scholarly journals Research on Spatio-temporal Data Construction of Library under the Background of Digital Humanities-Take the Northeast Anti-Japanese United Forces as an example

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Hong Ni ◽  
Baorui Liu
Keyword(s):  
Big Data ◽  
Data Structure ◽  
Cloud Service ◽  
Temporal Data ◽  
Construction Process ◽  
Knowledge Reconstruction ◽  
New Type ◽  
User Demand ◽  
Spatio Temporal ◽  
Cloud Service Platform

This paper from the perspective of multi-dimensional, relational, dynamic this data characteristics and knowledge reconstruction of library spatio-temporal data, Build a cloud service platform for spatio-temporal data of the library?based on the analysis of user demand then discussed its collection, processing, storage and the construction process of user service that provided with the spatio-temporal data. In the era of big data, spatio-temporal data, as a new type of resource, its construction and research enriched and developed traditional data structure relatively.

scholarly journals Trajectory Clustering and k-NN for Robust Privacy Preserving k-NN Query Processing in GeoSpark

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 182
Author(s):  
Elias Dritsas ◽  
Andreas Kanavos ◽  
Maria Trigka ◽  
Gerasimos Vonitsanos ◽  
Spyros Sioutas ◽  
...  
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Privacy Preservation ◽  
Nearest Neighbor ◽  
Data Representation ◽  
Privacy Preserving ◽  
Temporal Data ◽  
K Nearest Neighbor ◽  
Trajectory Data ◽  
Spatio Temporal

Privacy Preserving and Anonymity have gained significant concern from the big data perspective. We have the view that the forthcoming frameworks and theories will establish several solutions for privacy protection. The k-anonymity is considered a key solution that has been widely employed to prevent data re-identifcation and concerns us in the context of this work. Data modeling has also gained significant attention from the big data perspective. It is believed that the advancing distributed environments will provide users with several solutions for efficient spatio-temporal data management. GeoSpark will be utilized in the current work as it is a key solution that has been widely employed for spatial data. Specifically, it works on the top of Apache Spark, the main framework leveraged from the research community and organizations for big data transformation, processing and visualization. To this end, we focused on trajectory data representation so as to be applicable to the GeoSpark environment, and a GeoSpark-based approach is designed for the efficient management of real spatio-temporal data. Th next step is to gain deeper understanding of the data through the application of k nearest neighbor (k-NN) queries either using indexing methods or otherwise. The k-anonymity set computation, which is the main component for privacy preservation evaluation and the main issue of our previous works, is evaluated in the GeoSpark environment. More to the point, the focus here is on the time cost of k-anonymity set computation along with vulnerability measurement. The extracted results are presented into tables and figures for visual inspection.

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.

scholarly journals Semantically Enriched Simplification of Trajectories

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>

A Survey on Big Data Processing Frameworks for Mobility Analytics

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.

High-Level Languages for Geospatial Analysis of Big Data

2021 ◽  
pp. 62-81
Author(s):  
Symphorien Monsia ◽  
Sami Faiz
Keyword(s):  
Big Data ◽  
Query Language ◽  
Research Work ◽  
Temporal Data ◽  
Data Systems ◽  
High Level Language ◽  
Gis Tools ◽  
Spatio Temporal ◽  
Big Data Systems ◽  
High Level

In recent years, big data has become a major concern for many organizations. An essential component of big data is the spatio-temporal data dimension known as geospatial big data, which designates the application of big data issues to geographic data. One of the major aspects of the (geospatial) big data systems is the data query language (i.e., high-level language) that allows non-technical users to easily interact with these systems. In this chapter, the researchers explore high-level languages focusing in particular on the spatial extensions of Hadoop for geospatial big data queries. Their main objective is to examine three open source and popular implementations of SQL on Hadoop intended for the interrogation of geospatial big data: (1) Pigeon of SpatialHadoop, (2) QLSP of Hadoop-GIS, and (3) ESRI Hive of GIS Tools for Hadoop. Along the same line, the authors present their current research work toward the analysis of geospatial big data.

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