A Methodology of Real-Time Data Fusion for Localized Big Data Analytics
The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a shor...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8329418/ |
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| author | Sohail Jabbar Kaleem R. Malik Mudassar Ahmad Omar Aldabbas Muhammad Asif Shehzad Khalid Kijun Han Syed Hassan Ahmed |
| author_facet | Sohail Jabbar Kaleem R. Malik Mudassar Ahmad Omar Aldabbas Muhammad Asif Shehzad Khalid Kijun Han Syed Hassan Ahmed |
| author_sort | Sohail Jabbar |
| collection | DOAJ |
| description | The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper. |
| format | Article |
| id | doaj-art-5dcf3c1fe8164749829434d3039d3051 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5dcf3c1fe8164749829434d3039d30512025-01-30T00:00:32ZengIEEEIEEE Access2169-35362018-01-016245102452010.1109/ACCESS.2018.28201768329418A Methodology of Real-Time Data Fusion for Localized Big Data AnalyticsSohail Jabbar0https://orcid.org/0000-0002-2127-1235Kaleem R. Malik1Mudassar Ahmad2https://orcid.org/0000-0002-6366-8230Omar Aldabbas3Muhammad Asif4https://orcid.org/0000-0003-1839-2527Shehzad Khalid5Kijun Han6Syed Hassan Ahmed7https://orcid.org/0000-0002-1381-5095Department of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Science and Engineering, Air University Multan Campus, Multan, PakistanDepartment of Computer Science, National Textile University, Faisalabad, PakistanFaculty of Engineering, AlBalqa’ Applied University, Amman, JordanDepartment of Computer Science, National Textile University, Faisalabad, PakistanDepartment of Computer Engineering, Bahria University, Islamabad, PakistanDepartment of Computer Engineering, Kyungpook National University, Daegu, South KoreaDepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USAThe traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.https://ieeexplore.ieee.org/document/8329418/Big datadata fusiondata transformationdata transformation challenges |
| spellingShingle | Sohail Jabbar Kaleem R. Malik Mudassar Ahmad Omar Aldabbas Muhammad Asif Shehzad Khalid Kijun Han Syed Hassan Ahmed A Methodology of Real-Time Data Fusion for Localized Big Data Analytics IEEE Access Big data data fusion data transformation data transformation challenges |
| title | A Methodology of Real-Time Data Fusion for Localized Big Data Analytics |
| title_full | A Methodology of Real-Time Data Fusion for Localized Big Data Analytics |
| title_fullStr | A Methodology of Real-Time Data Fusion for Localized Big Data Analytics |
| title_full_unstemmed | A Methodology of Real-Time Data Fusion for Localized Big Data Analytics |
| title_short | A Methodology of Real-Time Data Fusion for Localized Big Data Analytics |
| title_sort | methodology of real time data fusion for localized big data analytics |
| topic | Big data data fusion data transformation data transformation challenges |
| url | https://ieeexplore.ieee.org/document/8329418/ |
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