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Profiling relational data: a survey

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Abstract

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.

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Notes

  1. See Sect. 6 for a more comprehensive list of tools.

  2. “Data gazing involves looking at the data and trying to reconstruct a story behind these data. [...] Data gazing mostly uses deduction and common sense.” [104]

  3. A more detailed regular expression, taking into account different formatting options and different restrictions (e.g., phone numbers cannot begin with a 1), can easily reach 200 characters in length.

  4. Differential dependencies also generalize matching dependencies [49] (if two tuples have close values of X, their A values must be exactly the same) and metric functional dependencies [89] (if two tuples have the same values of X, their A values must be close).

References

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  21. Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. SIGMOD Rec. 26(2), 265–276 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  23. Chandola, V., Kumar, V.: Summarization—compressing data into an informative representation. Knowl. Inf. Syst. 12(3), 355–378 (2007)

    Article  Google Scholar 

  24. Chiang, F., Miller, R.J.: Discovering data quality rules. Proc. VLDB Endow. 1, 1166–1177 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Book  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  47. Fan, W., Geerts, F., Li, J., Xiong, M.: Discovering conditional functional dependencies. IEEE Trans. Knowl. Data Eng. 23(4), 683–698 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  52. Flach, P.A., Savnik, I.: Database dependency discovery: a machine learning approach. AI Commun. 12(3), 139–160 (1999)

    MathSciNet  Google Scholar 

  53. Ganguly, S.: Counting distinct items over update streams. Theor. Comput. Sci. 378(3), 211–222 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  54. Garofalakis, M., Keren, D., Samoladas, V.: Sketch-based geometric monitoring of distributed stream queries. Proc. VLDB Endow. 6(10), 937–948 (2013)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  57. Golab, L., Karloff, H., Korn, F., Saha, A., Srivastava, D.: Sequential dependencies. Proc. VLDB Endow. 2(1), 574–585 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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)

    Article  Google Scholar 

  64. Gunopulos, D., Khardon, R., Mannila, H., Sharma, R.S.: Discovering all most specific sentences. ACM Trans. Database Syst. 28, 140–174 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  72. Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining—a general survey and comparison. SIGKDD Explor. 2(1), 58–64 (2000)

    Article  Google Scholar 

  73. Holmes, D.I.: Authorship attribution. Comput. Humanit. 28, 87–106 (1994)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  79. Johnson, T.: Encyclopedia of Database Systems, chapter Data Profiling. Springer, Heidelberg (2009)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  97. Lucchesi, C.L., Osborn, S.L.: Candidate keys for relations. J. Comput. Syst. Sci. 17(2), 270–279 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  104. Maydanchik, A.: Data Quality Assessment. Technics Publications, New Jersey (2007)

    Google Scholar 

  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)

    Article  Google Scholar 

  108. Naumann, F.: Data profiling revisited. SIGMOD Rec. 42(4), 40–49 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  112. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  121. Rahm, E., Do, H.-H.: Data cleaning: problems and current approaches. IEEE Data Eng. Bull. 23(4), 3–13 (2000)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

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Authors and Affiliations

  1. MIT CSAIL, Cambridge, MA, USA

    Ziawasch Abedjan

  2. University of Waterloo, Waterloo, Canada

    Lukasz Golab

  3. Hasso Plattner Institute, Potsdam, Germany

    Felix Naumann

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  1. Ziawasch Abedjan
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  2. Lukasz Golab
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  3. Felix Naumann
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Correspondence to Felix Naumann.

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Abedjan, Z., Golab, L. & Naumann, F. Profiling relational data: a survey. The VLDB Journal 24, 557–581 (2015). https://doi.org/10.1007/s00778-015-0389-y

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