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Algo_Seer: System for Extracting and Searching Algorithms in Scholarly Big Data

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2019)

Abstract

Algorithms are the crucial and important part for any research and developments. Algorithms are usually published in the scientific publications, journals, conference papers or thesis. Algorithms plays important role especially in the computational and research areas where the researchers and developers look for the innovations. Therefore there is need for a search system which automatically searches for algorithms from the scholarly big data. Algo_Seer is been proposed as part of CiteSeer system which automatically searches for pseudo codes and algorithmic procedures and performs indexing, analysis and ranking to extract the algorithms. This work proposes a search system Algo_Seer which utilizes a novel arrangement of procedures such as rule based method, machine learning methods to recognize, separate and extract the calculated algorithms from the academic reports. Particularly mixture troupe machine learning systems are utilized to obtain the efficient results.

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References

  1. Wang, J.: Mean-variance analysis: a new document ranking theory in information retrieval. In: Proceedings of the 31st European Conference IR Research on Advances in Information Retrieval, pp. 4–16 (2009)

    Google Scholar 

  2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  3. Tuarob, S., Tucker, C.S.: Fad or here to stay: predicting product market adoption and longevity using large scale, social media data. In: Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (2013)

    Google Scholar 

  4. Tuarob, S., Tucker, C.S.: Quantifying product favorability and extracting notable product features using large scale social media data. J. Comput. Inform. Sci. Eng. 15(3) (2015). http://computingengineering.asmedigitalcollection.asme.org/article.aspx?articleid=2090327

    Article  Google Scholar 

  5. Hirschberg, D.S.: A linear space algorithm for computing maximal common subsequences. Commun. ACM 18(6), 341–343 (1975)

    Article  MathSciNet  Google Scholar 

  6. Guha, S., Koudas, N.: Approximating a data stream for querying and estimation: algorithms and performance evaluation. In: Proceedings of the 18th International Conference on Data Engineering, pp. 567–576 (2002)

    Google Scholar 

  7. Kataria, S., Browuer, W., Mitra, P., Giles, C.L.: Automatic extraction of data points and text blocks from two-dimensional plots in digital documents. In: Proceedings of the 23rd National Conference on Artificial Intelligence, vol. 2, pp. 1169–1174 (2008)

    Google Scholar 

  8. Sojka, P., Lıska, M.: The art of mathematics retrieval. In: Proceedings of the ACM Symposium on Document Engineering, pp. 57–60 (2011)

    Google Scholar 

  9. Bhatia, S., Mitra, P.: Summarizing figures, tables, and algorithms in scientific publications to augment search results. ACM Trans. Inf. Syst. 30(1), 3:1–3:24 (2012)

    Article  Google Scholar 

  10. Liu, Y., Bai, K., Mitra, P., Giles, C.L.: TableSeer: automatic table metadata extraction and searching in digital libraries. In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 91–100 (2007)

    Google Scholar 

  11. Hearst, M.A., Divoli, A., Guturu, H., Ksikes, A., Nakov, P., Wooldridge, M.A., Ye, J.: BioText search engine: beyond abstract search. Bioinformatics 23(16), 2196–2197 (2007)

    Article  Google Scholar 

  12. Hassan, T.: Object-level document analysis of PDF files. In: Proceedings of the 9th ACM Symposium on Document Engineering, pp. 47–55 (2009)

    Google Scholar 

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Correspondence to Pranayanath Reddy .

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Biradar Sangam, M., Shekhar, R., Reddy, P. (2020). Algo_Seer: System for Extracting and Searching Algorithms in Scholarly Big Data. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-28364-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28363-6

  • Online ISBN: 978-3-030-28364-3

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