WBDPR: a way for big data provenance relationship Online publication date: Mon, 09-Sep-2024
by Zhiwen Zheng; Ying Song; Yunmei Shi; Bo Wang
International Journal of Computational Science and Engineering (IJCSE), Vol. 27, No. 5, 2024
Abstract: With the increasing complexity of data generation relationships, existing provenance frameworks face challenges such as resource consumption, redundant storage, and slow query times. This paper proposes a way for big data provenance relationship (WBDPR), a solution for efficient data provenance in the Hadoop scenario. WBDPR addresses these issues by supporting asynchronous provenance log integration and introducing a provenance storage mode and query algorithm based on provenance directed acyclic graph (PROV-DAG). Experimental results demonstrate that WBDPR reduces memory occupation by 56% and index disk storage by 75%. Additionally, it improves query performance by 80% in 64% of leaf and intermediate nodes. Compared to RAMP, Newt, and Atlas systems, WBDPR achieves up to 5.1% reduction in tracing time. WBDPR technology is a fault-tolerant technology that records the provenance information of data and its calculation process, and its significance lies in ensuring the integrity and reliability of data.
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