SPARQL-ST: Extending SPARQL to Support Spatiotemporal Queries

2011 ◽  
pp. 61-86 ◽  
Author(s):  
Matthew Perry ◽  
Prateek Jain ◽  
Amit P. Sheth
Keyword(s):  
Spatiotemporal Queries

Spatiotemporal Object Database Approach to Dynamic Segmentation

2003 ◽  
Vol 1836 (1) ◽  
pp. 118-125 ◽  
Author(s):  
Bo Huang ◽  
Li Yao
Keyword(s):  
Query Language ◽  
Time Varying ◽  
Data Types ◽  
The Road ◽  
Dynamic Segmentation ◽  
Management Group ◽  
Object Database ◽  
On The Road ◽  
Temporal Dimensions ◽  
Spatiotemporal Queries

Dynamic segmentation is viewed as one of the most important functions of geographic information systems for transportation applications. Although the road network and associated events (e.g., pavement material, traffic volume, incidents) can be referenced to both space and time, the spatial and temporal dimensions have not been well integrated. Modeling space-varying, time-varying, and space-time-varying events in dynamic segmentation by using an object database approach that is in line with the Object Database Management Group standard is explored. A mechanism called parametric polymorphism is used to lift conventional data types to spatial, temporal, and spatiotemporal types for maintaining knowledge about events that could change spatially, temporally, and spatiotemporally along linear features. An associated object query language, DS-OQL, was designed to support the formulation of spatial, temporal, and spatiotemporal queries on the road and event information.

Spatiotemporal Data Model and Query Language for Tracking Land Use Change

2005 ◽  
Vol 1902 (1) ◽  
pp. 107-113 ◽  
Author(s):  
Bo Huang ◽  
Christophe Claramunt
Keyword(s):  
Land Use ◽  
Data Model ◽  
Query Language ◽  
Spatiotemporal Data ◽  
Spatiotemporal Information ◽  
Management Group ◽  
Object Databases ◽  
Temporal Dimensions ◽  
Spatiotemporal Queries ◽  
Spatiotemporal Data Model

Management of spatiotemporal information requires a more generic and consolidated data model to facilitate applications such as tracking land use parcel changes. This paper presents such a spatiotemporal data model in the context of object databases by extending the Object Data Management Group (ODMG) standard and examines its feasibility in a land use application. This model extends the ODMG object model with a parameterized type, TimeSeries<T>, which allows the shifting of spatial types into spatiotemporal types to support the representation of a series of states (i.e., the history) of an object. An object query language (OQL), spatiotemporal OQL (STOQL), which adds spatial and temporal dimensions to ODMG's OQL, is also designed. A case study demonstrates that STOQL supports the formulation of various spatiotemporal queries pertaining to historical states of spatial objects as well as spatial changes, including spatial type substitution. The model and query language have been implemented by using an object-oriented language in a geographic information system environment.

Spatiotemporal Queries on Road Networks, Coding Based Methods

2017 ◽  
pp. 2168-2173
Author(s):  
Sandeep Gupta ◽  
Chinya V. Ravishankar
Keyword(s):  
Road Networks ◽  
Spatiotemporal Queries

Incorporating Spatial Queries into Semantic Sensor Streams on the Internet of Things

2017 ◽  
Vol 28 (4) ◽  
pp. 24-39 ◽  
Author(s):  
Sungkwang Eom ◽  
Kyong-Ho Lee
Keyword(s):  
Internet Of Things ◽  
Query Language ◽  
Vital Role ◽  
The Internet ◽  
Observation Data ◽  
Temporal Properties ◽  
Efficient Management ◽  
Tremendous Amount ◽  
Spatiotemporal Queries ◽  
The Internet Of Things

In the Internet of Things (IoT) environment, the use of sensors and sensor readings is significant in research and industry. The number of sensors is increasing exponentially, adding a tremendous amount of data to the Web. Therefore, the efficient management of sensors and observation data is becoming important. Especially, the location and time of observations are expected to play a vital role in IoT. However, existing researches mainly focus on the temporal properties of data stream. It is necessary to consider the spatial features in addition to the temporal ones. In this article, the authors propose a spatiotemporal query language which integrates spatial and temporal features. Also, they propose an efficient method of building a spatiotemporal index and processing the proposed query language. To evaluate the proposed method, the authors conduct experiments through implementation. The experimental results show that the proposed method deals with spatiotemporal queries within a reasonable time.

scholarly journals SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Sávio S. T. De Oliveira ◽  
Vagner J. S. Rodrigues ◽  
Wellington S. Martins
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Big Data Analytics ◽  
Spatiotemporal Data ◽  
Temporal Data ◽  
Query Engine ◽  
Clear Winner ◽  
Spatio Temporal ◽  
Spatiotemporal Queries ◽  
Smart Machine

Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

Incorporating Spatial Queries into Semantic Sensor Streams on the Internet of Things

2020 ◽  
pp. 1703-1719
Author(s):  
Sungkwang Eom ◽  
Kyong-Ho Lee
Keyword(s):  
Internet Of Things ◽  
Query Language ◽  
Vital Role ◽  
The Internet ◽  
Observation Data ◽  
Temporal Properties ◽  
Temporal Features ◽  
Tremendous Amount ◽  
Spatiotemporal Queries ◽  
The Internet Of Things

In the Internet of Things (IoT) environment, the use of sensors and sensor readings is significant in research and industry. The number of sensors is increasing exponentially, adding a tremendous amount of data to the Web. Therefore, the efficient management of sensors and observation data is becoming important. Especially, the location and time of observations are expected to play a vital role in IoT. However, existing researches mainly focus on the temporal properties of data stream. It is necessary to consider the spatial features in addition to the temporal ones. In this article, the authors propose a spatiotemporal query language which integrates spatial and temporal features. Also, they propose an efficient method of building a spatiotemporal index and processing the proposed query language. To evaluate the proposed method, the authors conduct experiments through implementation. The experimental results show that the proposed method deals with spatiotemporal queries within a reasonable time.

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