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Preface to the 2nd International Workshop on Unstructured Data Management (USDM 2011)

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Book cover Web Technologies and Applications (APWeb 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6612))

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Abstract

The management of unstructured data has been recognized as one of the most attracting problems in the information technology industry. With the consistent increase of computing and storage capacities (due to hardware progress) and the emergence of many data-centric applications (e.g. web applications), a huge volume of unstructured data has been generated. Over 80% of world data today is unstructured with self-contained content items. Since most techniques and researches that have proved so successful performing on structured data don’t work well when it comes to unstructured data, how to effectively handle and utilize unstructured data becomes a critical issue to these data-centric applications.

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References

  1. Berchtold, S., Christian, G., Braunmller, B., Keim, D.A., Kriegel, H.-P.: Fast parallel similarity search in multimedia databases. In: SIGMOD, pp. 1–12 (1997)

    Google Scholar 

  2. Dong, X., Halevy, A.Y., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: Nascimento, M.A., Ozsu, M.T., Kossmann, D., Miller, R.J., Blakeley, J.A., Schiefer, K.B. (eds.) VLDB, pp. 372–383. Morgan Kaufmann, San Francisco (2004)

    Google Scholar 

  3. Eguchi, K., Lavrenko, V.: Sentiment retrieval using generative models. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia, pp. 345–354 (2006)

    Google Scholar 

  4. McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured models for fine-to-coarse sentiment analysis. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, pp. 432–439 (2007)

    Google Scholar 

  5. Resource Description Framework (RDF): Concepts and Abstract Syntax, http://www.w3.org/TR/rdf-concepts

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© 2011 Springer-Verlag Berlin Heidelberg

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Wang, T. (2011). Preface to the 2nd International Workshop on Unstructured Data Management (USDM 2011). In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds) Web Technologies and Applications. APWeb 2011. Lecture Notes in Computer Science, vol 6612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20291-9_44

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  • DOI: https://doi.org/10.1007/978-3-642-20291-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20290-2

  • Online ISBN: 978-3-642-20291-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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