AI-Powered Impact Analysis for Multi-Site Schema Evolution
Keywords:
schema evolution, AI impact analysis, DDL diff simulation, multi-site databases, dependency propagationAbstract
Schema evolution in distributed, multi-site database ecosystems is complex and error-prone which causes unanticipated propagation failures across the staging and production tiers. The objective of this paper is to present an AI-driven impact analysis technique that uses knowledge graph-based reasoning which combines DDL diffs and reproduce their transitive effects across related contexts.
Downloads
References
C. Curino, H. Moon, E. Wu, and S. Madden, “Avoiding the pitfalls of schema evolution in distributed databases,” Proc. VLDB Endow., vol. 5, no. 12, pp. 1704–1715, Aug. 2012.
A. Pavlo, D. J. Abadi, D. DeWitt, S. Madden, M. Stonebraker, A. Rasin, E. Y. Chang, and M. Cherniack, “A comparison of approaches to large-scale data analysis,” Proc. SIGMOD, pp. 165–178, June 2009.
R. Cattell, “Scalable SQL and NoSQL data stores,” SIGMOD Rec., vol. 39, no. 4, pp. 12–27, Dec. 2011.
J. F. Roddick, “A survey of schema versioning issues for database systems,” Inf. Softw. Technol., vol. 37, no. 7, pp. 383–393, Sept. 1995.
M. J. Carey, “The architecture of the EXODUS extensible DBMS,” SIGMOD Rec., vol. 15, no. 2, pp. 49–54, June 1986.
A. Abouzied, S. Madden, and A. Silberschatz, “Hood: Fast schema modifications for data warehouses,” Proc. VLDB Endow., vol. 6, no. 11, pp. 1078–1089, Sept. 2013.
S. Rizzi, “Schema evolution and versioning in multidimensional databases,” Data Knowl. Eng., vol. 58, no. 2, pp. 142–167, May 2006.
F. Wang, J. Ma, and M. R. Lyu, “Mining and analyzing data from version control systems: A survey,” IEEE Trans. Softw. Eng., vol. 43, no. 6, pp. 492–518, June 2017.
G. Guerrini, L. Bellatreche, and F. Trujillo, “On schema evolution in data warehouses: an overview,” Data Sci. J., vol. 6, pp. 1–19, 2007.
A. Bonifati, R. Ciucanu, and P. Papotti, “Handling schema evolution in the cloud,” Proc. IEEE Int. Conf. Data Eng., pp. 1165–1176, Apr. 2017.
Y. S. Ma, H. W. Chang, and C. T. Wang, “Schema evolution in distributed databases: Issues and approaches,” J. Syst. Softw., vol. 45, no. 3, pp. 239–251, Dec. 1999.
M. Färber, S. Kannen, and M. Sattler, “Schema change impact analysis in complex database environments,” Proc. ACM SIGMOD, pp. 1509–1518, June 2013.
A. Hogan et al., “Knowledge graphs,” arXiv preprint arXiv:2003.02320, Mar. 2020. (Background knowledge relevant up to 2019)
J. Lehmann et al., “DBpedia – a large-scale, multilingual knowledge base extracted from Wikipedia,” Semantic Web, vol. 6, no. 2, pp. 167–195, Apr. 2015.
P. Hitzler, M. Krötzsch, and S. Rudolph, Foundations of Semantic Web Technologies. Boca Raton, FL, USA: CRC Press, 2009.
S. Agarwal et al., “Automated impact analysis for schema evolution in data-intensive systems,” Proc. IEEE Int. Conf. Data Eng., pp. 168–179, Apr. 2018.
M. R. Vieira and H. A. Santos, “A semantic-based approach to schema evolution and versioning in data warehouses,” Proc. ACM Symposium on Applied Computing, pp. 832–837, Mar. 2011.
S. B. Navathe and C. T. O’Connor, “Database schema evolution and conformance,” VLDB J., vol. 5, no. 2, pp. 158–183, Apr. 1996.
Y. Tian, B. Rountree, and W. Zwaenepoel, “Schema evolution in distributed data management,” IEEE Data Eng. Bull., vol. 38, no. 1, pp. 28–39, Mar. 2015.
J. E. Gonzalez et al., “GraphX: Graph processing in a distributed dataflow framework,” Proc. USENIX Symposium on Operating Systems Design and Implementation, pp. 599–613, Oct. 2014.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
