scholarly journals MongoDB Vs PostgreSQL: A comparative study on performance aspects

2020 ◽  
Author(s):  
Antonios Makris ◽  
Konstantinos Tserpes ◽  
Giannis Spiliopoulos ◽  
Dimitrios Zissis ◽  
Dimosthenis Anagnostopoulos
Keyword(s):  
Data Storage ◽  
Response Times ◽  
Storage Systems ◽  
Industrial Applications ◽  
Temporal Data ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Almost All ◽  
Application Requirements ◽  
Polygon Intersection

Abstract Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.

scholarly journals Efficient Data Diffusion and Elimination Control Method for Spatio-Temporal Data Retention System

2021 ◽  
Author(s):  
Shumpei YAMASAKI ◽  
Daiki NOBAYASHI ◽  
Kazuya TSUKAMOTO ◽  
Takeshi IKENAGA ◽  
Myung J. LEE
Keyword(s):  
Control Method ◽  
Temporal Data ◽  
Data Retention ◽  
Retention System ◽  
Efficient Data ◽  
Spatio Temporal

scholarly journals Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data

2020 ◽  
Vol 12 (5) ◽  
pp. 78 ◽  
Author(s):  
Sedick Baker Effendi ◽  
Brink van der Merwe ◽  
Wolf-Tilo Balke
Keyword(s):  
Data Analysis ◽  
Real World ◽  
Spatial Data ◽  
Response Times ◽  
Graph Database ◽  
Temporal Data ◽  
Extensive Evaluation ◽  
Database Technology ◽  
Spatio Temporal ◽  
Real World Datasets

Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this data dramatically increases the need for intelligent backend systems to provide realtime query response times while scaling well (in terms of storage and performance) with increasing quantities of structured or semi-structured, multi-dimensional data. Currently, relational database solutions with spatial extensions such as PostGIS, seem to come to their limits. However, the use of graph database technology has been rising in popularity and has been found to handle graph-like spatio-temporal data much more effectively. Motivated by the need to effectively store multi-dimensional, interconnected data, this paper investigates whether or not graph database technology is better suited when compared to the extended relational approach. Three database technologies will be investigated using real world datasets namely: PostgreSQL, JanusGraph, and TigerGraph. The datasets used are the Yelp challenge dataset and an ambulance response simulation dataset, thus combining real world spatial data with realistic simulations offering more control over the dataset. Our extensive evaluation is based on how each database performs under practical data analysis scenarios similar to those found on enterprise level.

scholarly journals A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

2021 ◽  
Vol 6 (1) ◽  
pp. 63-85
Author(s):  
Haitao Yuan ◽  
Guoliang Li
Keyword(s):  
Data Storage ◽  
Data Preprocessing ◽  
Intelligent Transportation ◽  
Mobile Internet ◽  
Traffic Prediction ◽  
Temporal Data ◽  
Traffic Light ◽  
Challenges And Opportunities ◽  
Spatio Temporal ◽  
Prediction Problems

AbstractIntelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

A Service-Oriented Spatio-Temporal Data Model of the Marine Environment

2014 ◽  
Vol 513-517 ◽  
pp. 4543-4546
Author(s):  
Zhao Li ◽  
Yong Xin Feng
Keyword(s):  
Marine Environment ◽  
Data Model ◽  
Data Organization ◽  
Temporal Data ◽  
Geography Markup Language ◽  
Organization And Management ◽  
Time Space ◽  
Efficient Data ◽  
Service Oriented ◽  
Spatio Temporal

On the basis of analyzing of the marine environment spatio-temporal data characteristics and the existing data model, proposes a conceptual model for marine environment data based on the feature and field to achieve integration of description and expression of feature, field, time, space and semantic domains. Designs and implements the service-oriented marine environment data organization and storage using Geography Markup Language (GML). Efficient data organization and management is crucial to marine environment visual services.

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

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