Abstract Data quality is important in many data-driven applications, such as decision making, data analysis, and data mining. Recent studies focus on data cleaning techniques by deleting or repairing the dirty data, which may cause information loss and bring new inconsistencies. To avoid these problems, we propose EntityManager, a general system to manage dirty data without data cleaning. This system takes real-world entity as the basic storage unit and retrieves query results according to the quality requirement of users. The system is able to handle all kinds of inconsistencies recognized by entity resolution. We elaborate the EntityManager system, covering its architecture, data model, and query processing techniques. To process queries efficiently, our system adopts novel indices, similarity operator and query optimization techniques. Finally, we verify the efficiency and effectiveness of this system and present future research challenges.
This work was partially supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2015BAH10F01, the National Natural Science Foundation of China under Grant Nos. U1509216, 61472099, and 61133002, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Heilongjiang Province of China under Grant No. LC2016026, and the Ministry of Education (MOE)-Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology.
Corresponding Authors: Hong-Zhi Wang
About author: Xue-Li Liu is a Ph.D. candidate in computer technology and science, Harbin Institute of Technology, Harbin. Her research interests include data quality and massive data management.
Cite this article:
Xue-Li Liu, Hong-Zhi Wang, Jian-Zhong Li, Hong Gao.EntityManager: Managing Dirty Data Based on Entity Resolution[J] Journal of Computer Science and Technology, 2017,V32(3): 644-661
 Andritsos P, Fuxman A, Miller R J. Clean answers over dirty databases: A probabilistic approach. In Proc. the 22nd ICDE, April 2006, Article No. 30. Fuxman A D, Miller R J. First-order query rewriting for inconsistent databases. In Proc. the 10th ICDT, January 2005, pp.337-351. Fuxman A, Fazli E, Miller R J. Conquer: Efficient management of inconsistent databases. In Proc. SIGMOD, June 2005, pp.155-166. Boulos J, Dalvi N, Mandhani B, Mathur S, Ré C, Suciu D. MYSTIQ: A system for finding more answers by using probabili31 ties. In Proc. SIGMOD, June 2005, pp.891-893. Hassanzadeh O, Miller R J. Creating probabilistic databases from duplicated data. VLDB J., 2009, 18(5): 1141-1166. Widom J. Trio: A system for integrated management of data, accuracy, and lineage. In Proc. CIDR, Jan. 2005, pp.262-276. Getoor L, Machanavajjhala A. Entity resolution: Theory, practice & open challenges. PVLDB, 2012, 5(12): 2018- 2019. Waguih D A, Berti-Equille L. Truth discovery algorithms: An experimental evaluation. arXiv: 1409.6428, May 2014. https://arxiv.org/abs/1409.6428, Mar. 2017. Lipner S B, Balenson D M, Ellison C M, Walker S T. System and method for data recovery, September 1996. US Patent 5,557,765. https://www.google.com/patents/us5557765, Apr. 2017. Miles M B, Huberman A M. Qualitative Data Analysis: An Expanded Sourcebook. Sage Publications, Inc., 1994. Rahm E, Do H H. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull., 2000, 23(4): 3-13. Arasu A, Ganti V, Kaushik R. Efficient exact set-similarity joins. In Proc. the 32nd VLDB, September 2006, pp.918- 929. Behm A, Ji S, Li C, Lu J. Space-constrained gram-based indexing for efficient approximate string search. In Proc. ICDE, March 29-April 2, 2009, pp.604-615. Goemans M X, Williamson D P. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM, 1995, 42(6): 1115-1145. Hadjieleftheriou M, Chandel A, Koudas N, Srivastava D. Fast indexes and algorithms for set similarity selection queries. In Proc. the 24th ICDE, April 2008, pp.267-276. Hadjieleftheriou M, Koudas N, Srivastava D. Incremental maintenance of length normalized indexes for approximate string matching. In Proc. ACM SIGMOD, June 29-July 2, 2009, pp.429-440. Xiao C, Wang W, Lin X. Ed-Join: An efficient algorithm for similarity joins with edit distance constraints. PVLDB, 2008, 1(1): 933-944. Xiao C, Wang W, Lin X, Yu J X, Wang G. Efficient similarity joins for near-duplicate detection. ACM Transactions on Database Systems, 2011, 36(3): 15:1-15:15. Zhang Z, Hadjieleftheriou M, Ooi B C, Srivastava D. Bedtree: An all-purpose index structure for string similarity search based on edit distance. In Proc. SIGMOD, June 2010, pp.915-926. Bayardo R J, Ma Y, Srikant R. Scaling up all pairs similarity search. In Proc. the 16th WWW, May 2007, pp.131-140. Wang J, Li G, Feng J. Trie-join: Efficient trie-based string similarity joins with edit-distance constraints. PVLDB, 2010, 3(1): 1219-1230. Sarawagi S, Kirpal A. Efficient set joins on similarity predicates. In Proc. ACM SIGMOD, June 2004, pp.743-754. Vernica R, Carey M J, Li C. Efficient parallel set-similarity joins using mapreduce. In Proc. ACM SIGMOD, June 2010, pp.495-506. Li C, Wang B, Yang X. VGRAM: Improving performance of approximate queries on string collections using variablelength grams. In Proc. the 33rd VLDB, September 2007, pp.303-314. Wang J, Li G, Feng J. Can we beat the prefix filtering?: An adaptive framework for similarity join and search. In Proc. ACM SIGMOD, May 2012, pp.85-96. Ioannidis Y E. The history of histograms (abridged). In Proc. the 29th VLDB, Sept. 2003, pp.19-30. Haas P J, Naughton J F, Seshadri S, Swami A N. Selectivity and cost estimation for joins based on random sampling. Journal of Computer and System Sciences, 1996, 52(3): 550-569. Hou W C, Ozsoyoglu G, Dogdu E. Error-constrained COUNT query evaluation in relational databases. ACM SIGMOD Record, 1991, 20(2): 278-287. Olken F. Random sampling from databases[Ph.D. Thesis]. University of California, 1993. Ngu A H, Harangsri B, Shepherd J. Query size estimation for joins using systematic sampling. Distributed and Parallel Databases, 2004, 15(3): 237-275. Lee H, Ng R T, Shim K. Similarity join size estimation using locality sensitive hashing. PVLDB, 2011, 4(6): 338-349. Tong X, Wang H. Fgram-Tree: An index structure based on feature grams for string approximate search. In Proc. the 13th WAIM, August 2012, pp.241-253. Liu X, Wang H, Li J, Gao H. Similarity join algorithm based on entity. Journal of Software, 2015, 26(6): 1421-1437. (in Chinese) Zhang Y, Yang L, Wang H. Range query estimation for dirty data management system. In Proc. the 13th WAIM, August 2012, pp.152-164. Liu X, Wang H, Li J, Gao H. Multi-similarity join order selection in entity database. Journal of Frontiers of Computer Science and Technology, 2012, 6(10): 865-876. Garcia-Molina H, Ullman J D, Widom J. Database System Implementation. Prentice-Hall, 2000. Abiteboul S, Hull R, Vianu V. Foundations of Databases. Addison-Wesley, 1995. Ilyas I F, Beskales G, Soliman M A. A survey of top-k query processing techniques in relational database systems. ACM Computing Surveys (CSUR), 2008, 40(4): 11:1-11:58. Zhang Y, Yang L, Wang H. Similarity join size estimation with threshold for dirty data. Journal of Computers, 2012, 35(10): 2159-2168. (in Chinese) Xu R, Wunsch D. Survey of clustering algorithms. IEEE Transactions on Neural Networks, 2005, 16(3): 645-678. Clauset A, Newman M E, Moore C. Finding community structure in very large networks. Physical Review E, 2004, 70(6): 66-111. Li Y, Wang H, Gao H. Efficient entity resolution based on sequence rules. In Proc. CSIE, May 2011, pp.381-388. Kuang D, Li X, Ling C X. A new search engine integrating hierarchical browsing and keyword search. In Proc. the 22nd IJCAI, July 2011, pp.2464-2469.