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: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Intelligent Network and Systems Society
2023
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| 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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| Summary: | 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. |
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