視覺化道路救援商業智慧系統平台建構之研究

隨著商業競爭環境的詭譎多變，企業決策者越來越重視數據可能帶來的幫助，然而，大量原始資料（Raw Data）格式不一與雜亂，提高了閱讀與理解上的困難度，因此，資訊視覺化成為輕鬆理解數據意義的方法。本研究以道路救援公司2017年1月到2020年6月之服務歷史紀錄，以及內政部、氣象局等開放資料為基礎。運用Excel Power Business Intelligence（BI）工具打造一個全新的商業智慧系統。道路救援服務數據分析之呈現，透過企業決策者的需求訪談及文獻探討，將資訊圖表歸類為四種模式：（1）資料融合呈現、（2）時間序列呈現、（3）未來預估呈現、（4）空間地理位置呈現。協助企業在道路救援服務上，有效地分析內外部資訊之關聯性，透過視覺化的資訊呈現，快速掌握市場發展脈動，提升決策之精準效度。

關鍵字

道路救援；商業智慧系統；資訊視覺化

並列摘要

With the changing competitive environment, business decision makers are paying more and more attention to the assistance that data may bring. However, a large amount of raw data comes with inconsistent formats and messy arrangements which increase the level of difficulty for reading and understanding. Therefore, information visualization has become an easy and meaningful way to understand data. This research is based on the service history of road rescue companies from January 2017 to June 2020 and the open data from the Ministry of the Interior and the Central Weather Bureau to conduct to conduct researches on three stages, respectively: enterprise corporate data collection & sorting, data warehousing, and information visualization platform system construction. An Excel Power Business Intelligence (BI) Tool is used to create a brand new business intelligence system. In the presentation of road rescue service data analysis, infographics are classified into four modes: (1) Data fusion presentation, (2) Time series presentation, (3) Future forecast presentation, and (4) Spatial geographic location presentation through required interviews with business decision makers as well as literature discussions. To assist companies with road rescue services, the correlation between internal and external information is analyzed effectively. Through visualized information presentation, the market development trend can be grasped quickly to improve the accuracy and validity of decision-making.

並列關鍵字

Road Rescue ； Business Intelligence System ； Information Visualization

參考文獻

Ali, S. M., Gupta, N., Nayak, G. K. & Lenka, R. K. (2016). Big data visualization:Tools and challenges. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), 656-660. doi: 10.1109/IC3I.2016.7918044

Bernard Liautaud (2000). E-Business Intelligence: Turning Information into Knowledge into Profit. McGraw-Hill, Inc.

Google Scholar

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4)

Google Scholar

Chen, T. F., Chang, K. H. & Tsai, C. Y. (2014). Modeling direct and indirect effect of long range transport on atmospheric PM2.5 levels. Atmospheric Environment, 89,1-9.

Google Scholar

Choi, T. M., Chan, H. K. & Yue, X. (2017). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.

Google Scholar

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