Skip to main content
SpringerLink
Log in

Parallel Algorithms to Align Multiple Strings in the Context of Web Data Extraction

  • Chapter
  • First Online:
Emergent Computation

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 24))

  • 1348 Accesses

Abstract

The alignment of multiple strings generated from web pages represents a crucial problem in the processing of daily increasing amounts of data in the Internet. The complexity of this problem grows exponentially with the number of strings. Since it is not possible to achieve practically acceptable results on serial computers even with efficient heuristic approaches, parallel processing seems to be an inevitable option. There already exist emerging parallel solutions for the alignment of multiple strings in areas such as bioinformatics and genome applications. However, to our knowledge, no parallel solution has been published so far for a problem which arises in the context of web data extraction. In this work, we present two algorithms for a parallel solution of this problem, where input web data records are represented as a two-dimensional array of symbols. The algorithms differ in the assignment of the array data to the parallel processes. In the first one a distribution according to symbols is considered, whereas the second one operates by partitioning its columns. Communication among processes is handled via message passing in both cases. The algorithms are analyzed with respect to time and space complexity. We implemented both algorithms and have studied their properties by running them on a multiprocessor system. For the version with distributed columns, we observed that its speedup significantly suffers from the communication overhead. However, the results for the version with data distribution by symbols are convincing. In this case, reasonable performance has been obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Web page excerpt from http://www.barnesandnoble.com/.

  2. 2.

    http://www.tdg-seville.info/Hassan/CEDAR.

  3. 3.

    http://www.tic.udc.es/~mad/resources/projects/dataextraction/testcollection_0507.htm.

  4. 4.

    http://msn.autotrader.com/cars-for-sale/.

  5. 5.

    http://productsearch.barnesandnoble.com/search/results.aspx?CAT=1000652&STORE=music.

  6. 6.

    http://zoopla.homesandproperty.co.uk/to-rent/property/london/.

  7. 7.

    http://www.amazon.com/.

  8. 8.

    http://www.kobobooks.com/browse/Art__Architecture/LjUHGEwvR0KbkEbC9oZP_Q-2.html.

  9. 9.

    http://www.w3.org/People/Raggett/tidy/.

References

  1. Álvarez, M., Pan, A., Raposo, J., Bellas, F., Cacheda, F.: Finding and extracting data records from web pages. In: Proceedings of the 2007 International Conference on Embedded and Ubiquitous Computing, pp. 466–478 (2007)

    Google Scholar 

  2. Arasu, A., Garcia-Molina, H.: Extracting structured data from web pages. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 337–348 (2003)

    Google Scholar 

  3. Chang, C.H., Kayed, M., Girgis, M.R., Shaalan, K.F.: A survey of web information extraction systems. IEEE Trans. Knowl. Data Eng. 18(10), 1411–1428 (2006)

    Article  Google Scholar 

  4. Chang, C.H., Kuo, S.C.: OLERA: Semisupervised web-data extraction with visual support. IEEE Intell. Syst. 19(6), 56–64 (2004)

    Article  Google Scholar 

  5. Chang, C.H., Lui, S.C.: IEPAD: Information extraction based on pattern discovery. In: Proceedings of the 10th International Conference on World Wide Web, pp. 681–688 (2001)

    Google Scholar 

  6. Church, P.C., Goscinski, A., Holt, K., Inouye, M., Ghoting, A., Makarychev, K., Reumann, M.: Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers. In: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 924–927. IEEE (2011). doi:10.1109/IEMBS.2011.6090208

  7. Crescenzi, V., Mecca, G.: Automatic information extraction from large websites. J. ACM 51(5), 731–779 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  8. Crescenzi, V., Mecca, G., Merialdo, P.: RoadRunner: Towards automatic data extraction from large web sites. In: Proceedings of the 27th International Conference on Very Large Data Bases, pp. 109–118 (2001)

    Google Scholar 

  9. Daugelaite, J., Driscoll, A.O., Sleator, R.D.: An overview of multiple sequence alignments and cloud computing in bioinformatics. ISRN Biomath. 2013, 1–14 (2013). doi:10.1155/2013/615630

    Article  MATH  Google Scholar 

  10. Gusfield, D.: Efficient methods for multiple sequence alignment with guaranteed error bounds. Bull. Math. Biol. 55(1), 141–154 (1993)

    Article  MATH  Google Scholar 

  11. Gusfield, D.: Algorithms on Strings, Trees, and Sequences, 1st edn. Cambridge University Press, New York (1997)

    Book  MATH  Google Scholar 

  12. Hunga, C.L., Linb, Y.S., Linc, C.Y., Chungb, Y.C., Chungc, Y.F.: CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on multi-GPUs. Comput. Biol. Chem. 58, 62–68 (2015)

    Google Scholar 

  13. Kayed, M.: Peer matrix alignment: A new algorithm. In: Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 268–279 (2012)

    Google Scholar 

  14. Kayed, M., Chang, C.H.: FiVaTech: Page-level web data extraction from template pages. IEEE Trans. Knowl. Data Eng. 22(2), 249–263 (2010)

    Article  Google Scholar 

  15. Laender, A.H.F., Ribeiro-Neto, B.A., da Silva, A.S., Teixeira, J.S.: A brief survey of web data extraction tools. SIGMOD Rec. 31(2), 84–93 (2002)

    Article  Google Scholar 

  16. Li, K.B.: ClustalW-MPI: ClustalW analysis using distributed and parallel computing. Bioinformatics 19(12), 1585–1586 (2003)

    Article  Google Scholar 

  17. Liu, W., Meng, X., Meng, W.: ViDE: A vision-based approach for deep web data extraction. IEEE Trans. Knowl. Data Eng. 22(3), 447–460 (2010)

    Article  Google Scholar 

  18. Lopes, H.S., Lima, C.R.E., Moritz, G.L.: A parallel algorithm for large-scale multiple sequence alignment. Comput. Inform. 29(6), 1233–1250 (2010)

    Google Scholar 

  19. Miao, G., Tatemura, J., Hsiung, W.P., Sawires, A., Moser, L.E.: Extracting data records from the web using tag path clustering. In: Proceedings of the 18th International Conference on World Wide Web, pp. 981–990 (2009)

    Google Scholar 

  20. Mikhailov, D., Cofer, H., Gomperts, R.: Performance optimization of ClustalW: Parallel ClustalW. HT Clustal and MultiClustal, SGI ChemBio (2001)

    Google Scholar 

  21. Nguyen, L.T., Schmidt, H.A., von Haeseler, A., Minh, B.Q.: IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32(1), 268–274 (2015). doi:10.1093/molbev/msu300

    Article  Google Scholar 

  22. Rognes, T.: ParAlign: A parallel sequence alignment algorithm for rapid and sensitive database searches. Nucl. Acids Res. 29(7), 1647–1652 (2001)

    Article  Google Scholar 

  23. Sarawagi, S.: Information extraction. Found. Trends Databases 1(3), 261–377 (2008)

    Article  MATH  Google Scholar 

  24. Simon, K., Lausen, G.: ViPER: Augmenting automatic information extraction with visual perceptions. In: Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, pp. 381–388 (2005)

    Google Scholar 

  25. Sleiman, H.A., Corchuelo, R.: An architecture for web information agents. In: 11th International Conference on Intelligent Systems Design and Applications, pp. 18–23 (2011)

    Google Scholar 

  26. Su, W., Wang, J., Lochovsky, F.H.: ODE: Ontology-assisted data extraction. ACM Trans. Database Syst. 34(2), 12:1–12:35 (2009)

    Google Scholar 

  27. W3C: Document Object Model (DOM). http://www.w3.org/DOM/. Accessed 6 Jan 2009

  28. W3C: HTML 4.01 specification. http://www.w3.org/TR/html401/. Accessed 24 Dec 1999

  29. W3C: HTML 5. http://www.w3.org/TR/2014/REC-html5-20141028/. Accessed 27 Dec 2015

  30. W3C: World Wide Web Consortium. http://www.w3.org/. Accessed 18 April 2013

  31. Wang, J., Lochovsky, F.H.: Data extraction and label assignment for web databases. In: Proceedings of the 12th International Conference on World Wide Web, pp. 187–196 (2003)

    Google Scholar 

  32. Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1(4), 337–348 (1994)

    Article  Google Scholar 

  33. Xia, Y., Yu, H., Zhang, S.: Automatic web data extraction using tree alignment. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1645–1648 (2009)

    Google Scholar 

  34. Yamada, Y., Craswell, N., Nakatoh, T., Hirokawa, S.: Testbed for information extraction from deep web. In: Proceedings of the 13th International World Wide Web Conference, pp. 346–347 (2004)

    Google Scholar 

  35. Zhai, Y., Liu, B.: Structured data extraction from the web based on partial tree alignment. IEEE Trans. Knowl. Data Eng. 18(12), 1614–1628 (2006)

    Article  Google Scholar 

  36. Zhao, H., Meng, W., Wu, Z., Raghavan, V., Yu, C.: Fully automatic wrapper generation for search engines. In: Proceedings of the 14th International Conference on World Wide Web, pp. 66–75 (2005)

    Google Scholar 

  37. Zhao, H., Meng, W., Yu, C.: Mining templates from search result records of search engines. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 884–893 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, University of Salzburg, Salzburg, Austria

    Christine Gfrerer, Marián Vajteršic & Rade Kutil

  2. Mathematical Institute, Department of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia

    Marián Vajteršic

Authors
  1. Christine Gfrerer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marián Vajteršic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rade Kutil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rade Kutil .

Editor information

Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England Unconventional Computing Centre, Bristol, United Kingdom

    Andrew Adamatzky

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Gfrerer, C., Vajteršic, M., Kutil, R. (2017). Parallel Algorithms to Align Multiple Strings in the Context of Web Data Extraction. In: Adamatzky, A. (eds) Emergent Computation . Emergence, Complexity and Computation, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-46376-6_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46376-6_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46375-9

  • Online ISBN: 978-3-319-46376-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation