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.

Problems in the development of GIS based on data stream management systems

2017 ◽  
Vol 922 (4) ◽  
pp. 44-47 ◽  
Author(s):  
A.V. Materuhin
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Industrial Applications ◽  
Management Systems ◽  
Temporal Data ◽  
Data Stream Management ◽  
Problematic Situation ◽  
Stream Management ◽  
Data Stream Management Systems ◽  
Spatio Temporal

The article provides the analysis of the current situation in the use of data stream management systems (DSMS) and discusses the reasons why this technology is not used to develop geographic information systems. DSMS, despite its novelty, has ceased to be a pure research project and is used in industrial applications. However, this technology is not used to design the GIS, although the necessity of processing and analyzing of spatio-temporal data streams arises in many practically important applications. The essence of the current problematic situation is the gap between new technological capabilities and the lack of a theoretical framework for the processing and analysis of spatio-temporal data streams in DSMS. Existing spatial analytics algorithms are designed for relational databases with precomputed spatial indexes and are not suitable for DSMS. The article shows that, to resolve the current problematic situation with the geoinformation systems development based on DSMS should do the following

Data Stream Management Systems and Architectures

2007 ◽  
pp. 51-71 ◽  
Author(s):  
M. A. Hammad ◽  
T. M. Ghanem ◽  
W. G. Aref ◽  
A. K. Elmagarmid ◽  
M. F. Mokbel
Keyword(s):  
Data Stream ◽  
Management Systems ◽  
Data Stream Management ◽  
Stream Management ◽  
Data Stream Management Systems

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.

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