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- Publisher Website: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.115
- Scopus: eid_2-s2.0-84995530407
- WOS: WOS:000391002500100
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Conference Paper: A four-layer architecture for online and historical big data analytics
| Title | A four-layer architecture for online and historical big data analytics |
|---|---|
| Authors | |
| Keywords | Big data analytics four-layer architecture OHBDA online big data stream |
| Issue Date | 2016 |
| Publisher | IEEE. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7585853 |
| Citation | 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing (IEEE DataCom-16), Auckland, New Zealand, 8-12 August 2016. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) Proceedings, p. 634-639 How to Cite? |
| Abstract | Big data processing and analytics technologies have drawn much attention in recent years. However, the recent explosive growth of online data streams brings new challenges to the existing technologies. These online data streams tend to be massive, continuously arriving, heterogeneous, time-varying and unbounded. Therefore, it is necessary to have an integrated approach to process both big static data and online big data streams. We call this integrated approach online and historical big data analytics (OHBDA). We propose a four-layer architecture of OHBDA, i.e. including the storage layer, online and historical data processing layer, analytics layer, and decision-making layer. Functionalities and challenges of the four layers are further discussed. We conclude with a discussion of the requirements for the future OHBDA solutions, which may serve as a foundation for future big data analytics research. |
| Description | IEEE DataCom 2016 was held with IEEE CyberSciTech 2016, IEEE DASC 2016 and IEEE PICom 2016 DataCom Session 7: Big Data Infrastructure, Clouds and HPC – 2 |
| Persistent Identifier | http://hdl.handle.net/10722/247771 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Zhu, JY | - |
| dc.contributor.author | Xu, J | - |
| dc.contributor.author | Li, VOK | - |
| dc.date.accessioned | 2017-10-18T08:32:24Z | - |
| dc.date.available | 2017-10-18T08:32:24Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.citation | 2016 IEEE 2nd International Conference on Big Data Intelligence and Computing (IEEE DataCom-16), Auckland, New Zealand, 8-12 August 2016. In 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) Proceedings, p. 634-639 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/247771 | - |
| dc.description | IEEE DataCom 2016 was held with IEEE CyberSciTech 2016, IEEE DASC 2016 and IEEE PICom 2016 | - |
| dc.description | DataCom Session 7: Big Data Infrastructure, Clouds and HPC – 2 | - |
| dc.description.abstract | Big data processing and analytics technologies have drawn much attention in recent years. However, the recent explosive growth of online data streams brings new challenges to the existing technologies. These online data streams tend to be massive, continuously arriving, heterogeneous, time-varying and unbounded. Therefore, it is necessary to have an integrated approach to process both big static data and online big data streams. We call this integrated approach online and historical big data analytics (OHBDA). We propose a four-layer architecture of OHBDA, i.e. including the storage layer, online and historical data processing layer, analytics layer, and decision-making layer. Functionalities and challenges of the four layers are further discussed. We conclude with a discussion of the requirements for the future OHBDA solutions, which may serve as a foundation for future big data analytics research. | - |
| dc.language | eng | - |
| dc.publisher | IEEE. The Proceedings' web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7585853 | - |
| dc.relation.ispartof | IEEE International Conference on Big Data Intelligence and Computing | - |
| dc.rights | IEEE International Conference on Big Data Intelligence and Computing. Copyright © IEEE. | - |
| dc.rights | ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
| dc.subject | Big data analytics | - |
| dc.subject | four-layer architecture | - |
| dc.subject | OHBDA | - |
| dc.subject | online big data stream | - |
| dc.title | A four-layer architecture for online and historical big data analytics | - |
| dc.type | Conference_Paper | - |
| dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
| dc.identifier.authority | Li, VOK=rp00150 | - |
| dc.identifier.doi | 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.115 | - |
| dc.identifier.scopus | eid_2-s2.0-84995530407 | - |
| dc.identifier.hkuros | 279686 | - |
| dc.identifier.spage | 634 | - |
| dc.identifier.epage | 639 | - |
| dc.identifier.isi | WOS:000391002500100 | - |
| dc.publisher.place | United States | - |