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TabbyXL: Rule-Based Spreadsheet Data Extraction and Transformation

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Information and Software Technologies (ICIST 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1078))

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Abstract

This paper presents an approach to rule-based spreadsheet data extraction and transformation. We determine a table object model and domain-specific language of table analysis and interpretation rules. In contrast to the existing data transformation languages, we draw up this process as consecutive steps: role analysis, structural analysis, and interpretation. To the best of our knowledge, there are no languages for expressing rules for transforming tabular data into the relational form in terms of the table understanding. We also consider a tool for transforming spreadsheet data from arbitrary to relational tables. The performance evaluation has been done automatically for both (role and structural) stages of table analysis with the prepared ground-truth data. It shows high F-score from 95.82% to 99.04% for different recovered items in the existing dataset of 200 arbitrary tables of the same genre (government statistics).

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Notes

  1. 1.

    http://commoncrawl.org.

  2. 2.

    http://lemurproject.org/clueweb09.

  3. 3.

    https://github.com/tabbydoc/tabbyxl2.

  4. 4.

    http://www.antlr.org.

  5. 5.

    https://www.drools.org.

  6. 6.

    http://www.jessrules.com.

  7. 7.

    https://github.com/tabbydoc/tabbyxl2/blob/master/src/main/resources/crl_gram.g.

  8. 8.

    https://www.jcp.org/ja/jsr/detail?id=94.

  9. 9.

    https://data.mendeley.com/datasets/ydcr7mcrtp/3.

References

  1. Astrakhantsev, N., Turdakov, D., Vassilieva, N.: Semi-automatic data extraction from tables. In: Selected Papers of the 15th All-Russian Scientific Conference on Digital Libraries: Advanced Methods and Technologies, Digital Collections, pp. 14–20 (2013)

    Google Scholar 

  2. Barik, T., Lubick, K., Smith, J., Slankas, J., Murphy-Hill, E.: Fuse: a reproducible, extendable, internet-scale corpus of spreadsheets. In: Proceedings of the 12th Working Conference on Mining Software Repositories, pp. 486–489. IEEE Press (2015). https://doi.org/10.1109/MSR.2015.70

  3. Barowy, D.W., Gulwani, S., Hart, T., Zorn, B.: FlashRelate: extracting relational data from semi-structured spreadsheets using examples. SIGPLAN Not. 50(6), 218–228 (2015). https://doi.org/10.1145/2813885.2737952

    Article  Google Scholar 

  4. Cao, T.D., Manolescu, I., Tannier, X.: Extracting linked data from statistic spreadsheets. In: Proceedings of the International Workshop on Semantic Big Data, pp. 5:1–5:5 (2017). https://doi.org/10.1145/3066911.3066914

  5. Chen, Z.: Information extraction on para-relational data. Ph.D. thesis, University of Michigan, US (2016)

    Google Scholar 

  6. Chen, Z., Cafarella, M.: Automatic web spreadsheet data extraction. In: Proceedings of the 3rd International Workshop on Semantic Search Over the Web, pp. 1:1–1:8 (2013). https://doi.org/10.1145/2509908.2509909

  7. Chen, Z., et al.: Spreadsheet property detection with rule-assisted active learning. Technical report CSE-TR-601-16 (2016). https://www.cse.umich.edu/techreports/cse/2016/CSE-TR-601-16.pdf

  8. Cunha, J., Erwig, M., Mendes, J., Saraiva, J.: Model inference for spreadsheets. Autom. Softw. Eng. 23(3), 361–392 (2016). https://doi.org/10.1007/s10515-014-0167-x

    Article  Google Scholar 

  9. Cunha, J., Fernandes, J.P., Mendes, J., Saraiva, J.: Spreadsheet engineering. In: Zsók, V., Horváth, Z., Csató, L. (eds.) CEFP 2013. LNCS, vol. 8606, pp. 246–299. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15940-9_6

    Chapter  Google Scholar 

  10. Cunha, J., Saraiva, J.a., Visser, J.: From spreadsheets to relational databases and back. In: Proceedings of the ACM SIGPLAN Workshop Partial Evaluation and Program Manipulation, pp. 179–188 (2009). https://doi.org/10.1145/1480945.1480972

  11. Dou, W., Xu, C., Cheung, S.C., Wei, J.: CACheck: detecting and repairing cell arrays in spreadsheets. IEEE Trans. Software Eng. 43(3), 226–251 (2017). https://doi.org/10.1109/TSE.2016.2584059

    Article  Google Scholar 

  12. 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, pp. 2477–2480 (2013). https://doi.org/10.1145/2505515.2508210. http://doi.acm.org/10.1145/2505515.2508210

  13. Embley, D.W., Krishnamoorthy, M.S., Nagy, G., Seth, S.: Converting heterogeneous statistical tables on the web to searchable databases. IJDAR 19(2), 119–138 (2016). https://doi.org/10.1007/s10032-016-0259-1

    Article  Google Scholar 

  14. Ermilov, I., Ngomo, A.-C.N.: TAIPAN: automatic property mapping for tabular data. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 163–179. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_11

    Chapter  Google Scholar 

  15. Fiorelli, M., Lorenzetti, T., Pazienza, M.T., Stellato, A., Turbati, A.: Sheet2RDF: a flexible and dynamic spreadsheet import&lifting framework for RDF. In: Ali, M., Kwon, Y., Lee, C.H., Kim, J., Kim, Y. (eds.) IEA/AIE 2015. LNCS, vol. 9101, pp. 131–140. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19066-2_13

    Chapter  Google Scholar 

  16. Galkin, M., Mouromtsev, D., Auer, S.: Identifying web tables: supporting a neglected type of content on the web. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2015. CCIS, vol. 518, pp. 48–62. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24543-0_4

    Chapter  Google Scholar 

  17. Gulwani, S., Harris, W.R., Singh, R.: Spreadsheet data manipulation using examples. Commun. ACM 55(8), 97–105 (2012). https://doi.org/10.1145/2240236.2240260

    Article  Google Scholar 

  18. Han, L., Finin, T., Parr, C., Sachs, J., Joshi, A.: RDF123: from spreadsheets to RDF. In: Sheth, A., et al. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 451–466. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88564-1_29

    Chapter  Google Scholar 

  19. Harris, W.R., Gulwani, S.: Spreadsheet table transformations from examples. SIGPLAN Not. 46(6), 317–328 (2011). https://doi.org/10.1145/1993316.1993536

    Article  Google Scholar 

  20. Hung, V., Benatallah, B., Saint-Paul, R.: Spreadsheet-based complex data transformation. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1749–1754 (2011). https://doi.org/10.1145/2063576.2063829

  21. Hurst, M.: Layout and language: challenges for table understanding on the web. In: Proceedings of the 1st International Workshop on Web Document Analysis, pp. 27–30 (2001)

    Google Scholar 

  22. Jin, Z., Anderson, M.R., Cafarella, M., Jagadish, H.V.: Foofah: transforming data by example. In: Proceedings of the ACM International Conference on Management of Data, pp. 683–698 (2017). https://doi.org/10.1145/3035918.3064034

  23. Koci, E., Thiele, M., Lehner, W., Romero, O.: Table recognition in spreadsheets via a graph representation. In: 13th IAPR International Workshop on Document Analysis Systems, pp. 139–144 (2018). https://doi.org/10.1109/DAS.2018.48

  24. Koci, E., Thiele, M., Romero, O., Lehner, W.: A machine learning approach for layout inference in spreadsheets. In: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 77–88 (2016). https://doi.org/10.5220/0006052200770088

  25. Koci, E., Thiele, M., Romero, O., Lehner, W.: Table identification and reconstruction in spreadsheets. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 527–541. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_33

    Chapter  Google Scholar 

  26. Kolb, S., Paramonov, S., Guns, T., De Raedt, L.: Learning constraints in spreadsheets and tabular data. Mach. Learn. 106(9), 1441–1468 (2017). https://doi.org/10.1007/s10994-017-5640-x

    Article  MathSciNet  MATH  Google Scholar 

  27. Langegger, A., Wöß, W.: XLWrap – querying and integrating arbitrary spreadsheets with SPARQL. In: Bernstein, A., Karger, D.R., Heath, T., Feigenbaum, L., Maynard, D., Motta, E., Thirunarayan, K. (eds.) ISWC 2009. LNCS, vol. 5823, pp. 359–374. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04930-9_23

    Chapter  Google Scholar 

  28. Mitlöhner, J., Neumaier, S., Umbrich, J., Polleres, A.: Characteristics of open data CSV files. In: 2nd International Conference on Open and Big Data, pp. 72–79 (2016). https://doi.org/10.1109/OBD.2016.18

  29. Mulwad, V., Finin, T., Joshi, A.: A domain independent framework for extracting linked semantic data from tables. In: Ceri, S., Brambilla, M. (eds.) Search Computing. LNCS, vol. 7538, pp. 16–33. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34213-4_2

    Chapter  Google Scholar 

  30. Nagy, G.: TANGO-DocLab web tables from international statistical sites (Troy\(\_\)200), 1, ID: Troy\(\_\)200\(\_\)1 (2016). http://tc11.cvc.uab.es/datasets/Troy_200_1

  31. O’Connor, M.J., Halaschek-Wiener, C., Musen, M.A.: Mapping master: a flexible approach for mapping spreadsheets to OWL. In: Patel-Schneider, P.F., et al. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 194–208. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17749-1_13

    Chapter  Google Scholar 

  32. Shigarov, A., Altaev, A., Mikhailov, A., Paramonov, V., Cherkashin, E.: TabbyPDF: web-based system for PDF table extraction. In: Damaševičius, R., Vasiljevienė, G. (eds.) ICIST 2018. CCIS, vol. 920, pp. 257–269. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99972-2_20

    Chapter  Google Scholar 

  33. Shigarov, A.: Rule-based table analysis and interpretation. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2015. CCIS, vol. 538, pp. 175–186. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24770-0_16

    Chapter  Google Scholar 

  34. Shigarov, A.: Table understanding using a rule engine. Expert Syst. Appl. 42(2), 929–937 (2015). https://doi.org/10.1016/j.eswa.2014.08.045

    Article  Google Scholar 

  35. Shigarov, A., Khristyuk, V.: TabbyXL2: experiment data. Mendeley Data, v2 (2018). https://doi.org/10.17632/ydcr7mcrtp.2

  36. Shigarov, A., Mikhailov, A., Altaev, A.: Configurable table structure recognition in untagged PDF documents. In: Proceedings of the ACM Symposium on Document Engineering, pp. 119–122 (2016). https://doi.org/10.1145/2960811.2967152

  37. 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.) ICIST 2016. CCIS, vol. 639, pp. 78–91. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46254-7_7

    Chapter  Google Scholar 

  38. 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.2017.08.004

    Article  Google Scholar 

  39. de Vos, M., Wielemaker, J., Rijgersberg, H., Schreiber, G., Wielinga, B., Top, J.: Combining information on structure and content to automatically annotate natural science spreadsheets. Int. J. Hum. Comput. Stud. 103, 63–76 (2017). https://doi.org/10.1016/j.ijhcs.2017.02.006

    Article  Google Scholar 

  40. Wang, X.: Tabular abstraction, editing, and formatting. Ph.D. thesis, University of Waterloo, Waterloo, Ontario, Canada (1996)

    Google Scholar 

  41. Yang, S., Guo, J., Wei, R.: Semantic interoperability with heterogeneous information systems on the internet through automatic tabular document exchange. Inf. Syst. 69, 195–217 (2017). https://doi.org/10.1016/j.is.2016.10.010

    Article  Google Scholar 

  42. Yang, S., Wei, R., Shigarov, A.: Semantic interoperability for electronic business through a novel cross-context semantic document exchange approach. In: Proceedings of the ACM Symposium on Document Engineering, pp. 28:1–28:10 (2018). https://doi.org/10.1145/3209280.3209523

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Acknowledgment

This work is supported by the Russian Science Foundation under Grant No.: 18-71-10001.

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Correspondence to Alexey Shigarov .

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Shigarov, A., Khristyuk, V., Mikhailov, A., Paramonov, V. (2019). TabbyXL: Rule-Based Spreadsheet Data Extraction and Transformation. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-030-30275-7_6

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