Smart big data framework for insight discovery
Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157821003542 |
| _version_ | 1811296302609203200 |
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| author | Siham Yousfi Dalila Chiadmi Maryem Rhanoui |
| author_facet | Siham Yousfi Dalila Chiadmi Maryem Rhanoui |
| author_sort | Siham Yousfi |
| collection | DOAJ |
| description | Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems. |
| first_indexed | 2024-04-13T05:46:24Z |
| format | Article |
| id | doaj.art-0d3dd551b573491e93277bf615c6ff41 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T05:46:24Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-0d3dd551b573491e93277bf615c6ff412022-12-22T02:59:56ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-11-01341097779792Smart big data framework for insight discoverySiham Yousfi0Dalila Chiadmi1Maryem Rhanoui2SIP Research Team, Rabat IT Center, EMI, Mohammed V University in Rabat, Morocco; Meridian Team, LYRICA Laboratory, School of Information Sciences, Rabat, Morocco; Corresponding author.SIP Research Team, Rabat IT Center, EMI, Mohammed V University in Rabat, MoroccoMeridian Team, LYRICA Laboratory, School of Information Sciences, Rabat, Morocco; IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, MoroccoBig Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems.http://www.sciencedirect.com/science/article/pii/S1319157821003542Big data value chainData integrationHeterogeneous data sourcesSpacio-temporal traffic monitoringTraffic management systems |
| spellingShingle | Siham Yousfi Dalila Chiadmi Maryem Rhanoui Smart big data framework for insight discovery Journal of King Saud University: Computer and Information Sciences Big data value chain Data integration Heterogeneous data sources Spacio-temporal traffic monitoring Traffic management systems |
| title | Smart big data framework for insight discovery |
| title_full | Smart big data framework for insight discovery |
| title_fullStr | Smart big data framework for insight discovery |
| title_full_unstemmed | Smart big data framework for insight discovery |
| title_short | Smart big data framework for insight discovery |
| title_sort | smart big data framework for insight discovery |
| topic | Big data value chain Data integration Heterogeneous data sources Spacio-temporal traffic monitoring Traffic management systems |
| url | http://www.sciencedirect.com/science/article/pii/S1319157821003542 |
| work_keys_str_mv | AT sihamyousfi smartbigdataframeworkforinsightdiscovery AT dalilachiadmi smartbigdataframeworkforinsightdiscovery AT maryemrhanoui smartbigdataframeworkforinsightdiscovery |