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Data Quality in a Big Data Context

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Book cover Advances in Databases and Information Systems (ADBIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11019))

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Abstract

In each of the phases of a Big Data analysis process, data quality (DQ) plays a key role. Given the particular characteristics of the data at hand, the traditional DQ methods used for relational databases, based on quality dimensions and metrics, must be adapted and extended, in order to capture the new characteristics that Big Data introduces. This paper dives into this problem, re-defining the DQ dimensions and metrics for a Big Data scenario, where data may arrive, for example, as unstructured documents in real time. This general scenario is instantiated to study the concrete case of Twitter feeds. Further, the paper also describes the implementation of a system that acquires tweets in real time, and computes the quality of each tweet, applying the quality metrics that are defined formally in the paper. The implementation includes a web user interface that allows filtering the tweets for example by keywords, and visualizing the quality of a data stream in many different ways. Experiments are performed and their results discussed.

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Notes

  1. 1.

    http://iso25000.com/index.php/en/iso-25000-standards/iso-25012.

  2. 2.

    http://www.twitter.com.

  3. 3.

    https://kafka.apache.org/.

  4. 4.

    https://zookeeper.apache.org/.

  5. 5.

    https://projects.spring.io/spring-boot/.

  6. 6.

    https://socket.io/.

  7. 7.

    The system is available upon request to the authors.

References

  1. Data, data everywhere (2008). https://www.economist.com/node/15557443

  2. English-words project (2018). https://github.com/dwyl/english-words

  3. Agrawal, D., Bernstein, P., Bertino, E., Davidson, S., Dayal, U.: Challenges and opportunities with big data (2011). https://docs.lib.purdue.edu/cgi/viewcontent.cgi?referer=www.google.com.ar/&httpsredir=1&article=1000&context=cctech

  4. Batini, C., Rula, A., Scannapieco, M., Viscusi, G.: From data quality to big data quality. J. Database Manag. 26(1), 60–82 (2015)

    Article  Google Scholar 

  5. Batini, C., Scannapieco, M.: Data Quality: Concepts Methodologies and Techniques. DCSA. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-33173-5

    Book  MATH  Google Scholar 

  6. Bolchini, C., Curino, C.A., Quintarelli, E., Schreiber, F.A., Tanca, L.: A data-oriented survey of context models. SIGMOD Rec. 36(4), 19–26 (2007). https://doi.org/10.1145/1361348.1361353

    Article  Google Scholar 

  7. Ciaccia, P., Torlone, R.: Modeling the propagation of user preferences. In: Jeusfeld, M., Delcambre, L., Ling, T.-W. (eds.) ER 2011. LNCS, vol. 6998, pp. 304–317. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24606-7_23

    Chapter  Google Scholar 

  8. Firmani, D., Mecella, M., Scannapieco, M., Batini, C.: On the meaningfulness of “big data quality” (invited paper). Data Sci. Eng. 1–15 (2015)

    Google Scholar 

  9. Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014). http://www.aclweb.org/anthology/P/P14/P14-5010

  10. Marotta, A., Vaisman, A.: Rule-based multidimensional data quality assessment using contexts. In: Madria, S., Hara, T. (eds.) DaWaK 2016. LNCS, vol. 9829, pp. 299–313. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43946-4_20

    Chapter  Google Scholar 

  11. Poeppelmann, D., Schultewolter, C.: Towards a data quality framework for decision support in a multidimensional context. IJBIR 3(1), 17–29 (2012)

    Google Scholar 

  12. Scannapieco, M., Catarci, T.: Data quality under a computer science perspective. Archivi Comput. 2, 1–15 (2002). https://www.fing.edu.uy/inco/cursos/caldatos/articulos/ArchiviComputer2002.pdf

    Google Scholar 

  13. Stefanidis, K., Pitoura, E., Vassiliadis, P.: Managing contextual preferences. Inf. Syst. 36(8), 1158–1180 (2011)

    Article  Google Scholar 

  14. Strong, D.M., Lee, Y.W., Wang, R.Y.: Data quality in context. Commun. ACM 40(5), 103–110 (1997). https://doi.org/10.1145/253769.253804

    Article  Google Scholar 

  15. Task Team on Big Data: Classification of types of big data (2007). https://statswiki.unece.org/display/bigdata/Classification+of+Types+of+Big+Data

  16. Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)

    Article  Google Scholar 

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Acknowledgments

Alejandro Vaisman was partially supported by the Argentinian Scientific Agency, PICT-2014 Project 0787.

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Authors and Affiliations

  1. Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina

    Franco Arolfo & Alejandro Vaisman

Authors
  1. Franco Arolfo
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  2. Alejandro Vaisman
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Correspondence to Alejandro Vaisman .

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Editors and Affiliations

  1. Eötvös Loránd University, Budapest, Hungary

    András Benczúr

  2. Christian-Albrechts-Universität, Kiel, Germany

    Bernhard Thalheim

  3. Eötvös Loránd University, Budapest, Hungary

    Tomáš Horváth

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Arolfo, F., Vaisman, A. (2018). Data Quality in a Big Data Context. In: Benczúr, A., Thalheim, B., Horváth, T. (eds) Advances in Databases and Information Systems. ADBIS 2018. Lecture Notes in Computer Science(), vol 11019. Springer, Cham. https://doi.org/10.1007/978-3-319-98398-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-98398-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98397-4

  • Online ISBN: 978-3-319-98398-1

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