scholarly journals Spatio-Temporal Range Search using K-DSTH Indexing Structure

2019 ◽  
Vol 9 (1) ◽  
pp. 3791-3797
Keyword(s):  
Social Networking Sites ◽  
Research Work ◽  
Hash Table ◽  
Temporal Data ◽  
Indexing Structure ◽  
Efficient Storage ◽  
Photo Sharing ◽  
Spatio Temporal ◽  
Temporal Objects ◽  
Storage Structures

A large amount of data which includes spatial and temporal information related to different fields like geography, satellite, medical or multimedia is generated and collected at an extraordinary scale. Such data is produced by satellites, mobile devices, emerging applications like social networking sites, photo sharing sites and many more. As the whole world is aware of the importance of such spatio-temporal data, a great amount of research work is evolving around efficient storage structures and algorithms to handle a variety of spatio-temporal queries. In this paper, the authors are introducing a novel spatio-temporal indexing structure k-dStH which is an extension of k-dSTHash indexing structure. It uses master hash table, local hash table and B-tree additionally which are based on timestamp values. The researchers also introduce an algorithm k-dStHSpaTempRangeSrch based on the proposed indexing structure to find spatio-temporal objects in given spatial range at particular temporal value. The performance analysis shows that the algorithm proposed by the authors is far more efficient as compared to brute force technique of searching for the spatio-temporal objects.

scholarly journals APPLICATION OF 3D SPATIO-TEMPORAL DATA MODELING, MANAGEMENT, AND ANALYSIS IN DB4GEO

2016 ◽  
Vol IV-2/W1 ◽  
pp. 163-170
Author(s):  
P. V. Kuper ◽  
M. Breunig ◽  
M. Al-Doori ◽  
A. Thomsen
Keyword(s):  
Water Supply ◽  
United Arab Emirates ◽  
Transport Systems ◽  
Reservoir Modeling ◽  
Future Research ◽  
Temporal Data ◽  
Temporal Dimension ◽  
Geospatial Database ◽  
Spatio Temporal ◽  
Temporal Objects

Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

scholarly journals RESEARCH ON CONSTRUCTION METHOD OF SPATIO-TEMPORAL DATA MODEL BASED ON BEHAVIOR-EVENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 591-594
Author(s):  
J. W. Li ◽  
Y. Ma ◽  
J. W. Jiang ◽  
W. D. Chen ◽  
N. Yu ◽  
...  
Keyword(s):  
Data Model ◽  
Logical Structure ◽  
Physical Structure ◽  
Temporal Data ◽  
Model Based ◽  
Time Space ◽  
Before And After ◽  
Spatio Temporal ◽  
Temporal Objects ◽  
Organic Combination

Abstract. Starting from the object-oriented idea, this paper analyses the existing event-based models and the logical relationship between behavioral cognition and events, and discusses the continuity of behavioral cognition on the time axis from the perspective of temporal and spatial cognition. A geospatial data model based on behavioral-event is proposed. The physical structure and logical structure of the model are mainly designed, and the four-dimensional model of “time, space, attribute and event” is constructed on the axis. The organic combination of the four models can well describe the internal mechanism and rules of geographical objects. The expression of data model based on behavior-event not only elaborates the basic information of geospatial objects, but also records the changes of related events caused by the changes of geographic Entities' behavior, and expresses the relationship between spatial and temporal objects before and after the changes of behavior cognition. This paper also designs an effective method to organize spatio-temporal data, so as to realize the effective management and analysis of spatio-temporal data and meet the requirements of storage, processing and mining of large spatio-temporal data.

3SST Model

2010 ◽  
pp. 1-14
Author(s):  
Andreea Sabau
Keyword(s):  
Data Model ◽  
Conceptual Modeling ◽  
Domain Model ◽  
Relational Model ◽  
Temporal Data ◽  
Spatio Temporal ◽  
Entity Relationship ◽  
Temporal Objects ◽  
Database Platform ◽  
Event Based

In order to represent spatio-temporal data, many conceptual models have been designed and a part of them have been implemented. This chapter describes an approach of the conceptual modeling of spatio-temporal data, called 3SST. Also, the spatio-temporal conceptual and relational data models obtained by following the proposed phases are presented. The 3SST data model is obtained by following three steps: the construction of an entity-relationship spatio-temporal model, the specification of the domain model and the design of a class diagram which includes the objects characteristic to a spatiotemporal application and other needed elements. The relational model of the 3SST conceptual model is the implementation of the conceptual 3SST data model on a relational database platform. Both models are characterized by generality in representing spatial, temporal and spatio-temporal data. The spatial objects can be represented as points or objects with shape and the evolution of the spatio-temporal objects can be implemented as discrete or continuous in time, on time instants or time intervals. More than that, different types of spatial, temporal, spatio-temporal and event-based queries can be performed on represented data. Therefore, the proposed 3SST relational model can be considered the core of a spatio-temporal data model.

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

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.

scholarly journals A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

2021 ◽  
Author(s):  
Shengnan Ke ◽  
Jun Gong ◽  
Songnian Li ◽  
Qing Zhu ◽  
Xintao Liu ◽  
...  
Keyword(s):  
Hash Table ◽  
Index Structure ◽  
Hybrid Index ◽  
Temporal Data ◽  
Trajectory Data ◽  
Generation Efficiency ◽  
Tree Structures ◽  
Data Indexing ◽  
Indexing Method ◽  
Spatio Temporal

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.

scholarly journals Managing Sparse Spatio-Temporal Data in SAVIME: an Evaluation of the Ph-tree Index

2021 ◽  
Author(s):  
Stiw Herrera ◽  
Larissa Miguez da Silva ◽  
Paulo Ricardo Reis ◽  
Anderson Silva ◽  
Fabio Porto
Keyword(s):  
Sparse Data ◽  
Scientific Data ◽  
Efficient Implementation ◽  
Temporal Data ◽  
Indexing Structure ◽  
Tree Index ◽  
Spatio Temporal ◽  
Data Ingestion ◽  
Memory Indexing ◽  
Array Databases

Scientific data is mainly multidimensional in its nature, presenting interesting opportunities for optimizations when managed by array databases. However, in scenarios where data is sparse, an efficient implementation is still required. In this paper, we investigate the adoption of the Ph-tree as an in-memory indexing structure for sparse data. We compare the performance in data ingestion and in both range and punctual queries, using SAVIME as the multidimensional array DBMS. Our experiments, using a real weather dataset, highlights the challenges involving providing a fast data ingestion, as proposed by SAVIME, and at the same time efficiently answering multidimensional queries on sparse data.

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