A taxonomy for moving object queries in spatial databases

Alamri, Sultan; Taniar, David; Safar, Maytham

doi:10.1016/j.future.2014.02.007

Cited by 37 publications

(29 citation statements)

References 35 publications

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“…Some buildings (such as airports and shopping centers) contain wings/sections that determine the success or otherwise of the positioning queries. From observation, in some buildings, the positioning of mobile objects can be based on their section [13]. For example, "Where is object O 9 ?"…”

Section: Multidimensional Index: Gmi-treesmentioning

confidence: 99%

“…For example, the Beijing subway is a rapid transit rail networks with 227 stations and passengers numbering in excess of 7 million daily. Consequently, the positioning and monitoring of mobile objects indoors on different floors have become an essential research field with many applications for the location of mobile objects, indoor multifloor wayfinding, and security [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Vertical Indexing for Moving Objects in Multifloor Environments

Alamri

Taniar

Nguyen

2018

Mobile Information Systems

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e indexing and tracking of objects moving in indoor spaces has increasingly become an important area of research, which presents a fundamentally different challenge. ere are two main reasons for why indoor should be treated as cellular space. Firstly, an indoor space has entities, such as rooms and walls, that constrain the movement of the moving objects. Secondly, the relevant notion of locations of an object is cell based rather than an exact Euclidean coordinate. As a solution, in our earlier works, we proposed a cell-based indexing structure, called the C-tree, for indexing objects moving in indoor space. In this paper, we extend the C-tree to solve another interesting problem. It can be observed that many indoor spaces (such as shopping centers) contain wings/sections. For such a space, there are queries for which the wing/section location of an object, rather than the cellular location, is the relevant answer (e.g., "the object is in the east wing"). In this paper, we propose a new index structure, called the GMI-tree ("GMI" stands for "Graph-based Multidimensional Index"). e GMI-tree is based on two notions of distance, or equivalently, two notions of adjacency: one represents horizontal adjacency and the other represents vertical adjacency.

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Section: Multidimensional Index: Gmi-treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vertical Indexing for Moving Objects in Multifloor Environments

Alamri

Taniar

Nguyen

2018

Mobile Information Systems

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“…Las consultas en el espacio euclidiano o en la red espacial de caminos tienen en cuenta los movimientos de los objetos entre pares de coordenadas, mientras que las consultas en las celdas espaciales se basan en notaciones de celdas, por ejemplo: ¿Cuáles son los objetos que se mueven en la celda C1? (Alamri, Taniar y Safar, 2014). De forma que, en un espacio geométrico, tanto la posición actual como las posiciones futuras son representadas como n-tuplas coordenadas, en forma de puntos, áreas y volúmenes.…”

Section: Taxonomíaunclassified

“…Mientras Indoor-tree registra movimientos dentro de las celdas espaciales (Alamri, Taniar y Safar, 2014). Otras propuestas de estructuras e índice incluyen la capacidad de procesar datos de objetos en movimientos considerando objetos vecinos también en movimiento como el M2TPR-tree (Chunming, 2013).…”

Section: Como El íNdice I+-mon-tree Yel Indice Statistics and Trace-bunclassified

Taxonomía de las bases de datos espaciotemporales con objetos en movimiento

Barbosa¹

2018

tecnura

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Contexto: En la última década, las bases de datos han evolucionado tanto que ya no solo se habla de bases de datos espaciales, sino también de bases de datos espacio-temporales. Esto quiere decir que el evento o registro cuenta con una variable espacial o de localización, y con una variable de temporalidad, que permite la actualización del registro almacenado anteriormente.Método: En este trabajo se presenta una revisión bibliográfica sobre conceptos, modelos de datos espacio-temporales, específicamente los modelos de datos de objetos en movimiento.Resultados: Se presentan consideraciones taxonómicas de las consultas en los modelos de datos de objetos en movimiento, de acuerdo a la perspectiva de la consulta (tiempo, localización, movimiento, objeto y patrones), así como también las diferentes propuestas de índices y estructuras.Conclusiones: La implementación desordenada de las propuestas tanto de modelos, como de índices y estructuras puede conllevar a problemas de estandarización es por esto, que deben estar normalizadas bajos las normas y estándares de la OGC (Open Geospatial Consortium).

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“…Researchers have addressed a variety of approaches in modeling and querying the location of the moving spatiotemporal data both in relational databases [5][6][7][8] and spatiotemporal databases [9][10][11]. Early studies on the positions of spatiotemporal data assumed that the spatiotemporal data remain static in each time interval [12,13].…”

Section: Introductionmentioning

confidence: 99%

Interpolation and Prediction of Spatiotemporal Data Based on XML Integrated with Grey Dynamic Model

Bai

2017

IJGI

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Abstract:Interpolation and prediction of spatiotemporal data are integral components of many real-world applications. Thus, approaches of interpolating and predicting spatiotemporal data have been extensively investigated. Currently, the grey dynamic model has been used to enhance the performance of interpolating and predicting spatiotemporal data. Meanwhile, the Extensible Markup Language (XML) has unique characteristics of information representation and exchange. In this paper, we first couple the grey dynamic model with the spatiotemporal XML model. Based on a definition of the position part of the spatiotemporal XML model, we extract the corresponding position information of each time interval and propose an algorithm for constructing an AVL tree to store them. Then, we present the architecture of an interpolating and predicting process and investigate change operations in positions. On this basis, we present an algorithm for interpolation and prediction of spatiotemporal data based on XML integrated with the grey dynamic model. Experimental results demonstrate the performance advantages of the proposed approach.

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A taxonomy for moving object queries in spatial databases

Cited by 37 publications

References 35 publications

Vertical Indexing for Moving Objects in Multifloor Environments

Vertical Indexing for Moving Objects in Multifloor Environments

Taxonomía de las bases de datos espaciotemporales con objetos en movimiento

Interpolation and Prediction of Spatiotemporal Data Based on XML Integrated with Grey Dynamic Model

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