Platform to Build the Knowledge Base by Combining Sensor Data and Context Data
Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to pro...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2014-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/542764 |
| _version_ | 1797763409721163776 |
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| author | Sungho Shin Jungho Um Dongmin Seo Sung-Pil Choi Seungwoo Lee Hanmin Jung Mun Yong Yi |
| author_facet | Sungho Shin Jungho Um Dongmin Seo Sung-Pil Choi Seungwoo Lee Hanmin Jung Mun Yong Yi |
| author_sort | Sungho Shin |
| collection | DOAJ |
| description | Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms do not guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform. |
| first_indexed | 2024-03-12T19:41:14Z |
| format | Article |
| id | doaj.art-451c783387ff4f3094b5a44dcdc0769a |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T19:41:14Z |
| publishDate | 2014-01-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-451c783387ff4f3094b5a44dcdc0769a2023-08-02T03:47:26ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772014-01-011010.1155/2014/542764542764Platform to Build the Knowledge Base by Combining Sensor Data and Context DataSungho Shin0Jungho Um1Dongmin Seo2Sung-Pil Choi3Seungwoo Lee4Hanmin Jung5Mun Yong Yi6 Department of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Computer Intelligence Research, Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 305-806, Republic of Korea Department of Knowledge Service Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of KoreaSensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms do not guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform.https://doi.org/10.1155/2014/542764 |
| spellingShingle | Sungho Shin Jungho Um Dongmin Seo Sung-Pil Choi Seungwoo Lee Hanmin Jung Mun Yong Yi Platform to Build the Knowledge Base by Combining Sensor Data and Context Data International Journal of Distributed Sensor Networks |
| title | Platform to Build the Knowledge Base by Combining Sensor Data and Context Data |
| title_full | Platform to Build the Knowledge Base by Combining Sensor Data and Context Data |
| title_fullStr | Platform to Build the Knowledge Base by Combining Sensor Data and Context Data |
| title_full_unstemmed | Platform to Build the Knowledge Base by Combining Sensor Data and Context Data |
| title_short | Platform to Build the Knowledge Base by Combining Sensor Data and Context Data |
| title_sort | platform to build the knowledge base by combining sensor data and context data |
| url | https://doi.org/10.1155/2014/542764 |
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