scholarly journals Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

2021 ◽  
Vol 71 ◽  
Author(s):  
John Grant ◽  
Maria Vanina Martinez ◽  
Cristian Molinaro ◽  
Francesco Parisi
Keyword(s):  
Real World ◽  
Knowledge Bases ◽  
Propositional Knowledge ◽  
Location Based Services ◽  
Temporal Databases ◽  
Temporal Data ◽  
Time Space ◽  
Spatio Temporal ◽  
Temporal Dimensions ◽  
Natural Measures

The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting. In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define “dimension-aware” counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.

scholarly journals A Survey on Spatio-temporal Data Analytics Systems

2022 ◽  
Author(s):  
Md Mahbub Alam ◽  
Luis Torgo ◽  
Albert Bifet
Keyword(s):  
Programming Languages ◽  
Spatial Data ◽  
Data Analytics ◽  
Spatial Databases ◽  
Location Based Services ◽  
Temporal Data ◽  
Wide Range ◽  
Data Volume ◽  
Spatio Temporal ◽  
Gis Software

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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.

Mining in Spatio-Temporal Databases

2011 ◽  
pp. 272-293
Author(s):  
Junmei Wang ◽  
Wynne Hsu ◽  
Mong Li Lee
Keyword(s):  
Spatial Databases ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Temporal Databases ◽  
Temporal Data ◽  
Service Patterns ◽  
Sequence Patterns ◽  
Spatial Coordinates ◽  
Spatio Temporal ◽  
Work Done

Recent interest in spatio-temporal applications has been fueled by the need to discover and predict complex patterns that occur when we observe the behavior of objects in the three-dimensional space of time and spatial coordinates. Although the complex and intrinsic relationships among the spatio-temporal data limit the usefulness of conventional data mining techniques to discover the patterns in the spatio-temporal databases, they also lead to opportunities for mining new classes of patterns in spatio-temporal databases. This chapter provides a survey of the work done for mining patterns in spatial databases and temporal databases, and the preliminary work for mining patterns in spatio-temporal databases. We highlight the unique challenges of mining interesting patterns in spatio-temporal databases. We also describe two special types of spatio-temporal patterns: location-sensitive sequence patterns and geographical features for location-based service patterns.

Building a Visual Analytics Tool for Location-Based Services

2016 ◽  
pp. 620-642 ◽  
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

Toward a Visual Query System for Spatio-Temporal Databases

2009 ◽  
pp. 987-1002
Author(s):  
Valéria M.B. Cavalcanti ◽  
Ulrich Schiel ◽  
Claudio de Souza Baptista
Keyword(s):  
Query Languages ◽  
Temporal Databases ◽  
Temporal Database ◽  
Database Queries ◽  
Integrated Environment ◽  
Visual Query ◽  
Query System ◽  
Spatio Temporal ◽  
Temporal Dimensions ◽  
Query Systems

Visual query systems (VQS) for spatio-temporal databases, which enable formulation of queries involving both spatial and temporal dimensions, are an important research subject. Existing results treat these dimensions separately and there are only a few integrated proposals. This chapter presents a VQS called spatio-temporal visual query environment (S-TVQE), which allows the formulation of conventional, spatial, temporal, and spatio-temporal database queries in an integrated environment. With S-TVQE, the user, instead of querying the database by textual query languages will interact with the system by visual operators for the statement of the query conditions. The tool provides a visualization of the results in different formats such as maps, graphics, and tables.

scholarly journals Merging diaries and GPS records: The method of data collection for spatio-temporal research

2015 ◽  
Vol 23 (2) ◽  
pp. 12-25
Author(s):  
Martin Šveda ◽  
Michala Madajová
Keyword(s):  
Data Collection ◽  
Traditional Method ◽  
Temporal Data ◽  
Proof Of Concept ◽  
Data Frame ◽  
Gps Technology ◽  
Time Space ◽  
Spatio Temporal ◽  
Transportation Research ◽  
Space Data

Abstract The results of a ‘proof-of-concept’ study that examined a new opportunity for using GPS technology in activity surveys are presented in this article. The aim is to demonstrate the method of collection and processing of individual time-space data via the dual records of a time-space diary and the GPS locator. The GPS technology here is not treated as a substitute for the traditional method of diaries; rather, the paper concentrates on the potential existing in a combination of these two techniques. The time-geographical approach and the corresponding methodology are used in order to assess the complexities of an individual’s everyday life, and to capture the spectrum of human activities in a data frame applicable to different analyses in behavioural, social and transportation research. This method not only improves the quality and robustness of spatio-temporal data, but also reduces under-reporting and the burdens on the respondents.

scholarly journals MAP-Vis: A Distributed Spatio-Temporal Big Data Visualization Framework Based on a Multi-Dimensional Aggregation Pyramid Model

2020 ◽  
Vol 10 (2) ◽  
pp. 598
Author(s):  
Xuefeng Guan ◽  
Chong Xie ◽  
Linxu Han ◽  
Yumei Zeng ◽  
Dannan Shen ◽  
...  
Keyword(s):  
High Performance ◽  
Visual Analysis ◽  
Interactive Visualization ◽  
Temporal Data ◽  
Time Space ◽  
Big Data Visualization ◽  
Spatio Temporal ◽  
And Performance ◽  
Open Datasets ◽  
Map Data

During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high rendering delay, and poor visual effects. Research has shown that the development of distributed computing frameworks provides a feasible solution for big spatio-temporal data management and visualization. Accordingly, to address these challenges, this paper adopts a proprietary pre-processing visualization scheme and designs and implements a highly scalable distributed visual analysis framework, especially targeted at massive point-type datasets. Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid. The proposed MAP model can support the simultaneous hierarchical aggregation of time, space, and attributes, and also later transformation of the derived aggregates into discrete key-value pairs for scalable storage and efficient retrieval. Using the generated MAP datasets, we develop an open-source distributed visualization framework (MAP-Vis). In MAP-Vis, a high-performance Spark cluster is used as a parallel preprocessing platform, while distributed HBase is used as the massive storage for the generated MAP data. The client of MAP-Vis provides a variety of correlated visualization views, including heat map, time series, and attribute histogram. Four open datasets, with record numbers ranging from the millions to the tens of billions, are chosen for system demonstration and performance evaluation. The experimental results demonstrate that MAP-Vis can achieve millisecond-level query response and support efficient interactive visualization under different queries on the space, time, and attribute dimensions.

Towards a Visual Query System for Spatio-Temporal Databases

2011 ◽  
pp. 158-173
Author(s):  
Valéria M.B. Cavalcanti
Keyword(s):  
Query Languages ◽  
Temporal Databases ◽  
Temporal Database ◽  
Database Queries ◽  
Integrated Environment ◽  
Visual Query ◽  
Query System ◽  
Spatio Temporal ◽  
Temporal Dimensions ◽  
Query Systems

Visual Query Systems (VQS) for Spatio-Temporal Databases; which enable formulation of queries involving both spatial and temporal dimensions; are an important research subject. Existing results treat these dimensions separately and there are only a few integrated proposals. This chapter presents a VQS; called Spatio-Temporal Visual Query Environment (S-TVQE) which allows the formulation of conventional; spatial; temporal; and spatio-temporal database queries in an integrated environment. With S-TVQE the user; instead of querying the database by textual query languages will interact with the system by visual operators for the statement of the query conditions. The tool provides a visualization of the results in different formats such as maps; graphics; and tables.

Building a Visual Analytics Tool for Location-Based Services

Big Data ◽  
2016 ◽  
pp. 615-637
Author(s):  
Erdem Kaya ◽  
Mustafa Tolga Eren ◽  
Candemir Doger ◽  
Selim Saffet Balcisoy
Keyword(s):  
Visual Analytics ◽  
3D Visualization ◽  
Location Based Services ◽  
Task Completion ◽  
Temporal Data ◽  
Density Map ◽  
Spatio Temporal ◽  
Visualization Techniques ◽  
Analytical Tools ◽  
2D And 3D

Conventional visualization techniques and tools may need to be modified and tailored for analysis purposes when the data is spatio-temporal. However, there could be a number of pitfalls for the design of such analysis tools that completely rely on the well-known techniques with well-known limitations possibly due to the multidimensionality of spatio-temporal data. In this chapter, an experimental study to empirically testify whether widely accepted advantages and limitations of 2D and 3D representations are valid for the spatio-temporal data visualization is presented. The authors implemented two simple representations, namely density map and density cube, and conducted a laboratory experiment to compare these techniques from task completion time and correctness perspectives. Results of the experiment revealed that the validity of the generally accepted properties of 2D and 3D visualization needs to be reconsidered when designing analytical tools to analyze spatio-temporal data.

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