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An Introduction to Data Profiling

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Business Intelligence and Big Data (eBISS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 324))

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Abstract

One of the crucial requirements before consuming datasets for any application is to understand the dataset at hand and its metadata. The process of metadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of metadata via profiling algorithms. In this course, we will discuss the importance of data profiling as part of any data-related use-case, and shed light on the area of data profiling by classifying data profiling tasks and reviewing the state-of-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and their application in data management and data analytics. We conclude with directions for future research in the area of data profiling.

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Notes

  1. 1.

    https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2c61c6c56f63.

References

  1. Abedjan, Z., Golab, L., Naumann, F.: Profiling relational data: a survey. VLDB J. 24(4), 557–581 (2015)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. 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 (2012). http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf

  5. 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)

    Google Scholar 

  6. 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 

  7. 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)

    Google Scholar 

  8. Benford, F.: The law of anomalous numbers. Proc. Am. Philos. Soc. 78(4), 551–572 (1938)

    MATH  Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

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

    Article  Google Scholar 

  12. Caruccio, L., Deufemia, V., Polese, G.: Relaxed functional dependencies - a survey of approaches. IEEE Trans. Knowl. Data Eng. (TKDE) 28(1), 147–165 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. 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)

    Google Scholar 

  15. Cormode, G., Garofalakis, M., Haas, P.J., Jermaine, C.: Synopses for massive data: samples, histograms, wavelets, sketches. Found. Trends Databases 4(1–3), 1–294 (2011)

    Article  Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. Dasu, T., Johnson, T., Marathe, A.: Database exploration using database dynamics. IEEE Data Eng. Bull. 29(2), 43–59 (2006)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. Dasu, T., Loh, J.M.: Statistical distortion: consequences of data cleaning. Proc. VLDB Endowment (PVLDB) 5(11), 1674–1683 (2012)

    Article  Google Scholar 

  21. Fan, W., Geerts, F., Jia, X., Kementsietsidis, A.: Conditional functional dependencies for capturing data inconsistencies. ACM Trans. Database Syst. (TODS) 33(2), 1–48 (2008)

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

  23. Garofalakis, M., Keren, D., Samoladas, V.: Sketch-based geometric monitoring of distributed stream queries. Proc. VLDB Endowment (PVLDB) 6(10) (2013)

    Article  Google Scholar 

  24. 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

  25. Golab, L., Karloff, H., Korn, F., Srivastava, D.: Data auditor: exploring data quality and semantics using pattern tableaux. Proc. VLDB Endowment (PVLDB) 3(1–2), 1641–1644 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. 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)

    Google Scholar 

  28. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)

    Article  Google Scholar 

  29. Heise, A., Quiané-Ruiz, J.-A., Abedjan, Z., Jentzsch, A., Naumann, F.: Scalable discovery of unique column combinations. Proc. VLDB Endowment (PVLDB) 7(4), 301–312 (2013)

    Article  Google Scholar 

  30. 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 Endowment (PVLDB) 5(12), 1700–1711 (2012)

    Article  Google Scholar 

  31. 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 

  32. 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  Google Scholar 

  33. 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)

    Google Scholar 

  34. 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)

    Google Scholar 

  35. 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)

    Google Scholar 

  36. 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)

    Google Scholar 

  37. Koehler, H., Leck, U., Link, S., Prade, H.: Logical foundations of possibilistic keys. In: Fermé, E., Leite, J. (eds.) JELIA 2014. LNCS (LNAI), vol. 8761, pp. 181–195. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11558-0_13

    Chapter  Google Scholar 

  38. Koeller, A., Rundensteiner, E.A.: Heuristic strategies for the discovery of inclusion dependencies and other patterns. In: Spaccapietra, S., Atzeni, P., Chu, W.W., Catarci, T., Sycara, K.P. (eds.) Journal on Data Semantics V. LNCS, vol. 3870, pp. 185–210. Springer, Heidelberg (2006). https://doi.org/10.1007/11617808_7

    Chapter  Google Scholar 

  39. Lopes, S., Petit, J.-M., Lakhal, L.: Efficient discovery of functional dependencies and armstrong relations. In: Zaniolo, C., Lockemann, P.C., Scholl, M.H., Grust, T. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 350–364. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-46439-5_24

    Chapter  Google Scholar 

  40. Lopes, S., Petit, J.-M., Toumani, F.: Discovering interesting inclusion dependencies: application to logical database tuning. Inf. Syst. 27(1), 1–19 (2002)

    Article  Google Scholar 

  41. Mannino, M.V., Chu, P., Sager, T.: Statistical profile estimation in database systems. ACM Comput. Surv. 20(3), 191–221 (1988)

    Article  Google Scholar 

  42. De Marchi, F., Lopes, S., Petit, J.-M.: Efficient algorithms for mining inclusion dependencies. In: Jensen, C.S., et al. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 464–476. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45876-X_30

    Chapter  Google Scholar 

  43. 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 

  44. 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)

    Google Scholar 

  45. Morton, K., Balazinska, M., Grossman, D., Mackinlay, J.: Support the data enthusiast: challenges for next-generation data-analysis systems. Proc. VLDB Endowment (PVLDB) 7(6), 453–456 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Novelli, N., Cicchetti, R.: FUN: an efficient algorithm for mining functional and embedded dependencies. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 189–203. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44503-X_13

    Chapter  Google Scholar 

  48. Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. Proc. VLDB Endowment (PVLDB) 8(12), 1860–1871 (2015)

    Article  Google Scholar 

  49. 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 Endowment (PVLDB) 8(10) (2015)

    Article  Google Scholar 

  50. Papenbrock, T., Kruse, S., Quiané-Ruiz, J.-A., Naumann, F.: Divide & conquer-based inclusion dependency discovery. Proc. VLDB Endowment (PVLDB) 8(7) (2015)

    Article  Google Scholar 

  51. Papenbrock, T., Naumann, F.: A hybrid approach to functional dependency discovery. In: Proceedings of the International Conference on Management of Data (SIGMOD), pp. 821–833 (2016)

    Google Scholar 

  52. 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)

    Google Scholar 

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

    Google Scholar 

  54. 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)

    Google Scholar 

  55. 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)

    Google Scholar 

  56. 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)

    Google Scholar 

  57. 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)

    Google Scholar 

  58. Chen, M.S., Hun, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. (TKDE) 8, 866–883 (1996)

    Article  Google Scholar 

  59. Wyss, C., Giannella, C., Robertson, E.: FastFDs: a heuristic-driven, depth-first algorithm for mining functional dependencies from relation instances extended abstract. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 101–110. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44801-2_11

    Chapter  Google Scholar 

  60. 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)

    Google Scholar 

  61. Yao, H., Hamilton, H.J.: Mining functional dependencies from data. Data Min. Knowl. Disc. 16(2), 197–219 (2008)

    Article  MathSciNet  Google Scholar 

  62. Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. (TKDE) 12(3), 372–390 (2000)

    Article  Google Scholar 

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  1. TU Berlin, Berlin, Germany

    Ziawasch Abedjan

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  1. Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

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Abedjan, Z. (2018). An Introduction to Data Profiling. In: Zimányi, E. (eds) Business Intelligence and Big Data. eBISS 2017. Lecture Notes in Business Information Processing, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-319-96655-7_1

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