replication or long recovery times, and do not handle faults and slow nodes. The framework proposed in this paper is a cornerstone towards efficient real-time managing and monitoring of mobile objects through distributed spatio-temporal streams processing on large clusters. 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Due to these advancements, a new\nclass of monitoring applications has come to the focus, including\nreal-time intelligent transportation systems, traffic monitoring and\nmobile objects tracking. These new information flow processing (IFP)\napplication domains need to process huge volume of mobility data\narriving in the form of continuous data streams from mobile objects. IFP\napplications are pushing traditional database technologies beyond their\nlimits due to their massively increasing data volumes and demands for\nreal-time processing. Mobility data, i.e. real-time, transient,\ntime-varying sequences of spatio-temporal data items, generated by\nembedded positioning sensors demonstrates at least two Big Data core\nfeatures: volume and velocity. Existing distributed data stream\nmanagement systems (DSMS), real-time computing systems (RTCS) and their\nprocessing models are dominantly based on relational paradigm and\ncontinuous operator model. Thus, they have rudimentary spatio-temporal\ncapabilities, provide expensive fault recovery requiring either hot\nreplication or long recovery times, and do not handle faults and slow\nnodes. The framework proposed in this paper is a cornerstone towards\nefficient real-time managing and monitoring of mobile objects through\ndistributed spatio-temporal streams processing on large clusters. A\nprototype implementation is rooted in a new stream processing model that\novercomes the challenges of current distributed stream processing models\nand enable seamless integration with batch and interactive processing\nlike MapReduce.},\naddress = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},\nauthor = {Galic, Zdravko and Meskovic, Emir and Osmanovic, Dario},\ndoi = {10.1007/s10707-016-0264-z},\nissn = {1384-6175},\njournal = {GEOINFORMATICA},\nkeywords = {Big data; Data stream architectures; GeoStreaming;},\nmonth = apr,\nnumber = {2},\npages = {263--291},\npublisher = {SPRINGER},\ntitle = {{Distributed processing of big mobility data as spatio-temporal data streams}},\ntype = {Article},\nvolume = {21},\nyear = {2017}\n}\n","author_short":["Galic, Z.","Meskovic, E.","Osmanovic, D."],"key":"WOS:000395099800005","id":"WOS:000395099800005","bibbaseid":"galic-meskovic-osmanovic-distributedprocessingofbigmobilitydataasspatiotemporaldatastreams-2017","role":"author","urls":{},"keyword":["Big data; Data stream architectures; GeoStreaming;"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/f/rTKwnBhyGKSSptYtt/My Collection.bib","dataSources":["DY3AeP9t2QujfB78L","MpTxM7aR49zFotABd"],"keywords":["big data; data stream architectures; geostreaming;"],"search_terms":["distributed","processing","big","mobility","data","spatio","temporal","data","streams","galic","meskovic","osmanovic"],"title":"Distributed processing of big mobility data as spatio-temporal data streams","year":2017}