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Merging image databases as an example for information integration

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Abstract

By integrating information we mean pulling information pieces from various sources together with as little loss of information and as little redundancy as possible. Operation Research techniques applied in many situations, like heuristic algorithms, meta-heuristics, similarity measures, use of constraints, combinatorial algorithms and collaborative efforts provide valuable tools for this process, if augmented by other techniques known from knowledge management and information retrieval. Given the huge amount of information we are confronted with, information integration is one of the biggest challenges of this century. This paper describes methods and techniques for the information integration process. It is interesting to note that in areas such as information integration, consolidation and simplification that are not usually at the heart of Operation Research, some of the most important techniques used in this field can be applied successfully. Information integration is an important topic but, in general no really convincing approaches have been discovered, so far. By limiting the domain to the integration of image databases the problem becomes tractable, by using the mentioned techniques and also applying methods ranging from image processing, knowledge management and natural language processing to feature engineering.

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References

  • Afzal MT, Balke WT, Kulathuramaiyer N, Maurer H (2010) Rule based autonomous citation mining with TIERL. JDIM 8(1):196–204

    Google Scholar 

  • Austria-Forum (2014) http://Austria-forum.org/. Accessed 20 Aug 2014

  • Bikel DM, Schwartz R, Weischedel RM (1999) An algorithm that learns what’s in a name. Mach Learn 34(1):211–231

    Article  Google Scholar 

  • Blazsik Z, Imre C, Kovcs Z (2008) Heuristic algorithms for a complex parallel machine scheduling problem. CJOR 16(4):379–390

    Google Scholar 

  • Carlson A, Betteridge J, Wang RC, Hruschka ER Jr, Mitchell T (2010) Coupled semi-supervised learning for information extraction. In: Proceedings of the third ACM international conference on web search and data mining, pp 101–110. doi:10.1145/1718487.1718501

  • Cohen W (2000) Whirl a word-based information representation language. Artical Intell 118(1):163–196

    Article  Google Scholar 

  • Debnath S, Mitra P, Giles CL (2005) Automatic extraction of informative blocks from webpages. 20th annual ACM symposium on applied computing. doi:10.1145/1066677.1067065

  • Denish (2008) Table drag and drop JQuery plugin. http://isocra.com/2008/02/table-drag-and-drop-jquery-plugin/. Accessed 20 Aug 2014

  • Deutsche National Bibliothek (2014) Common Authority File (GND). http://www.dnb.de/DE/Standardisierung/GND/gnd_node.html. Accessed 20 Aug 2014

  • Deutsche National Bibliothek (2014). http://d-nb.info/gnd/118601121/about/html. Accessed 20 Aug 2014

  • Furian N, Vössner S (2013) Constrained order packing:comparison of heuristic approaches for a new bin packing problem. CJOR 21(1):237–264

    Google Scholar 

  • Genesereth M, keller A, Duschka O (1997) Infomaster: an information integration system. ACM SIGMOD international conference on management of data, Tucson. doi:10.1145/253262.253400

  • Grossman DA, Frieder O (2004) Information retrieval: algorithms and heuristics, 2nd edn. Springer, Berlin

    Book  Google Scholar 

  • Imagno (2014). http://www.imagno.at. Accessed 20 Aug 2014

  • Kaklauskas L, Sakalauskas L (2013) Study of on-line measurement of traffic self-similarity. CJOR 21(1):63–84

    Google Scholar 

  • Kappe F, Maurer H, Zaka B (2006) Plagiarism—A survey. J Univers Comput Sci 12(8):1050–1084

    Google Scholar 

  • Kirk T, Levy AY, Sagiv Y, Srivastava D (1995) The information manifold. In: Proceedings of the AAAI on information gathering from heterogeneous, distributed environments. doi:10.1.1.32.1651

  • Korica P, Maurer H (2012) Semi-automatic information retrieval and consolidation with a sample application. In: Proceedings of international conference on emerging technologies (ICET). doi:10.1109/ICET.2012.6375491

  • Latif A, Afzal MT, Saeed AU, Hoefler P, Tochtermann K (2009) CAF-SIAL: concept aggregation framework for structuring informational aspects of linked open data. In: Proceedings of NDT. doi:10.1109/NDT.2009.5272079

  • Liu Y, Wang Q (2006) A heuristic approach for topical information extraction from news pages. Lect Notes Comput Sci 4255(2006):357–362

    Article  Google Scholar 

  • Manning CD, Raghavan P (2009) Introduction to information retrieval. Cambridge University Press, New York

  • Maurer H, Scherbakov N (1998) Mulitmeida authroing for presentation and education. Addison-Wesely, Boston

    Google Scholar 

  • Maurer H, Tochtermann K (2002) On a new powerful model for knowledge-management and its applications. JUCS 8(1):85–96

    Google Scholar 

  • McCann R, Kramnik A, Warren S, Varadarajan V, Sobulo O, Doan A (2005) Integrating data from disparate sources: a mass collaboration approach. In: ICDE proceedings. doi:10.1109/ICDE.2005.81

  • Mes M, Heijden M, Schur P (2013) Interaction between intelligent agent strategies for real-time transportation planning. CJOR 21(2):337–358

    Google Scholar 

  • Nakapan W, Halin G, Bignon J-C, Wagner M, Humbert P (2003) Extraction of building product image from the Web. Int J Intell Syst 19(1):65–78

    Google Scholar 

  • Nigam K (2006) Text Information extraction, autumn school machine learning over text and images. http://videolectures.net/mlas06_nigam_tie/. Accessed 20 Aug 2014

  • Saracevic T (1986) Processes and problems in information consolidation. Information processing and management, vol 22, 1st edn. Pergamon Press, Tarrytown, NY, pp 45–60

    Google Scholar 

  • Swiercz A, Burke EK, Cichenski M, Pawlak G, Petrovic S, Zurkowski T, Blazewicz J (2014) Unified encoding for hyper heuristics with application to bioinformatics. CJOR 22(3):567–589

    Google Scholar 

  • Wurzinger G (2010) Information consolidation in large bodies of information. J Univers Comput Sci 16(21):3314–3323

    Google Scholar 

  • Zelenko D, Aone C, Richardella A, Hofmann T, Poggio T, Shawe-Taylor J (2003) Kernel methods for relation extraction. J Mach Learn Res 3:1083–1106

    Google Scholar 

  • Zijing T (2008) Answering xpath queries in virtual xml data integration system. In: Proceedings of the international conference on computer science and software engineering. doi:10.1109/CSSE.2008.579

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Authors and Affiliations

  1. Institute for Information Systems and Computer Media, Graz University of Technology, Graz, Austria

    Hermann Maurer & Rizwan Mehmood

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  1. Hermann Maurer
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  2. Rizwan Mehmood
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Correspondence to Rizwan Mehmood.

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Maurer, H., Mehmood, R. Merging image databases as an example for information integration. Cent Eur J Oper Res 23, 441–458 (2015). https://doi.org/10.1007/s10100-015-0380-0

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  • DOI: https://doi.org/10.1007/s10100-015-0380-0

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