Abstract
Data Provenance is information about the origin and creation process of data. Such information is useful for debugging data and transformations, auditing, evaluating the quality of and trust in data, modelling authenticity, and implementing access control for derived data. Provenance has been studied by the database, workflow, and distributed systems communities, but provenance for Big Data - which we refer to as Big Provenance - is a largely unexplored field. This paper reviews existing approaches for large-scale distributed provenance and discusses potential challenges for Big Data benchmarks that aim to incorporate provenance data/management. Furthermore, we will examine how Big Data benchmarking could benefit from different types of provenance information. We argue that provenance can be used for identifying and analyzing performance bottlenecks, to compute performance metrics, and to test a system’s ability to exploit commonalities in data and processing.
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References
Ahmad, F., Lee, S., Thottethodi, M., Vijaykumar, T.: PUMA: Purdue MapReduce Benchmarks Suite. Tech. Rep. TR-ECE-12-11, Purdue University (2012)
Akoush, S., Sohan, R., Hopper, A.: HadoopProv: Towards Provenance as A First Class Citizen in MapReduce. TaPP (2013)
Amsterdamer, Y., Davidson, S., Deutch, D., Milo, T., Stoyanovich, J., Tannen, V.: Putting Lipstick on Pig: Enabling Database-style Workflow Provenance. PVLDB 5(4), 346–357 (2011)
Chapman, A., Jagadish, H.V., Ramanan, P.: Efficient Provenance Storage. In: SIGMOD, pp. 993–1006 (2008)
Divyakant, A., Bertino, E., Davidson, S., Franklin, M., Halevy, A., Han, J., Jagadish, H.V., Madden, S., Papakonstantinou, Y., Ramakrishnan, R., Ross, K., Shahabi, C., Vaithyanathan, S., Widom, J.: Challenges and opportunities with big data (2012)
Graefe, G.: Benchmarking robust performance. In: Rabl, T., et al. (eds.) WBDB 2012. LNCS, vol. 8163, Springer, Heidelberg (2012)
Ikeda, R., Park, H., Widom, J.: Provenance for generalized map and reduce workflows. In: CIDR, pp. 273–283 (2011)
Karvounarakis, G., Green, T.: Semiring-Annotated Data: Queries and Provenance. SIGMOD Record 41(3), 5–14 (2012)
Malik, T., Nistor, L., Gehani, A.: Tracking and Sketching Distributed Data Provenance. In: eScience, pp. 190–197 (2010)
Muniswamy-Reddy, K., Macko, P., Seltzer, M.: Provenance for the cloud. In: FAST, pp. 197–210 (2010)
Park, J., Nguyen, D., Sandhu, R.: A provenance-based access control model. In: PST, pp. 137–144 (2012)
Seltzer, M., Macko, P., Chiarini, M.: Collecting Provenance via the Xen Hypervisor. In: TaPP (2011)
Widom, J.: Trio: A System for Managing Data, Uncertainty, and Lineage. Managing and Mining Uncertain Data, 1–35 (2008)
Zhang, M., Zhang, X., Zhang, X., Prabhakar, S.: Tracing Lineage beyond Relational Operators. In: VLDB, pp. 1116–1127 (2007)
Zhou, W., Mapara, S., Ren, Y., Li, Y., Haeberlen, A., Ives, Z., Loo, B., Sherr, M.: Distributed time-aware provenance. PVLDB 6(2), 49–60 (2012)
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Glavic, B. (2014). Big Data Provenance: Challenges and Implications for Benchmarking. In: Rabl, T., Poess, M., Baru, C., Jacobsen, HA. (eds) Specifying Big Data Benchmarks. WBDB WBDB 2012 2012. Lecture Notes in Computer Science, vol 8163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53974-9_7
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DOI: https://doi.org/10.1007/978-3-642-53974-9_7
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