Profiling relational data: a survey

VLDB Journal

Volumn 24, Issue 4, 2015, Pages 557-581

  Abedjan, Ziawasch ^a   Golab, Lukasz ^b   Naumann, Felix ^c  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF WATERLOO   (Canada)

^c UNIVERSITY OF POTSDAM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

INDUSTRIAL RESEARCH; SURVEYS;

CLASSIFICATION OF DATA; COLUMN COMBINATIONS; FUNCTIONAL DEPENDENCY; INCLUSION DEPENDENCIES; IT PROFESSIONAL; MULTIPLE COLUMNS; RELATIONAL DATABASE; STATE OF THE ART;

METADATA;

EID: 84937966957     PISSN: 10668888     EISSN: 0949877X     Source Type: Journal    
DOI: 10.1007/s00778-015-0389-y     Document Type: Article

References (144)

1. Abedjan, Z., Grütze, T., Jentzsch, A., Naumann, F.: Mining and profiling RDF data with ProLOD++. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1198–1201 (2014). Demo
2. Abedjan, Z., Lorey, J., Naumann, F.: Reconciling ontologies and the web of data. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 1532–1536 (2012)
3. Abedjan, Z., Naumann, F.: Advancing the discovery of unique column combinations. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 1565–1570 (2011)
4. Abedjan, Z., Naumann, F.: Synonym analysis for predicate expansion. In: Proceedings of the Extended Semantic Web Conference (ESWC), pp. 140–154 (2013)
5. Abedjan, Z., Quiané-Ruiz, J.-A., Naumann, F.: Detecting unique column combinations on dynamic data. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1036–1047 (2014)
6. Abedjan, Z., Schulze, P., Naumann, F.: DFD: efficient functional dependency discovery. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 949–958 (2014)
7. Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U., Franklin, M., Gehrke, J., Haas, L., Halevy, A., Han, J., Jagadish, H.V., Labrinidis, A., Madden, S., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Ross, K., Shahabi, C., Suciu, D., Vaithyanathan, S., Widom, J.: Challenges and opportunities with Big Data. Technical report, Computing Community Consortium. http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf (2012)
8. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 487–499 (1994)
9. Andritsos, P., Miller, R.J., Tsaparas, P.: Information-theoretic tools for mining database structure from large data sets. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 731–742 (2004)
10. Arenas, M., Daenen, J., Neven, F., Ugarte, M., Van den Bussche, J., Vansummeren, S.: Discovering XSD keys from XML data. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 61–72 (2013)
11. Astrahan, M.M., Schkolnick, M., Kyu-Young, W.: Approximating the number of unique values of an attribute without sorting. Inf. Syst. 12(1), 11–15 (1987)
12. Auer, S., Demter, J., Martin, M., Lehmann, J.: LODStats—an extensible framework for high-performance dataset analytics. In: Proceedings of the International Conference on Knowledge Engineering and Knowledge Management (EKAW), pp. 353–362 (2012)
13. Bauckmann, J., Abedjan, Z., Müller, H., Leser, U., Naumann, F.: Discovering conditional inclusion dependencies. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 2094–2098 (2012)
14. Bauckmann, J., Leser, U., Naumann, F., Tietz, V.: Efficiently detecting inclusion dependencies. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1448–1450 (2007)
15. Benford, F.: The law of anomalous numbers. Proc. Am. Philos. Soc. 78(4), 551–572 (1938)
16. Berti-Equille, L., Dasu, T., Srivastava, D.: Discovery of complex glitch patterns: a novel approach to quantitative data cleaning. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 733–744 (2011)
17. Bex, G.J., Neven, F., Vansummeren, S.: Inferring XML schema definitions from XML data. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 998–1009 (2007)
18. Böhm, C., Lorey, J., Naumann, F.: Creating void descriptions for web-scale data. J. Web Semant. 9(3), 339–345 (2011)
19. Bravo, L., Fan, W., Ma, S.: Extending dependencies with conditions. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 243–254 (2007)
20. Brill, E.: Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput. Linguist. 21(4), 543–565 (1995)
21. Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. SIGMOD Rec. 26(2), 265–276 (1997)
22. Buneman, P., Davidson, S.B., Fan, W., Hara, C.S., Tan, W.C.: Reasoning about keys for XML. Inf. Syst. 28(8), 1037–1063 (2003)
23. Chandola, V., Kumar, V.: Summarization—compressing data into an informative representation. Knowl. Inf. Syst. 12(3), 355–378 (2007)
24. Chiang, F., Miller, R.J.: Discovering data quality rules. Proc. VLDB Endow. 1, 1166–1177 (2008)
25. Chiang, R.H.L., Cecil, C.E.H., Lim, E.-P.: Linear correlation discovery in databases: a data mining approach. Data Knowl. Eng. 53(3), 311–337 (2005)
26. Choi, B.: What are real DTDs like? In: Proceedings of the ACM SIGMOD Workshop on the Web and Databases (WebDB), pp. 43–48 (2002)
27. Christen, P.: Data Matching. Springer, Berlin (2012)
28. Chu, X., Ilyas, I., Papotti, P., Ye, Y.: RuleMiner: data quality rules discovery. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1222–1225 (2014)
29. Chu, X., Ilyas, I.F., Papotti, P.: Discovering denial constraints. Proc. VLDB Endow. 6(13), 1498–1509 (2013)
30. Cong, G., Fan, W., Geerts, F., Jia, X., Ma, S.: Improving data quality: consistency and accuracy. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 315–326 (2007)
31. Cormode, G., Garofalakis, M., Haas, P.J., Jermaine, C.: Synopses for massive data: samples, histograms, wavelets, sketches. Found. Trends Databases 4(13), 1–294 (2011)
32. Cormode, G., Golab, L., Flip, K., McGregor, A., Srivastava, D., Zhang, X.: Estimating the confidence of conditional functional dependencies. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 469–482 (2009)
33. Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Space- and time-efficient deterministic algorithms for biased quantiles over data streams. In: Proceedings of the Symposium on Principles of Database Systems (PODS), pp. 263–272 (2006)
34. Dallachiesa, M., Ebaid, A., Eldawy, A., Elmagarmid, A., Ilyas, I.F., Ouzzani, M., Tang, N.: NADEEF: a commodity data cleaning system. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 541–552 (2013)
35. Das, A., Ng, W.-K., Woon, Y.-K.: Rapid association rule mining. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 474–481 (2001)
36. Dasu, T., Johnson, T.: Hunting of the snark: finding data glitches using data mining methods. In: Proceedings of the International Conference on Information Quality (IQ), pp. 89–98 (1999)
37. Dasu, T., Johnson, T., Marathe, A.: Database exploration using database dynamics. IEEE Data Eng. Bull. 29(2), 43–59 (2006)
38. Dasu, T., Johnson, T., Muthukrishnan, S., Shkapenyuk, V.: Mining database structure; or, how to build a data quality browser. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 240–251 (2002)
39. Dasu, T., Loh, J.M.: Statistical distortion: consequences of data cleaning. Proc. VLDB Endow. 5(11), 1674–1683 (2012)
40. Dasu, T., Loh, J.M., Srivastava, D.: Empirical glitch explanations. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 572–581 (2014)
41. Deshpande, A., Garofalakis, M., Rastogi, R.: Independence is good: dependency-based histogram synopses for high-dimensional data. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 199–210 (2001)
42. Diallo, T., Novelli, N., Petit, J.-M.: Discovering (frequent) constant conditional functional dependencies. Int. J. Data Min. Model. Manag. 4(3), 205–223 (2012)
43. Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 323–333 (1998)
44. Euzenat, J., Shvaiko, P.: Ontology Matching, 2nd edn. Springer, Berlin (2013)
45. Fan, W., Geerts, F., Jia, X.: Semandaq: a data quality system based on conditional functional dependencies. Proc. VLDB Endow. 1(2), 1460–1463 (2008)
46. Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for capturing data inconsistencies. ACM Trans. Database Syst. 33(2), 1–48 (2008)
47. Fan, W., Geerts, F., Li, J., Xiong, M.: Discovering conditional functional dependencies. IEEE Trans. Knowl. Data Eng. 23(4), 683–698 (2011)
48. Fan, W., Geerts, F., Ma, S., Müller, H.: Detecting inconsistencies in distributed data. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 64–75 (2010)
49. Fan, W., Jia, X., Li, J., Ma, S.: Reasoning about record matching rules. Proc. VLDB Endow. 2(1), 407–418 (2009)
50. Fan, W., Li, J., Tang, N., Yu, W.: Incremental detection of inconsistencies in distributed data. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 318–329 (2012)
51. Fernau, H.: Algorithms for learning regular expressions from positive data. Inf. Comput. 207(4), 521–541 (2009)
52. Flach, P.A., Savnik, I.: Database dependency discovery: a machine learning approach. AI Commun. 12(3), 139–160 (1999)
53. Ganguly, S.: Counting distinct items over update streams. Theor. Comput. Sci. 378(3), 211–222 (2007)
54. Garofalakis, M., Keren, D., Samoladas, V.: Sketch-based geometric monitoring of distributed stream queries. Proc. VLDB Endow. 6(10), 937–948 (2013)
55. Giannella, C., Wyss, C.: Finding minimal keys in a relation instance (1999). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=?doi=10.1.1.41.7086
56. Ginsburg, S., Hull, R.: Order dependency in the relational model. Theor. Comput. Sci. 26, 149–195 (1983)
57. Golab, L., Karloff, H., Korn, F., Saha, A., Srivastava, D.: Sequential dependencies. Proc. VLDB Endow. 2(1), 574–585 (2009)
58. Golab, L., Karloff, H., Korn, F., Srivastava, D.: Data auditor: exploring data quality and semantics using pattern tableaux. Proc. VLDB Endow. 3(1–2), 1641–1644 (2010)
59. Golab, L., Karloff, H., Korn, F., Srivastava, D., Bei, Y.: On generating near-optimal tableaux for conditional functional dependencies. Proc. VLDB Endow. 1(1), 376–390 (2008)
60. Golab, L., Korn, F., Srivastava, D.: Discovering pattern tableaux for data quality analysis: a case study. In: Proceedings of the International Workshop on Quality in Databases (QDB), pp. 47–53 (2011)
61. Golab, L., Korn, F., Srivastava, D.: Efficient and effective analysis of data quality using pattern tableaux. IEEE Data Eng. Bull. 34(3), 26–33 (2011)
62. Grahne, G., Zhu, J.: Discovering approximate keys in XML data. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 453–460 (2002)
63. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)
64. Gunopulos, D., Khardon, R., Mannila, H., Sharma, R.S.: Discovering all most specific sentences. ACM Trans. Database Syst. 28, 140–174 (2003)
65. Haas, P.J., Naughton, J.F., Seshadri, S., Stokes, L.: Sampling-based estimation of the number of distinct values of an attribute. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 311–322 (1995)
66. Hainaut, J.-L., Henrard, J., Englebert, V., Roland, D., Hick, J.-M.: Database reverse engineering. In: Liu, L., Tamer Özsu, M. (eds.) Encyclopedia of Database Systems, pp. 723–728. Springer, Heidelberg (2009)
67. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)
68. Hanrahan, P.: Analytic database technology for a new kind of user—the data enthusiast (keynote). In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 577–578 (2012)
69. Hegewald, J., Naumann, F., Weis, M.: XStruct: efficient schema extraction from multiple and large XML databases. In: Proceedings of the International Workshop on Database Interoperability (InterDB) (2006)
70. Heise, A., Quiané-Ruiz, J.-A., Abedjan, Z., Jentzsch, A., Naumann, F.: Scalable discovery of unique column combinations. Proc. VLDB Endow. 7(4), 301–312 (2013)
71. Hellerstein, J.M., Ré, C., Schoppmann, F., Wang, D.Z., Fratkin, E., Gorajek, A., Ng, K.S., Welton, C., Feng, X., Li, K., Kumar, A.: The MADlib analytics library or MAD skills, the SQL. Proc. VLDB Endow. 5(12), 1700–1711 (2012)
72. Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining—a general survey and comparison. SIGKDD Explor. 2(1), 58–64 (2000)
73. Holmes, D.I.: Authorship attribution. Comput. Humanit. 28, 87–106 (1994)
74. Hua, M., Pei, J.: Cleaning disguised missing data: a heuristic approach. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 950–958 (2007)
75. Huhtala, Y., Kärkkäinen, J., Porkka, P., Toivonen, H.: TANE: an efficient algorithm for discovering functional and approximate dependencies. Comput. J. 42(2), 100–111 (1999)
76. Ilyas, I.F., Markl, V., Haas, P.J., Brown, P., Aboulnaga, A.: CORDS: automatic discovery of correlations and soft functional dependencies. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 647–658 (2004)
77. Ioannidis, Y.: The history of histograms (abridged). In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 19–30 (2003)
78. Jain, A.K., Narasimha Murty, M., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
79. Johnson, T.: Encyclopedia of Database Systems, chapter Data Profiling. Springer, Heidelberg (2009)
80. Kache, H., Han, W.-S., Markl, V., Raman, V., Ewen, S.: POP/FED: progressive query optimization for federated queries in DB2. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 1175–1178 (2006)
81. Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J., Heer, J.: Profiler: integrated statistical analysis and visualization for data quality assessment. In: Proceedings of Advanced Visual Interfaces (AVI), pp. 547–554 (2012)
82. Kang, J., Naughton, J.F.: On schema matching with opaque column names and data values. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 205–216 (2003)
83. Keim, D.A., Oelke, D.: Literature fingerprinting: a new method for visual literary analysis. In: Proceedings of Visual Analytics Science and Technology (VAST), pp. 115–122 (2007)
84. Khoussainova, N., Balazinska, M., Suciu, D.: Towards correcting input data errors probabilistically using integrity constraints. In: Proceedings of the ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE), pp. 43–50 (2006)
85. Kivinen, J., Mannila, H.: Approximate inference of functional dependencies from relations. In: Proceedings of the International Conference on Database Theory (ICDT), pp. 129–149 (1995)
86. Koehler, H., Leck, U., Link, S., Prade, H.: Logical foundations of possibilistic keys. In: Fermé, E., Leite, J. (eds.) Logics in Artificial Intelligence, volume 8761 of Lecture Notes in Computer Science, pp. 181–195. Springer, Heidelberg (2014)
87. Koeller, A., Rundensteiner, E.A.: Heuristic strategies for the discovery of inclusion dependencies and other patterns. J. Data Semant. V. 3870, 185–210 (2006)
88. Korn, F., Saha, B., Srivastava, D., Ying, S.: On repairing structural problems in semi-structured data. Proc. VLDB Endow. 6(9), 601–612 (2013)
89. Koudas, N., Saha, A., Srivastava, D., Venkatasubramanian, S.: Metric functional dependencies. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 1275–1278 (2009)
90. Laney, D.: 3D data management: controlling data volume, velocity and variety. Technical report, Gartner (2001)
91. Li, J., Liu, J., Toivonen, H., Yong, J.: Effective pruning for the discovery of conditional functional dependencies. Comput. J. 56(3), 378–392 (2013)
92. Li, Y., Krishnamurthy, R., Raghavan, S., Vaithyanathan, S., Jagadish, H.V.: Regular expression learning for information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 21–30 (2008)
93. Liu, B.: Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2nd edn. Chapman and Hall/CRC, London (2010)
94. Liu, J., Li, J., Liu, C., Chen, Y.: Discover dependencies from data—a review. IEEE Trans. Knowl. Data Eng. 24(2), 251–264 (2012)
95. Lopes, S., Petit, J.-M., Lakhal, L.: Efficient discovery of functional dependencies and Armstrong relations. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. 350–364 (2000)
96. Lopes, S., Petit, J.-M., Toumani, F.: Discovering interesting inclusion dependencies: application to logical database tuning. Inf. Syst. 27(1), 1–19 (2002)
97. Lucchesi, C.L., Osborn, S.L.: Candidate keys for relations. J. Comput. Syst. Sci. 17(2), 270–279 (1978)
98. Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with Cupid. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 49–58 (2001)
99. Mannino, M.V., Chu, P., Sager, T.: Statistical profile estimation in database systems. ACM Comput. Surv. 20(3), 191–221 (1988)
100. De Marchi, F., Lopes, S., Petit, J.-M.: Efficient algorithms for mining inclusion dependencies. In: Proceedings of the International Conference on Extending Database Technology (EDBT), pp. 464–476 (2002)
101. De Marchi, F., Lopes, S., Petit, J.-M.: Unary and n-ary inclusion dependency discovery in relational databases. J. Intell. Inf. Syst. 32, 53–73 (2009)
102. De Marchi, F., Petit, J.-M.: Zigzag: a new algorithm for mining large inclusion dependencies in databases. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 27–34 (2003)
103. Markowitz, V.M., Makowsky, J.A.: Identifying extended entity-relationship object structures in relational schemas. IEEE Trans. Softw. Eng. 16(8), 777–790 (1990)
104. Maydanchik, A.: Data Quality Assessment. Technics Publications, New Jersey (2007)
105. Mignet, L., Barbosa, D., Veltri, P.: The XML web: a first study. In: Proceedings of the International World Wide Web Conference (WWW), pp. 500–510 (2003)
106. Mlynkova, I., Toman, K., Pokorný, J.: Statistical analysis of real XML data collections. In: Proceedings of the International Conference on Management of Data (COMAD), pp. 15–26 (2006)
107. Morton, K., Balazinska, M., Grossman, D., Mackinlay, J.: Support the data enthusiast: challenges for next-generation data-analysis systems. Proc. VLDB Endow. 7(6), 453–456 (2014)
108. Naumann, F.: Data profiling revisited. SIGMOD Rec. 42(4), 40–49 (2013)
109. Naumann, F., Ho, C.-T., Tian, X., Haas, L., Megiddo, N.: Attribute classification using feature analysis. In: Proceedings of the International Conference on Data Engineering (ICDE), p 271 (2002)
110. Novelli, N., Cicchetti, R.: FUN: an efficient algorithm for mining functional and embedded dependencies. In: Proceedings of the International Conference on Database Theory (ICDT), pp. 189–203 (2001)
111. Ntarmos, N., Triantafillou, P., Weikum, G.: Distributed hash sketches: scalable, efficient, and accurate cardinality estimation for distributed multisets. ACM Trans. Comput. Syst. 27(1), 1–53 (2009)
112. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)
113. Papenbrock, T., Ehrlich, J., Marten, J., Neubert, T., Rudolph, J.-P., Schönberg, M., Zwiener, J., Naumann, F.: Functional dependency discovery: an experimental evaluation of seven algorithms. Proc. VLDB Endow. 8(10) (2015)
114. Papenbrock, T., Kruse, S., Quiané-Ruiz, J.-A., Naumann, F.: Divide & conquer-based inclusion dependency discovery. Proc. VLDB Endow. 8(7), 774–785 (2015)
115. Park, J.S., Chen, M.-S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules. IEEE Trans. Knowl. Data Eng. 9, 813–825 (1997)
116. Petit, J.-M., Kouloumdjian, J., Boulicaut, J.-F., Toumani, F.: Using queries to improve database reverse engineering. In: Proceedings of the International Conference on Conceptual Modeling (ER), pp. 369–386 (1994)
117. Pipino, L., Lee, Y., Wang, R.: Data quality assessment. Commun. ACM 4, 211–218 (2002)
118. Poosala, V., Haas, P.J., Ioannidis, Y.E., Shekita, E.J.: Improved histograms for selectivity estimation of range predicates. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 294–305 (1996)
119. Poosala, V., Ioannidis, Y.E.: Selectivity estimation without the attribute value independence assumption. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 486–495 (1997)
120. Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, Burlington (1999)
121. Rahm, E., Do, H.-H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)
122. Raman, V., Hellerstein, J.M.: Potters wheel: an interactive data cleaning system. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 381–390 (2001)
123. Rostin, A., Albrecht, O., Bauckmann, J., Naumann, F., Leser, U.: A machine learning approach to foreign key discovery. In: Proceedings of the ACM SIGMOD Workshop on the Web and Databases (WebDB) (2009)
124. Sahuguet, A., Azavant, F.: Building light-weight wrappers for legacy Web data-sources using W4F. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 738–741 (1999)
125. Sarawagi, S.: Information extraction. Found. Trends Databases 1(3), 261–377 (2008)
126. Sismanis, Y., Brown, P., Haas, P.J., Reinwald, B.: GORDIAN: efficient and scalable discovery of composite keys. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 691–702 (2006)
127. Smith, K.P., Morse, M., Mork, P., Li, M.H., Rosenthal, A., Allen, M.D., Seligman, L.: The role of schema matching in large enterprises. In: Proceedings of the Conference on Innovative Data Systems Research (CIDR) (2009)
128. Song, S., Chen, L.: Differential dependencies: reasoning and discovery. ACM Trans. Database Syst. 36(3), 16:1–16:41 (2011)
129. Stonebraker, M., Bruckner, D., Ilyas, I.F., Beskales, G., Cherniack, M., Zdonik, S., Pagan, A., Xu, S.: Data curation at scale: the Data Tamer system. In: Proceedings of the Conference on Innovative Data Systems Research (CIDR) (2013)
130. Chen, M., Hun, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8, 866–883 (1996)
131. Tsai, P.S.M., Lee, C.-C., Chen, A.L.P.: An efficient approach for incremental association rule mining. Methodologies for Knowledge Discovery and Data Mining. volume 1574 of Lecture Notes in Computer Science, pp. 74–83. Springer, Heidelberg (1999)
132. Vincent, M.W., Liu, J., Liu, C.: Strong functional dependencies and their application to normal forms in XML. ACM Trans. Database Syst. 29(3), 445–462 (2004)
133. Vogel, T., Naumann, F.: Instance-based “one-to-some” assignment of similarity measures to attributes. In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS), pp. 412–420 (2011)
134. Wang, S.-L., Tsou, W.-C., Lin, J.-H., Hong, T.-P.: Maintenance of discovered functional dependencies: incremental deletion. Intelligent Systems Design and Applications, volume 23 of Advances in Soft Computing, pp. 579–588. Springer, Heidelberg (2003)
135. Xindong, W., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Trans. Inf. Syst. 22(3), 381–405 (2004)
136. Wyss, C., Giannella, C., Robertson, E.L.: FastFDs: a heuristic-driven, depth-first algorithm for mining functional dependencies from relation instances. In: Proceedings of the International Conference on Data Warehousing and Knowledge Discovery (DaWaK), pp. 101–110 (2001)
137. Xu, R., Wunsch II, D.C.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
138. Yakout, M., Elmagarmid, A.K., Neville, J., Ouzzani, M.: GDR: a system for guided data repair. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 1223–1226 (2010)
139. Yao, H., Hamilton, H.J.: Mining functional dependencies from data. Data Min. Knowl. Discov. 16(2), 197–219 (2008)
140. Yu, C., Jagadish, H.V.: Efficient discovery of XML data redundancies. In: Proceedings of the International Conference on Very Large Databases (VLDB), pp. 103–114 (2006)
141. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)
142. Zhang, M., Chakrabarti, K.: InfoGather+: semantic matching and annotation of numeric and time-varying attributes in web tables. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 145–156 (2013)
143. Zhang, M., Hadjieleftheriou, M., Ooi, B.C., Procopiuc, C.M., Srivastava, D.: On multi-column foreign key discovery. Proc. VLDB Endow. 3(1–2), 805–814 (2010)
144. Zhang, M., Hadjieleftheriou, M., Ooi, B.C., Procopiuc, C.M., Srivastava, D.: Automatic discovery of attributes in relational databases. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 109–120 (2011)