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Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP

Data Warehouse (DW) and on line analytical processing (OLAP) as parts of business intelligence (BI) are proven platform for decision-making support. Over the previous decade, the advent of Web 2.0 technologies has increased the accessibility of the data web across the internet. The use of semant...

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Main Authors: Wisnubhadra, Irya, Kamal Baharin, Safiza Suhana, Emran, Nurul Akmar, Budiyanto, Djoko Setyohadi
Format: Article
Language:English
Published: Intelligent Network and Systems Society 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27649/2/0028211122023.PDF
http://eprints.utem.edu.my/id/eprint/27649/
https://inass.org/wp-content/uploads/2023/01/2023063039-2.pdf
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spelling my.utem.eprints.276492024-10-04T16:08:59Z http://eprints.utem.edu.my/id/eprint/27649/ Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP Wisnubhadra, Irya Kamal Baharin, Safiza Suhana Emran, Nurul Akmar Budiyanto, Djoko Setyohadi Data Warehouse (DW) and on line analytical processing (OLAP) as parts of business intelligence (BI) are proven platform for decision-making support. Over the previous decade, the advent of Web 2.0 technologies has increased the accessibility of the data web across the internet. The use of semantic web (SW) including linked open data, linked open statistical data, and open government data is rising at a breakneck pace, creating a greater pool of machine-readable data that can be shared and analyzed for strategic decision-making. The new DW concept takes a novel approach to merging the availability of these data resources containing the spatiotemporal data. This paper proposed a way to publish agricultural production data in SW using a spatiotemporal DW vocabulary called QB4MobOLAP. The data sources come from the village and rural area information system (SIDeKa), which records agricultural production transactions with spatiotemporal information. This paper also applying a new spatiotemporal data warehousing technique for analyzing spatiotemporal data for agricultural productivity. The experiment uses 2.916.864 triples with temporal type data in a fact table, and 81.914 triples dimension data with spatial and temporal data. This approach offers a practical, simple model, and good performance for enabling executive decisions on agriculture production analysis. The experiment has execution time average below 10 s for spatiotemporal aggregation and less development time compared with DBMS. This approach also has 5-stars open data index. This paper also highlighted opportunities for scaling and fostering the spatiotemporal data warehousing initiative. Intelligent Network and Systems Society 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27649/2/0028211122023.PDF Wisnubhadra, Irya and Kamal Baharin, Safiza Suhana and Emran, Nurul Akmar and Budiyanto, Djoko Setyohadi (2023) Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP. International Journal of Intelligent Engineering and Systems, 16 (3). 495 - 508. ISSN 2185-310X https://inass.org/wp-content/uploads/2023/01/2023063039-2.pdf 10.22266/ijies2023.0630.39
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Data Warehouse (DW) and on line analytical processing (OLAP) as parts of business intelligence (BI) are proven platform for decision-making support. Over the previous decade, the advent of Web 2.0 technologies has increased the accessibility of the data web across the internet. The use of semantic web (SW) including linked open data, linked open statistical data, and open government data is rising at a breakneck pace, creating a greater pool of machine-readable data that can be shared and analyzed for strategic decision-making. The new DW concept takes a novel approach to merging the availability of these data resources containing the spatiotemporal data. This paper proposed a way to publish agricultural production data in SW using a spatiotemporal DW vocabulary called QB4MobOLAP. The data sources come from the village and rural area information system (SIDeKa), which records agricultural production transactions with spatiotemporal information. This paper also applying a new spatiotemporal data warehousing technique for analyzing spatiotemporal data for agricultural productivity. The experiment uses 2.916.864 triples with temporal type data in a fact table, and 81.914 triples dimension data with spatial and temporal data. This approach offers a practical, simple model, and good performance for enabling executive decisions on agriculture production analysis. The experiment has execution time average below 10 s for spatiotemporal aggregation and less development time compared with DBMS. This approach also has 5-stars open data index. This paper also highlighted opportunities for scaling and fostering the spatiotemporal data warehousing initiative.
format Article
author Wisnubhadra, Irya
Kamal Baharin, Safiza Suhana
Emran, Nurul Akmar
Budiyanto, Djoko Setyohadi
spellingShingle Wisnubhadra, Irya
Kamal Baharin, Safiza Suhana
Emran, Nurul Akmar
Budiyanto, Djoko Setyohadi
Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP
author_facet Wisnubhadra, Irya
Kamal Baharin, Safiza Suhana
Emran, Nurul Akmar
Budiyanto, Djoko Setyohadi
author_sort Wisnubhadra, Irya
title Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP
title_short Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP
title_full Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP
title_fullStr Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP
title_full_unstemmed Publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using QB4MobOLAP
title_sort publishing and querying of spatiotemporal agriculture production data warehouse on semantic web using qb4mobolap
publisher Intelligent Network and Systems Society
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27649/2/0028211122023.PDF
http://eprints.utem.edu.my/id/eprint/27649/
https://inass.org/wp-content/uploads/2023/01/2023063039-2.pdf
_version_ 1812129257271328768
score 13.211869

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