Advertisement

Heuristic Algorithm for Recovering a Physical Structure of Spreadsheet Header

  • Viacheslav ParamonovEmail author
  • Alexey Shigarov
  • Varvara Vetrova
  • Andrey Mikhailov
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1050)

Abstract

Tables in electronic documents (spreadsheets) contain large volumes of useful information about different domains. Efficient extraction of data from document tables plays a crucial role in its further usage including analysis and integration. The visual or logical structure of table elements might differ from its physical structure. Such differences cause difficulties for automated table processing and understanding. Automated correction from physical form to visual allows to simplify tables processing operations. In this paper, we propose a heuristic approach for transformation of tables’ header cells. The main goal of the proposed approach is to provide an algorithm and software tool for recovering a physical structure of a spreadsheet header. The proposed approach is illustrated by application to the Statistical Abstract of the United States (SAUS) dataset.

Keywords

Spreadsheets Table structure Cells Heuristics Table layers 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Abraham, R., Erwig, M.: Header and unit inference for spreadsheets through spatial analyses. In: Proceedings of 2004 IEEE Symposium on Visual Languages and Human Centric Computing(VLHCC), pp. 165–172, September 2004.  https://doi.org/10.1109/VLHCC.2004.29
  2. 2.
    Cafarella, M.J., Halevy, A., Wang, D.Z., Wu, E., Zhang, Y.: Webtables: exploring the power of tables on the web. Proc. VLDB Endow. 1(1), 538–549 (2008).  https://doi.org/10.14778/1453856.1453916CrossRefGoogle Scholar
  3. 3.
    Cunha, J., Fernandes, J.P., Mendes, J., Saraiva, J.: Spreadsheet engineering. In: Central European Functional Programming School - 5th Summer School, CEFP 2013, Cluj-Napoca, Romania, pp. 246–299, 8–20 July 2013.  https://doi.org/10.1007/978-3-319-15940-9_6Google Scholar
  4. 4.
    Eberius, J., Werner, C., Thiele, M., Braunschweig, K., Dannecker, L., Lehner, W.: Deexcelerator: a framework for extracting relational data from partially structured documents. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management CIKM 2013, pp. 2477–2480. ACM, New York (2013).  https://doi.org/10.1145/2505515.2508210
  5. 5.
    Embley, D.W., Hurst, M., Lopresti, D., Nagy, G.: Table-processing paradigms: a research survey. Int. J. Doc. Anal. Recogn. (IJDAR) 8, 66–86 (2006).  https://doi.org/10.1007/s10032-006-0017-xCrossRefGoogle Scholar
  6. 6.
    Koci, E., Thiele, M., Romero, O., Lehner, W.: Table identification and reconstruction in spreadsheets. In: Dubois, E., Pohl, K. (eds.) Advanced Information Systems Engineering, pp. 527–541. Springer, Cham (2017)CrossRefGoogle Scholar
  7. 7.
    Koci, E., Thiele, M., Romero, O., Lehner, W.: Cell classification for layout recognition in spreadsheets. In: 8th International Joint Conference, 9–11 November 2016, IC3K 2016, Porto, Portugal, pp. 78–100, January 2019.  https://doi.org/10.1007/978-3-319-99701-8Google Scholar
  8. 8.
    Nagy, G., Seth, S.C.: Table headers: an entrance to the data mine. In: 23rd International Conference on Pattern Recognition, ICPR 2016, Cancún, Mexico, pp. 4065–4070, 4–8 December 2016.  https://doi.org/10.1109/ICPR.2016.7900270
  9. 9.
    Panko, R.R.: Spreadsheet errors: What we know. what we think we can do. CoRR abs/0802.3457 (2008)Google Scholar
  10. 10.
    Pasupat, P., Liang, P.: Compositional semantic parsing on semi-structured tables. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1470–1480. Association for Computational Linguistics (2015).  https://doi.org/10.3115/v1/P15-1142
  11. 11.
    Rastan, R., Paik, H.Y., Shepherd, J., Ryu, S.H., Beheshti, A.: Texus: table extraction system for pdf documents. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds.) Databases Theory and Applications, pp. 345–349. Springer, Cham (2018)CrossRefGoogle Scholar
  12. 12.
    REASON, J.: Human error, pp. XV, 301, p. ill. 23 cm (1994). http://infoscience.epfl.ch/record/2249. bibliogr.: p. 258–290. Index
  13. 13.
    Shigarov, A., Altaev, A., Mikhailov, A., Paramonov, V., Cherkashin, E.: Tabbypdf: web-based system for pdf table extraction. In: Damaševičius, R., Vasiljevienė, G. (eds.) Information and Software Technologies, pp. 257–269. Springer, Cham (2018)CrossRefGoogle Scholar
  14. 14.
    Shigarov, A.O., Mikhailov, A.A.: Rule-based spreadsheet data transformation from arbitrary to relational tables. Inf. Syst. 71, 123–136 (2017).  https://doi.org/10.1016/j.is.2017.08.004CrossRefGoogle Scholar
  15. 15.
    Shigarov, A.O., Paramonov, V.V., Belykh, P.V., Bondarev, A.I.: Rule-based canonicalization of arbitrary tables in spreadsheets. In: Dregvaite, G., Damasevicius, R. (eds.) Information and Software Technologies, pp. 78–91. Springer, Cham (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of SciencesIrkutskRussia
  2. 2.School of Mathematics and StatisticsUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations

Cite paper

Buy options