Efficient generation of spatiotemporal relationships from spatial data streams and static data

2020 ◽  
Vol 57 (3) ◽  
pp. 102205
Author(s):  
Sungkwang Eom ◽  
Xiongnan Jin ◽  
Kyong-Ho Lee
Keyword(s):  
Data Streams ◽  
Spatial Data ◽  
Efficient Generation ◽  
Static Data ◽  
Spatiotemporal Relationships

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

Class-based Conditional MaxRS Query in Spatial Data Streams

2017 ◽  
Author(s):  
Mir Imtiaz Mostafiz ◽  
S. M. Farabi Mahmud ◽  
Muhammed Mas-ud Hussain ◽  
Mohammed Eunus Ali ◽  
Goce Trajcevski
Keyword(s):  
Data Streams ◽  
Spatial Data

A Distance-Window Approach for the Continuous Processing of Spatial Data Streams

2020 ◽  
Vol 11 (2) ◽  
pp. 16-30
Author(s):  
Salman Ahmed Shaikh ◽  
Akiyoshi Matono ◽  
Kyoung-Sook Kim
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Spatial Data ◽  
Moving Objects ◽  
Smart Cities ◽  
Continuous Processing ◽  
Law And Order ◽  
Essential Requirement ◽  
Stream Processing Engines ◽  
Window Approach

Real-time and continuous processing of citywide spatial data is an essential requirement of smart cities to guarantee the delivery of basic life necessities to its residents and to maintain law and order. To support real-time continuous processing of data streams, continuous queries (CQs) are used. CQs utilize windows to split the unbounded data streams into finite sets or windows. Existing stream processing engines either support time-based or count-based windows. However, these are not much useful for the spatial streams containing the trajectories of moving objects. Hence, this paper presents a distance-window based approach for the processing of spatial data streams, where the unbounded streams can be split with respect to the trajectory length. Since the window operation involves repeated computation, this work presents two incremental distance-based window approaches to avoid the repetition. A detailed experimental evaluation is presented to prove the effectiveness of the proposed incremental distance-based windows.

Unbounded Spatial Data Stream Query Processing using Spatial Semijoins

2021 ◽  
Vol 15 (02) ◽  
pp. 33-41
Author(s):  
Wendy Osborn
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Spatial Data ◽  
Data Stream ◽  
Spatial Extent ◽  
Common Region ◽  
Data Set ◽  
Spatial Join ◽  
The Common ◽  
Spatial Data Stream

In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

Processing Data Streams

2020 ◽  
pp. 20-39
Author(s):  
Parimala N.
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Continuous Queries ◽  
Stream Data ◽  
Continuous Sequence ◽  
Static Data ◽  
Transactional Data ◽  
Events Data

A data stream is a real-time continuous sequence that may be comprised of data or events. Data stream processing is different from static data processing which resides in a database. The data stream data is seen only once. It is too voluminous to store statically. A small portion of data called a window is considered at a time for querying, computing aggregates, etc. In this chapter, the authors explain the different types of window movement over incoming data. A query on a stream is repeatedly executed on the new data created by the movement of the window. SQL extensions to handle continuous queries is addressed in this chapter. Streams that contain transactional data as well as those that contain events are considered.

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