Spatio-Temporal Data Management for Moving Objects

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.

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Architecture of RFID Spatio-Temporal Data Management

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.314-316.2425 ◽

2011 ◽

Vol 314-316 ◽

pp. 2425-2428 ◽

Cited By ~ 40

Author(s):

Yong Hui Wang ◽

Huan Liang Sun ◽

Jing Ke Xu

Keyword(s):

Data Management ◽

Asset Management ◽

Data Model ◽

Temporal Data ◽

Rfid Technology ◽

Key Technology ◽

Memory Cache ◽

Rfid Middleware ◽

Spatio Temporal ◽

The Internet Of Things

With the development of RFID technology, we can identify, locate, track and monitor items in supply chain, retail store, and asset management applications. RFID has become a key technology in the Internet of Things. But RFID data can’t be effectively managed by only using traditional data model because they have their own unique characteristics, such as aggregation, location, temporal and history-oriented, which have to be fully considered and integrated into the data model. Therefore, the architecture of RFID spatio-temporal data management is proposed in this paper. We provide a brief overview of RFID technology and highlight a few of the spatio-temporal data management challenges, such as RFID middleware, RFID event processing, In-memory cache.

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Semantically Enriched Simplification of Trajectories

Proceedings of the ICA ◽

10.5194/ica-proc-2-128-2019 ◽

2019 ◽

Vol 2 ◽

pp. 1-8

Author(s):

Rajesh Tamilmani ◽

Emmanuel Stefanakis

Keyword(s):

Data Structure ◽

Moving Objects ◽

Temporal Data ◽

Temporal Dimension ◽

Line Simplification ◽

Data Points ◽

Spatio Temporal ◽

Gps Devices ◽

The Impact ◽

Spatial And Temporal Characteristics

<p><strong>Abstract.</strong> Moving objects that are equipped with GPS devices generate huge volumes of spatio-temporal data. This spatial and temporal information is used in tracing the path travelled by the object, so called trajectory. It is often difficult to handle this massive data as it contains millions of raw data points. The number of points in a trajectory is reduced by trajectory simplification techniques. While most of the simplification algorithms use the distance offset as a criterion to eliminate the redundant points, temporal dimension in trajectories should also be considered in retaining the points which convey both the spatial and temporal characteristics of the trajectory. In addition to that the simplification process may result in losing the semantics associated with the intermediate points on the original trajectories. These intermediate points can contain attributes or characteristics depending on the application domain. For example, a trajectory of a moving vessel can contain information about distance travelled, bearing, and current speed. This paper involves implementing the Synchronized Euclidean Distance (SED) based simplification to consider the temporal dimension and building the Semantically Enriched Line simpliFication(SELF) data structure to preserve the semantic attributes associated to individual points on actual trajectories. The SED based simplification technique and the SELF data structure have been implemented in PostgreSQL 9.4 with PostGIS extension using PL/pgSQL to support dynamic lines. Extended experimental work has been carried out to better understand the impact of SED based simplification over conventional Douglas-Peucker algorithm to both synthetic and real trajectories. The efficiency of SELF structure in regard to semantic preservation has been tested at different levels of simplification.</p>

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A Survey on Big Data Processing Frameworks for Mobility Analytics

ACM SIGMOD Record ◽

10.1145/3484622.3484626 ◽

2021 ◽

Vol 50 (2) ◽

pp. 18-29

Author(s):

Christos Doulkeridis ◽

Akrivi Vlachou ◽

Nikos Pelekis ◽

Yannis Theodoridis

Keyword(s):

Big Data ◽

Data Processing ◽

Data Management ◽

Spatial Data ◽

State Of The Art ◽

Temporal Data ◽

Big Data Processing ◽

Mobility Data ◽

New Challenges ◽

Spatio Temporal

In the current era of big spatial data, the vast amount of produced mobility data (by sensors, GPS-equipped devices, surveillance networks, radars, etc.) poses new challenges related to mobility analytics. A cornerstone facilitator for performing mobility analytics at scale is the availability of big data processing frameworks and techniques tailored for spatial and spatio-temporal data. Motivated by this pressing need, in this paper, we provide a survey of big data processing frameworks for mobility analytics. Particular focus is put on the underlying techniques; indexing, partitioning, query processing are essential for enabling efficient and scalable data management. In this way, this report serves as a useful guide of state-of-the-art methods and modern techniques for scalable mobility data management and analytics.

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