Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis
Demand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-09-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/907169 |
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| _version_ | 1850176411500806144 |
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| author | Wooju Kim Jung Hoon Won Sangun Park Juyoung Kang |
| author_facet | Wooju Kim Jung Hoon Won Sangun Park Juyoung Kang |
| author_sort | Wooju Kim |
| collection | DOAJ |
| description | Demand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly changing environmental data such as fine dust concentration but also the responses of potential customers that are expected to affect the demand for a medicine. Therefore, demand forecasting models based on data analysis in wireless sensor networks and topic modeling of buzzwords in blog documents were suggested in this study. First, we analyzed topics of documents from blogs that describe the symptoms of certain diseases related to selected medicines. Thereafter, we extracted topic trends for a selected period and constructed demand forecasting models that consist of topic trends, environmental data from wireless sensor networks, and time-series sales data. The experiment results show that topic trends about medicines significantly affect the performance of demand forecasting for these medicines. |
| format | Article |
| id | doaj-art-ae381a04f4d243859eceed1607178c9d |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ae381a04f4d243859eceed1607178c9d2025-08-20T02:19:16ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-09-011110.1155/2015/907169907169Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend AnalysisWooju Kim0Jung Hoon Won1Sangun Park2Juyoung Kang3 Department of Information & Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea Department of Information & Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea Department of Management Information System, Kyonggi University, 154-42 Gwanggyosan-ro, Yeongtong-gu, Suwon 442-760, Republic of Korea Department of e-Business, Ajou University, 206 Worldcup-ro, Yeongtong-gu, Suwon 443-749, Republic of KoreaDemand forecasting in the biomedical area is becoming more important because of radical changes in the macroeconomic environment and consumption trends. Moreover, the need for big data analysis on data from wireless sensor networks and social media is increasing because it shows not only the rapidly changing environmental data such as fine dust concentration but also the responses of potential customers that are expected to affect the demand for a medicine. Therefore, demand forecasting models based on data analysis in wireless sensor networks and topic modeling of buzzwords in blog documents were suggested in this study. First, we analyzed topics of documents from blogs that describe the symptoms of certain diseases related to selected medicines. Thereafter, we extracted topic trends for a selected period and constructed demand forecasting models that consist of topic trends, environmental data from wireless sensor networks, and time-series sales data. The experiment results show that topic trends about medicines significantly affect the performance of demand forecasting for these medicines.https://doi.org/10.1155/2015/907169 |
| spellingShingle | Wooju Kim Jung Hoon Won Sangun Park Juyoung Kang Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis International Journal of Distributed Sensor Networks |
| title | Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis |
| title_full | Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis |
| title_fullStr | Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis |
| title_full_unstemmed | Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis |
| title_short | Demand Forecasting Models for Medicines through Wireless Sensor Networks Data and Topic Trend Analysis |
| title_sort | demand forecasting models for medicines through wireless sensor networks data and topic trend analysis |
| url | https://doi.org/10.1155/2015/907169 |
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