MobiPLACE*: A Distributed Framework for Spatio-Temporal Data Streams Processing Utilizing Mobile Clients’ Processing Power

2014 ◽  
pp. 78-88
Author(s):  
Victor Zakhary ◽  
Hicham G. Elmongui ◽  
Magdy H. Nagi
Keyword(s):  
Data Streams ◽  
Temporal Data ◽  
Distributed Framework ◽  
Processing Power ◽  
Mobile Clients ◽  
Spatio Temporal

Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework

2012 ◽  
Vol 4 (3) ◽  
pp. 63-84
Author(s):  
Jonathan Cazalas ◽  
Ratan K. Guha
Keyword(s):  
Parallel Processing ◽  
Data Streams ◽  
Graphics Processing Unit ◽  
Performance Model ◽  
Processing Unit ◽  
Data Streaming ◽  
Temporal Data ◽  
Processing Power ◽  
Spatio Temporal ◽  
Graphics Processing

The efficient processing of spatio-temporal data streams is an area of intense research. However, all methods rely on an unsuitable processor (Govindaraju, 2004), namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents a performance model of the execution of spatio-temporal queries over the authors’ GEDS framework (Cazalas & Guha, 2010). GEDS is a scalable, Graphics Processing Unit (GPU)-based framework, employing computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal queries over spatio temporal data streams. Experimental evaluation shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments and demonstrates that, despite the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. To move beyond the analysis of specific algorithms over the GEDS framework, the authors developed an abstract performance model, detailing the relationship of the CPU and the GPU. From this model, they are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based applications.

PLACE1A Query Processor for Handling Real-time Spatio-temporal Data Streams

2004 ◽  
pp. 1377-1380 ◽  
Author(s):  
M MOKBEL ◽  
X XIONG ◽  
W AREF ◽  
S HAMBRUSCH ◽  
S PRABHAKAR ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Temporal Data ◽  
Spatio Temporal

scholarly journals Window Operators for Processing Spatio-Temporal Data Streams on Unmanned Vehicles

2020 ◽  
Vol 1 ◽  
pp. 1-23
Author(s):  
Tobias Werner ◽  
Thomas Brinkhoff
Keyword(s):  
Data Streams ◽  
Unmanned Vehicles ◽  
Vehicle Speed ◽  
Temporal Data ◽  
Data Stream Management ◽  
Time Intervals ◽  
Stream Management ◽  
Data Stream Management Systems ◽  
Spatio Temporal ◽  
Sliding Distance

Abstract. Unmanned aerial and submersible vehicles are used in an increasing number of applications especially for data collection in misanthropic environments. During a mission, such vehicles generate multiple spatio-temporal data streams suitable to be processed by data stream management systems (DSMS). The main approach of a DSMS is limiting the elements of a stream by using sliding and tilting windows with time intervals as temporal condition. However, due to varying vehicle speed and limited on-board resources, such temporal windows do not provide adequate support for spatio-temporal problems. For solving this problem, we propose a set of six new spatio-temporal window operators in this paper. This set comprises of sliding distance, tilting distance, tilting waypoint, session distance, jumping distance and an area window to limit stream elements based on spatial conditions. Each of the listed operators provides an individual behaviour to support sophisticated applications like spatial interpolation and forecasting. An evaluation based on an example trajectory shows the benefit of the presented operators for spatio-temporal applications.

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