Hair-oriented data model for spatio-temporal data representation

Madraky, Abbas; Othman, Zulaiha Ali; Hamdan, Abdul Razak

doi:10.1016/j.eswa.2016.04.028

Cited by 2 publications

(2 citation statements)

References 46 publications

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“…Spatio-temporal data is different from relational data, in which computational approaches are developed for both spatial and temporal attributes [2]. Because of spatio-temporal data structure features, which record the general variables and the corresponding location and time [3], it became challenging to cluster the spatial and temporal data together.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

Rus¹,

Othman²,

Bakar³

et al. 2022

IJACSA

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Clustering Spatio-temporal data is challenging because of the complexity of processing the spatial and temporal aspects. Various enhanced clustering approaches, such as partition-based and hierarchical-based algorithms have been proposed. However, the ST-DBSCAN density-based algorithm is commonly used to process irregularly shaped clusters. Moreover, ST-DBSCAN considers neighborhood parameters as spatial and non-spatial. The preliminary results from our experiments indicate that the ST-DBSCAN algorithm addresses temporal elements less effectively. Therefore, an improvement to the ST-DBSCAN algorithm was proposed by considering three neighborhood boundaries in neighborhood function. This experiment used the El Niño dataset from the UCI repository. The experimental results show that the proposed algorithm increased the performance indices by 27% compared to existing approaches. Further improvement using the hierarchical Ward's method (with thresholds of 0.3 and 0.1) reduced the number of clusters from 240 to 6 and increased performance indices by up to 73%. It can be concluded that ST-HDBSCAN is a suitable clustering algorithm for Spatio-temporal data.

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Section: Introductionmentioning

confidence: 99%

A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

Rus¹,

Othman²,

Bakar³

et al. 2022

IJACSA

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show abstract

“…J. Geo-Inf. 2018, 7, 108 2 of 18 be replaced by practice, materiality, embodiment, affect, ontology, and knowledge discovery [15][16][17]. This is the result of the process of digitization of huge data sets and the development of visualization methods, which created new opportunities for storytelling.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Database of Places Located in the Border Area

Mościcka

Kuźma

2018

IJGI

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As a result of changes in boundaries, the political affiliation of locations also changes. Data on such locations are now collected in datasets with reference to the present or to the past space. Therefore, they can refer to localities that either no longer exist, have a different name now, or lay outside of the current borders of the country. Moreover, thematic data describing the past are related to events, customs, items that are always "somewhere". Storytelling about the past is incomplete without knowledge about the places in which the given story has happened. Therefore, the objective of the article is to discuss the concept of spatio-temporal database for border areas as an "engine" for visualization of thematic data in time-oriented geographical space. The paper focuses on studying the place names on the Polish-Ukrainian border, analyzing the changes that have occurred in this area over the past 80 years (where there were three different countries during this period), and defining the changeability rules. As a result of the research, the architecture of spatio-temporal databases is defined, as well as the rules for using them for data geovisualisation in historical context.

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Hair-oriented data model for spatio-temporal data representation

Cited by 2 publications

References 46 publications

A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

Spatio-Temporal Database of Places Located in the Border Area

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