SPATIOTEMPORAL DATA MINING: ISSUES, TASKS AND APPLICATIONS (original) (raw)
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Spatiotemporal data mining: a survey on challenges and open problems
Artificial Intelligence Review
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.
QUERYING AND MINING SPATIOTEMPORAL
This paper presents an approach for mining spatiotemporal association rules. The proposed method is based on the computation of neighborhood relationships between geographic objects during a time interval. This kind of information is extracted from spatiotemporal database by the means of special mining queries enriched by time management parameters. The resulting spatiotemporal predicates are then processed by classical data mining tools in order to generate spatiotemporal association rules.
11.Challenging Issues of Spatio-Temporal Data Mining
The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining.
11.[55-63]Challenging Issues of Spatio-Temporal Data Mining
The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining.
A Survey of Spatial, Temporal and Spatio-Temporal Data Mining
Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mining system to deal with very large spatio-temporal data sets. With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry.
IJERT-Data Detection Framework for Spatio-Temporal Data Mining
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/data-detection-framework-for-spatio-temporal-data-mining https://www.ijert.org/research/data-detection-framework-for-spatio-temporal-data-mining-IJERTV9IS100305.pdf With the unpredictable increase in the generation and use of spatiotemporal data sets, the efficient handling of large volumes of spatiotemporal sets has been the subject of many research efforts. With the phenomenal growth of computer technology everywhere, mining from the enormous amount of spatiotemporal data sets is seen as a central technology that can provide information for real-world applications. Big spatial data apps cover a wide variety of interests, including infectious disease monitoring, simulation of climate change, opioid addiction, and others. As a result, significant research efforts are carried out within these applications to facilitate efficient analysis and intelligence by either offering spatial extensions to existing machine learning solutions or designing new solutions from scratch. Based on the concepts of states and events, the conceptual model was developed and the use of time as a basis for organising spatial data allowed the time and place of any modifications to be recorded. This paper proposes a conceptual-level spatio-temporal modelling approach, called MADS. The idea results from the description of the conditions for a conceptual model to be fulfilled. We can easily formalize spatiotemporal data mining challenges using the proposed knowledge discovery system.
Querying and Mining Spatiotemporal Association Rules
2011
This paper presents an approach for mining spatiotemporal association rules. The proposed method is based on the computation of neighborhood relationships between geographic objects during a time interval. This kind of information is extracted from spatiotemporal database by the means of special mining queries enriched by time management parameters. The resulting spatiotemporal predicates are then processed by classical data mining tools in order to generate spatiotemporal association rules.
International Journal of Knowledge Engineering and Data Mining, 2015
Mining frequent itemsets from large spatiotemporal databases is a hard task due to the existence of hidden spatiotemporal relationships. The aim of our proposal is to look for spatiotemporal association rules that relate properties of reference objects like large towns with properties of other spatial task-relevant objects. The extracted patterns are different relationships relating the spatial objects during time periods. We propose a four-step approach; the core step is to extract the spatiotemporal relationships. The second and third steps are devoted respectively to frequent spatiotemporal itemsets generation and spatiotemporal association rules extraction. The fourth step is the refinement of the extracted rules. To prove the applicability of our method, we conduct experimentation on a spatiotemporal database describing the city of Tunis in the dates 1987 and 2001. On the basis of our proposed measures namely the spatial closeness relevance and the time subsequence, only interesting spatiotemporal association rules are retained.
Mining spatiotemporal associations using queries
In this paper, we present our approach for mining spatiotemporal knowledge. The proposed method is based on the computation of neighborhood relationships between geographical objects during a time interval. This kind of information is nonexplicitly stored in spatio-temporal database and is extracted by the means of special mining queries enriched by time management parameters. The general aim of our approach is to develop a method that utilizes the inherent structure of spatiotemporal information as well as its rich semantics to derive spatio-temporal association rules in order to improve the decision making process about land changes and resulting prohibited risks.