• Journal of Korea Spatial Information System Society
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• v.5 no.2 s.10
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• pp.5-21
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• 2003

Sptaio-temporal databases are important to store the real-time location information of large spatio-temporal objects efficiently and retrieve them rapidly. Accordingly necessity for spatio-temporal database system that can manage spatial information, aspatial information and temporal information of spatio-temporal objects is increasing. Sptaio-temporal databases are important to store the real-time location information of large spatio-temporal objects efficiently and retrieve them rapidly. Accordingly necessity for spatio-temporal database system that can manage spatial information, aspatial information and temporal information of spatio-temporal objects is increasing. Therefore, in this paper, we propose a spatio-temporal data model that is able to efficiently manage historical spatio-temporal objects that change dynamically their states as time. Also, various spatio-temporal operations and constraint conditions are defined to keep integrity of spatio-temporal data and spatio-temporal operations.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.43-50
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• 2005

The Processing domains of spatio-temporal databases are divided into time-series databases for moving objects and sequence databases for discrete historical objects. Recently the selectivity estimation techniques for query optimization in spatio-temporal databases have been studied, but focused on query optimization in time-series databases. There wat no previous work on the selectivity estimation techniques for sequence databates as well. Therefore, we construct T-Minskew histogram for query optimization In sequence databases and propose a selectivity estimation method using the T-Minskew histogram. Furthermore we propose an effective histogram maintenance technique for food performance of the histogram.

• The Journal of Korean Association of Computer Education
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• v.10 no.2
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• pp.101-113
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• 2007

In this paper, we present an object-based modeling and language for an object-oriented spatio-temporal database system. For handling the structure of spatio-temporal objects and the spatio-temporal operators, we propose the two layers of data modeling: a spatio-temporal object model (STOM) and an spatio_temporal internal description model (STIM). We then propose STOQL, a spatio-temporal object-oriented query language. STOQL provides an integrated mechanism for the graphical display of spatial objects and the retrieval of spatio-temporal and aspatial objects.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.252-260
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• 2004

Recently, the TPR-tree has been proposed to support spatio-temporal queries for moving objects. Subsequently, various methods using the TPR-tree have been intensively studied. However, although the TPR-tree is one of the most popular access methods in spatio-temporal databases, any cost model for the TPR-tree has not yet been proposed. Existing cost models for the spatial index such as the R-tree do not accurately ostinato the number of disk accesses for spatio-temporal queries using the TPR-tree, because they do not consider the future locations of moving objects. In this paper, we propose a cost model of the TPR-tree for moving objects for the first time. Extensive experimental results show that our proposed method accurately estimates the number of disk accesses over various spatio-temporal queries.

• Journal of KIISE:Databases
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• v.34 no.1
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• pp.84-97
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• 2007

Selectivity estimation techniques in query optimization have been used in commercial databases and histograms are popularly used for the selectivity estimation. Recently, the techniques for spatio-temporal databases have been restricted to existing temporal and spatial databases. In addition, the selectivity estimation techniques focused on time-series data such as moving objects. It is also impossible to estimate selectivity for range queries with a time interval. Therefore, we construct two histograms, CMH (current multidimensional histogram) and PMH (past multidimensional histogram), to estimate the selectivity of multidimensional sequence data in spatio-temporal databases and propose effective selectivity estimation methods using the histograms. Furthermore, we solve a problem about the range query using our proposed histograms. We evaluated the effectiveness of histograms for range queries with a time interval through various experimental results.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.214-223
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• 2006

Recently there is a need to store and process enormous spatial data in spatio-temporal databases. For effective query processing in spatio-temporal databases, selectivity estimation in query optimization techniques, which approximate query results when the precise answer is not necessary or early feedback is helpful, has been studied. There have been selectivity estimation techniques such as sampling-based techniques, histogram-based techniques, and wavelet-based techniques. However, existing techniques in spatio-temporal databases focused on selectivity estimation for future extent of moving objects. In this paper, we construct a new histogram, named T-Minskew, for query optimization of past spatio-temporal data. We also propose an effective selectivity estimation method using T-Minskew histogram and effective histogram maintenance technique to prevent frequent histogram reconstruction using threshold.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.39-47
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• 2004

Spatio-temporal databases have been mostly studied in the area of access methods. However, without considering an extraordinary update maintenance overhead after building up a spatio-temporal index, most indexing techniques have focused on fast query processing only. In this paper, we propose an efficient update management method that reduces the number of disk accesses required in order to apply the updates of moving objects to a spatio-temporal index. We consider realistic update patterns that can represent the movements of objects properly. We present a memory based structure that can efficiently maintain a small number of very frequently updating objects. For an experimental environment with realistic update patterns, the number of disk accesses of our method is about 40% lower than that of a general update method of existing spatio-temporal indexes.

• Journal of the Korean Geographical Society
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• v.37 no.2
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• pp.191-202
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• 2002

Characteristics and patterns of geographic features and human activities can be interpreted in terms of spatiality and temporality. The necessity to record the historical changes and the ability to reason in the real world has lead to a new field of research so called Integrated Spatio-Temporal analysis. The objective of this study is to investigate temporal maps for Spatio-temporal analysis, which have the integration functionality for visualizing spatiality and temporality of the geographic appearances and human activities. Land information is composed of spatial, attribute and temporal data and requires spatio-temporal representations. It is possible to visualize spatio-temporal variations with spatio-temporal databases and temporal map produced by integrated data models. This study constructs spatio-temporal model for temporal maps of land price variation analysis. Taking advantage of the spatio-temporal model proposed here, it is possible to visualize spatio-temporal variations with spatio-temporal database and temporal map. On a practical level, this study would be extended and utilized to various geographic features.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.778-784
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• 2005

Miniaturization of computing devices, and advances in wireless communication and positioning systems will create a wide and increasing range of database applications such as location-based services, tracking and transportation systems that has to deal with Moving Objects. Various types of queries could be posted to moving objects, including past, present and future queries. The key problem is how to model the location of moving objects and enable Database Management System (DBMS) to predict the future location of a moving object. It is obvious that there is a need for an innovative, generic, conceptually clean and application-independent approach for spatio-temporal handling data. This paper presents behavioral aspect of the spatio-temporal databases for managing and querying moving objects. Our objective is to impelement and extend the Spatial TAU (STAU) system developed by Dr.Pelekis that provides spatio-temporal functionality to an Object-Relational Database Management System to support modeling and querying moving objecs. The results of the impelementation are demonstrated in this paper.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.53.2-53.2
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• 2016

We present our experiences with open-source spatio-temporal database systems for managing and analyzing big astronomical data acquired by wide-field time-domain sky surveys. Considering performance, cost, difficulty, and scalability of the database systems, we conduct comparison studies of open-source spatio-temporal databases such as GeoMesa and PostGIS that are already being used for handling big geographical data. Our experiments include ingesting, indexing, and querying millions or billions of astronomical spatio-temporal data. We choose the public VVV (VISTA Variables in the Via Lactea) catalogs of billions measurements for hundreds of millions objects as the test data. We discuss issues of how these spatio-temporal database systems can be adopted in the astronomy community.