DocumentCode
249358
Title
Provenance as a Service: A Data-centric Approach for Real-Time Monitoring
Author
Hammad, Rafat ; Ching-Seh Wu
Author_Institution
Dept. of Comput. Sci. & Eng., Oakland Univ., Oakland, MI, USA
fYear
2014
fDate
June 27 2014-July 2 2014
Firstpage
258
Lastpage
265
Abstract
Provenance of electronic data is an important piece of the metadata of a data object. Making systems provenance-aware can greatly benefit all parties involved. This paper discusses the importance of data provenance and presents an open cloud architecture named CloudProv, a framework to integrate, model, and monitor the provenance data. Our elastic cloud architecture will provide an open APIs which allows independent organizations to develop applications that can share and integrate provenance data across internal and external organizational boundaries. We propose a method to model the collected provenance information in such a way that can be used for continuous compliance monitoring and for root cause analysis. We have implemented a prototype of our framework, and used it to verify how we can generate a service model for provenance data using a small, but interesting set of data. We present a design of our proposed framework and its components along with a prototype implementation.
Keywords
application program interfaces; cloud computing; data integration; meta data; open systems; software architecture; CloudProv framework; application development; continuous compliance monitoring; data object; data-centric approach; dependence service model; elastic cloud architecture; electronic data provenance; external organizational boundaries; internal organizational boundaries; metadata; open API; open cloud architecture; provenance data integration; provenance data modelling; provenance data monitoring; provenance data sharing; provenance information collection; provenance-as-a-service; provenance-aware systems; real-time data monitoring; root cause analysis; Adaptation models; Business; Cloud computing; Data models; Electronic mail; Engines; Monitoring; Provenance; cloud; data streams; distributed event-based systems; message dependence graph; provenance management; real-time monitoring; root cause analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location
Anchorage, AK
Print_ISBN
978-1-4799-5056-0
Type
conf
DOI
10.1109/BigData.Congress.2014.46
Filename
6906787
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