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Heterogeneous Data Integration Architecture-Challenging Integration Issues

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
As of today, most of the data processing systems have to deal with a large amount of data originated from numerous sources. Data sources almost always differ regarding its purpose of existence. Thus model, data processing engine and technology differ intensely. Due to current trend for systems fusion there is a growing demand for data to be present in a common way regardless of its legacy. Many systems have been devised as a response to such integration needs. However, the present data integration systems mostly are dedicated solutions that bring constraints and issues when considered in general. In this paper we will focus on the present solutions for data integration, their flaws originating from their architecture or design concepts and present an abstract and general approach that could be introduced as an response to existing issues. The system integration is considered out of scope for this paper, we will focus particularly on efficient data integration.
Rocznik
Strony
7--11
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
autor
  • Institute of Computer Science, Maria Curie-Sklodowska University, pl. M. Curie-Sklodowskiej 5, 20-031 Lublin, Poland
Bibliografia
  • [1] http://www.omg.org/technology/documents/corba_spec_catalog.htm, 2010.
  • [2] http://en.wikipedia.org/wiki/list_of_web_service_specifications, 2010.
  • [3] http://www.jini.org/wiki/jini_architecture_specification, 2009.
  • [4] K. Kuliberda, R. Adamus, J. Wislicki, K. Kaczmarski, T. M. Kowalski, and K. Subieta. A generic proposal for a transparent integration of distributed data by an autonomous layer in a virtual repository. an International Journal (MAGS), 3(4):393-410, 2007.
  • [5] M. W. Sobolewski. Computing and metacomputing intergrid. Proc. 10th International Conference on Enterprise Information Systems, Barcelona, Spain, 2008.
  • [6] M. W. Sobolewski. Federated collaborations with exertions. pages 127-132, 2008.
  • [7] R. Grycuk et al. Content-based image indexing by data clustering and inverse document frequency. Communications in Computer and Information Science, pages 374-383, 2014.
  • [8] M. Lupa and A Piórkowski. Spatial query optimization based on transformation of constraints man-machine interactions. 242, 2014.
  • [9] P. Leszczynski and K. Stencel. Update propagator for joint scalable storage. Fundam. Inform., 119(3-4), 2012.
  • [10] P. Hepner. Integrating heterogeneous databases: An overview, school of computing and mathematics. Deakin University, Gee-long, Victoria, Australia, 1995.
  • [11] V. D. Gilgor and G. L. Luckenbaugh. Interconnecting heterogeneous database management system. IEEE Comp. Soc. Press, 17(1), 1983.
  • [12] S. E. Madnick. A taxonomy for classifying commercial approaches to information integration in heterogeneous environments. Database Engineering - Special Issues on Database Connectivity, 13(2), 1999.
  • [13] V. Rajeswari et al. Heterogeneous database integration for web applications. International Journal on Computer Science and Engineering, 1(13):227-234, 2009.
  • [14] S. C. Tseng Frank. Heterogeneous database integration using xml.
  • [15] S. Wei-Jung and H. Minng-Ying. An interactive tool based on xml technology for data exchange between heterogeneous erp systems. Journal of CIIE, 22(4):273-278, 2005.
  • [16] P. Rodriguez-Gianolli and J. Mylopoulos. A semantic approach to xml-based data integration conceptual modeling.
  • [17] Microsoft TechNet. http://technet.microsoft.com/enus/library/ms151835.aspx.
  • [18] J. Basu Nirav Chanchani. Heterogeneous xml-based data integration.
  • [19] B. Inmon. Building the data warehouse.
  • [20] M. Chromiak and K. Stencel. A data model for heterogeneous data integration architecture. 424, 2014.
  • [21] M. Chromiak and K. Stencel. The linkup data structure for heterogeneous data integration platform. 7709:263-274, 2012.
  • [22] et al. A. Piorkowski. Load balancing for heterogeneous web servers. 79:189-198, 2010.
  • [23] P. Wisniewski and K. Stencel. Query rewriting based on metagranular aggregation. pages 457-468, 2013.
  • [24] M. Gawarkiewicz and P. Wisniewski. Partial aggregation using hibernate. pages 90-99, 2011.
  • [25] A. Boniewicz et al. On materializing paths for faster recursive querying. pages 105-112, 2013.
  • [26] M. Burzanska et al. Recursive queries using object relational mapping.
  • [27] M.Chromiak et al. Exploiting order dependencies on primary keys for optimization. 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c8f706f4-ff48-4d49-b841-62e4ffd4354a
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