Agriculture Spatiotemporal Business Intelligence using Open Data Integration

Wisnubhadra, Irya; Adithama, Stephanie Pamela; Baharin, Safiza Suhana Kamal; Suryana, Nanna

doi:10.1109/isriti48646.2019.9034635

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Figure . 1 Farming cycle, adapted from [1] by farmers in some district during the month of Ramadan this year? How much seed and fertilizer will be required over the next two months?…”

Section: Introductionmentioning

confidence: 99%

Publishing and Querying of Spatiotemporal Agriculture Production Data Warehouse on Semantic Web Using QB4MobOLAP

2023

IJIES

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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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“…Figure . 1 Farming cycle, adapted from [1] by farmers in some district during the month of Ramadan this year? How much seed and fertilizer will be required over the next two months?…”

Section: Introductionmentioning

confidence: 99%

Publishing and Querying of Spatiotemporal Agriculture Production Data Warehouse on Semantic Web Using QB4MobOLAP

2023

IJIES

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Business Intelligence for Agricultural Foreign Trade: Design and Application of Power BI Dashboard

Sanabia-Lizarraga,

Carballo-Mendívil,

Arellano-González

et al. 2024

Sustainability

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In today’s rapidly evolving business landscape, data analytics has become a critical tool for organizations striving to remain competitive and make informed decisions. Although technology facilitates data analysis, many decision-makers need more technical knowledge to use these tools. This project focuses on developing a business intelligence system in Power BI to analyze agricultural exports from Mexico as part of the Agro-logistics Observatory, aiming to bridge this gap and make data more accessible for non-technical users. The objective is to create an interactive panel that would allow users to visualize and compare export and import data, thereby supporting decision-making for both business and academic purposes. The methodology involved collecting and cleaning data from various sources, such as public institutions, and a modeling process that integrates geospatial data and economic classifications. Key performance indicators (KPIs), such as trade balance and the export share by-product, were designed and calculated to identify critical products and trade patterns. Although similar approaches have been presented in the literature, the novelty of this research lies in the detailed presentation of its methodology, which provides a step-by-step guide for its replication that is often lacking in other studies. In addition, it offers a dashboard that integrates data from various sources, presenting them through easy-to-interpret visualizations so that stakeholders can quickly and efficiently access valuable information beyond traditional data visualization. Furthermore, the project highlights opportunities for improvements in data visualization, including integrating more specific product data and enhancing the dashboard’s capabilities through predictive analysis.

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Agriculture Spatiotemporal Business Intelligence using Open Data Integration

Cited by 2 publications

References 15 publications

Publishing and Querying of Spatiotemporal Agriculture Production Data Warehouse on Semantic Web Using QB4MobOLAP

Publishing and Querying of Spatiotemporal Agriculture Production Data Warehouse on Semantic Web Using QB4MobOLAP

Business Intelligence for Agricultural Foreign Trade: Design and Application of Power BI Dashboard

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