Recovery of scientific data using Intelligent Distributed Data Warehouse

A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element for its evolution. Unified profiles have been develo...

Full description

Autores:
Viloria, Amelec
Neira Rodado, Dionicio
Pineda Lezama, Omar Bonerge
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/4842
Acceso en línea:
https://hdl.handle.net/11323/4842
https://repositorio.cuc.edu.co/
Palabra clave:
scientific data
meta-data
meta-search engine
recovery of information
intelligent distributed data warehouse
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_2ab6cca42ab7e67aa91d5105491f9a4f
oai_identifier_str oai:repositorio.cuc.edu.co:11323/4842
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Recovery of scientific data using Intelligent Distributed Data Warehouse
title Recovery of scientific data using Intelligent Distributed Data Warehouse
spellingShingle Recovery of scientific data using Intelligent Distributed Data Warehouse
scientific data
meta-data
meta-search engine
recovery of information
intelligent distributed data warehouse
title_short Recovery of scientific data using Intelligent Distributed Data Warehouse
title_full Recovery of scientific data using Intelligent Distributed Data Warehouse
title_fullStr Recovery of scientific data using Intelligent Distributed Data Warehouse
title_full_unstemmed Recovery of scientific data using Intelligent Distributed Data Warehouse
title_sort Recovery of scientific data using Intelligent Distributed Data Warehouse
dc.creator.fl_str_mv Viloria, Amelec
Neira Rodado, Dionicio
Pineda Lezama, Omar Bonerge
dc.contributor.author.spa.fl_str_mv Viloria, Amelec
Neira Rodado, Dionicio
Pineda Lezama, Omar Bonerge
dc.subject.spa.fl_str_mv scientific data
meta-data
meta-search engine
recovery of information
intelligent distributed data warehouse
topic scientific data
meta-data
meta-search engine
recovery of information
intelligent distributed data warehouse
description A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element for its evolution. Unified profiles have been developed for the data storage of the entities involved in the scientific data management, generated from the fact of publishing a scientific paper. Such profiles are considered the beginning of the generation of new components for the meta-search engine that, using the proprietary information, can deliver information relevant for the user of the tool. To this end, the use of an intelligent distributed data warehouse is proposed.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-06-10T14:17:27Z
dc.date.available.none.fl_str_mv 2019-06-10T14:17:27Z
dc.date.issued.none.fl_str_mv 2019
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
format http://purl.org/coar/resource_type/c_6501
status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 0000-2010
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/4842
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 0000-2010
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/4842
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv [1] Garciarena Ucelay, M.J., Villegas, M.P., Cagnina, L., Errecalde, M.L.: Cross domain author profiling task in spanish language: an experimental study. J. Comput. Sci. Technol. 15, no. 2, (2015). [2] Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Computing. Surveys. CSUR. 37, 1–28 (2005). [3] Bhaduri K., Wolf R., Giannella C., and Kargupta H., “Distributed decision-tree induction in peer-to-peer systems.”, Statistical Analysis and Data Mining, Vol. 1, Issue 2, pp. 85–103, 2008. [4] Duan L., Xu L., Liu Y. and Lee J., “Cluster-based outlier detection.”, Annals of Operations Research 168, pp. 151–168, 2009. [5] Abhay Kumar Agarwal and Neelendra Badal “Data Storing in Intelligent and Distributed Data Warehouse using Unique Identification Number” published in International Journal of Grid and Distributed Computing, Publisher: SERSC Australia, (ISSN: 2005-4262 (Print) ISSN: 2207-6379 (Online)), Volume 10, No. 9, pp. 13-32, September 2017. [6] Agrawal R. and Srikant R., “Fast algorithms for mining association rules in large databases.”, In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB, Chile, pp. 487–499, 1994. [7] Chiang D., Lin C. and Chen M., “The adaptive approach for storage assignment by mining data of warehouse management system for distribution centre’s.”, Enterp. Inf. Syst, Vol. 5, Issue 2, pp. 219–234, 2001. [8] Abhay Kumar Agarwal and N. Badal “A Novel Approach for Intelligent Distribution of Data Warehouses” published in Egyptian Informatics Journal-Elsevier, Egypt, (ISSN: 1110-8665), http://dx.doi.org/10.1016/j.eij.2015.10.002, Volume 17, pp. 147-159, October, 2015. [9] Savasere A., Omiecinski E. and Navathe S., “An efficient algorithm for data mining association rules in large databases”, In Proceedings of 21st Very Large Data Base Conference, pp. 432- 444, 1995. [10] Stolfo S., Prodromidis A. L., Tselepis S., Lee W. and Fan D. W., “Jam: Java agents for meta- learning over distributed databases.”, In Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining., pp. 74-81, 1997. [11] Prodromidis A., Chan P. K., Stolfo S. J., “Meta learning in distributed data mining systems: Issues and approaches.”, In Kargupta H., Chan P. (eds) Book on Advances in Distributed and Parallel Knowledge Discovery, AAAI/MIT Press, 2000. [12] Grossman R. l., Bailey S. M., Sivakumar H. and Turinsky A. L., “papyrus: A system for data mining over local and wide area clusters and super-clusters.”, In Proceedings of ACM/IEEE Conference on Supercomputing, Article No. 63, 1999. [13] Chattratichat J., Darlington J., Guo Y., Hedvall S., Kohler M. and Syed J.“An architecture for distributed enterprise data mining.”, In Proceedings of 7th International Conference on High- Performance Computing and Networking, Netherlands, pp. 573-582, 1999. [14] Wang L., et. al., "G-Hadoop: MapReduce across Distributed Data Centers for Data-Intensive Computing.", Future Generation Computer Systems, Vol. 29, Issue 3, pp. 739-750, 2013. [15] Butenhof D. R., “Programming with POSIX threads.”, Addison-Wesley Longman Publishing Company, USA, 1997. [16] Gaitán-Angulo M., Cubillos Díaz J., Viloria A., Lis-Gutiérrez JP., Rodríguez-Garnica P.A. (2018) Bibliometric Analysis of Social Innovation and Complexity (Databases Scopus and Dialnet 2007–2017). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [17] Torres-Samuel M., Vásquez C.L., Viloria A., Varela N., Hernández-Fernandez L., Portillo-Medina R. (2018)a Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [18] Torres-Samuel M, Carmen Vásquez, Amelec Viloria, Tito Crissien Borrero, Noel Varela, Danelys Cabrera, Mercedes Gaitán-Angulo, JennyPaola Lis-Gutiérrez. (2018)b Efficiency Analysis of the Visibility of Latin American Universities and Their Impact on the Ranking Web. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [19] Torres-Samuel M., Vásquez C., Viloria A., Lis-Gutiérrez JP., Borrero T.C., Varela N. (2018)c Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [20] Vásquez C, Maritza Torres-Samuel, Amelec Viloria, Tito Crissien Borrero, Noel Varela, Jenny-Paola Lis-Gutiérrez, Mercedes GaitánAngulo. (2018) Visibility of Research in Universities: The Triad Product-Researcher-Institution. Case: Latin American Countries. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Procedia Computer Science
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/c99a2094-8989-428e-9fa4-171729a9faab/download
https://repositorio.cuc.edu.co/bitstreams/64ca70d7-ff4a-4fb5-b1d4-ef8b66a9bb63/download
https://repositorio.cuc.edu.co/bitstreams/4757ddf8-f390-4900-854b-5b3f7596c9bf/download
https://repositorio.cuc.edu.co/bitstreams/3590317d-074f-4f25-991d-3989eeee4415/download
https://repositorio.cuc.edu.co/bitstreams/7a673749-fdc7-4f7e-aef5-df911debd514/download
bitstream.checksum.fl_str_mv 503c04ae41d09e198c7850e3d5eec360
4460e5956bc1d1639be9ae6146a50347
8a4605be74aa9ea9d79846c1fba20a33
2ec58bcb0254c903c889834644df7e5c
e6502c36f98d25aa5fee18a796cccea4
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1811760830503452672
spelling Viloria, AmelecNeira Rodado, DionicioPineda Lezama, Omar Bonerge2019-06-10T14:17:27Z2019-06-10T14:17:27Z20190000-2010https://hdl.handle.net/11323/4842Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element for its evolution. Unified profiles have been developed for the data storage of the entities involved in the scientific data management, generated from the fact of publishing a scientific paper. Such profiles are considered the beginning of the generation of new components for the meta-search engine that, using the proprietary information, can deliver information relevant for the user of the tool. To this end, the use of an intelligent distributed data warehouse is proposed.Viloria, Amelec-52922525-9094-40f2-acd3-5424e90bb258-0Neira Rodado, Dionicio-0000-0003-0837-7083-0Pineda Lezama, Omar Bonerge-365a03a0-145e-4df5-9abe-f5ccf9d96612-0engProcedia Computer ScienceAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2scientific datameta-datameta-search enginerecovery of informationintelligent distributed data warehouseRecovery of scientific data using Intelligent Distributed Data WarehouseArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Garciarena Ucelay, M.J., Villegas, M.P., Cagnina, L., Errecalde, M.L.: Cross domain author profiling task in spanish language: an experimental study. J. Comput. Sci. Technol. 15, no. 2, (2015). [2] Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Computing. Surveys. CSUR. 37, 1–28 (2005). [3] Bhaduri K., Wolf R., Giannella C., and Kargupta H., “Distributed decision-tree induction in peer-to-peer systems.”, Statistical Analysis and Data Mining, Vol. 1, Issue 2, pp. 85–103, 2008. [4] Duan L., Xu L., Liu Y. and Lee J., “Cluster-based outlier detection.”, Annals of Operations Research 168, pp. 151–168, 2009. [5] Abhay Kumar Agarwal and Neelendra Badal “Data Storing in Intelligent and Distributed Data Warehouse using Unique Identification Number” published in International Journal of Grid and Distributed Computing, Publisher: SERSC Australia, (ISSN: 2005-4262 (Print) ISSN: 2207-6379 (Online)), Volume 10, No. 9, pp. 13-32, September 2017. [6] Agrawal R. and Srikant R., “Fast algorithms for mining association rules in large databases.”, In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB, Chile, pp. 487–499, 1994. [7] Chiang D., Lin C. and Chen M., “The adaptive approach for storage assignment by mining data of warehouse management system for distribution centre’s.”, Enterp. Inf. Syst, Vol. 5, Issue 2, pp. 219–234, 2001. [8] Abhay Kumar Agarwal and N. Badal “A Novel Approach for Intelligent Distribution of Data Warehouses” published in Egyptian Informatics Journal-Elsevier, Egypt, (ISSN: 1110-8665), http://dx.doi.org/10.1016/j.eij.2015.10.002, Volume 17, pp. 147-159, October, 2015. [9] Savasere A., Omiecinski E. and Navathe S., “An efficient algorithm for data mining association rules in large databases”, In Proceedings of 21st Very Large Data Base Conference, pp. 432- 444, 1995. [10] Stolfo S., Prodromidis A. L., Tselepis S., Lee W. and Fan D. W., “Jam: Java agents for meta- learning over distributed databases.”, In Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining., pp. 74-81, 1997. [11] Prodromidis A., Chan P. K., Stolfo S. J., “Meta learning in distributed data mining systems: Issues and approaches.”, In Kargupta H., Chan P. (eds) Book on Advances in Distributed and Parallel Knowledge Discovery, AAAI/MIT Press, 2000. [12] Grossman R. l., Bailey S. M., Sivakumar H. and Turinsky A. L., “papyrus: A system for data mining over local and wide area clusters and super-clusters.”, In Proceedings of ACM/IEEE Conference on Supercomputing, Article No. 63, 1999. [13] Chattratichat J., Darlington J., Guo Y., Hedvall S., Kohler M. and Syed J.“An architecture for distributed enterprise data mining.”, In Proceedings of 7th International Conference on High- Performance Computing and Networking, Netherlands, pp. 573-582, 1999. [14] Wang L., et. al., "G-Hadoop: MapReduce across Distributed Data Centers for Data-Intensive Computing.", Future Generation Computer Systems, Vol. 29, Issue 3, pp. 739-750, 2013. [15] Butenhof D. R., “Programming with POSIX threads.”, Addison-Wesley Longman Publishing Company, USA, 1997. [16] Gaitán-Angulo M., Cubillos Díaz J., Viloria A., Lis-Gutiérrez JP., Rodríguez-Garnica P.A. (2018) Bibliometric Analysis of Social Innovation and Complexity (Databases Scopus and Dialnet 2007–2017). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [17] Torres-Samuel M., Vásquez C.L., Viloria A., Varela N., Hernández-Fernandez L., Portillo-Medina R. (2018)a Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [18] Torres-Samuel M, Carmen Vásquez, Amelec Viloria, Tito Crissien Borrero, Noel Varela, Danelys Cabrera, Mercedes Gaitán-Angulo, JennyPaola Lis-Gutiérrez. (2018)b Efficiency Analysis of the Visibility of Latin American Universities and Their Impact on the Ranking Web. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [19] Torres-Samuel M., Vásquez C., Viloria A., Lis-Gutiérrez JP., Borrero T.C., Varela N. (2018)c Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [20] Vásquez C, Maritza Torres-Samuel, Amelec Viloria, Tito Crissien Borrero, Noel Varela, Jenny-Paola Lis-Gutiérrez, Mercedes GaitánAngulo. (2018) Visibility of Research in Universities: The Triad Product-Researcher-Institution. Case: Latin American Countries. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, ChamPublicationORIGINALRecovery of scientific data using Intelligent Distributed Data Warehouse.pdfRecovery of scientific data using Intelligent Distributed Data Warehouse.pdfapplication/pdf382055https://repositorio.cuc.edu.co/bitstreams/c99a2094-8989-428e-9fa4-171729a9faab/download503c04ae41d09e198c7850e3d5eec360MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/64ca70d7-ff4a-4fb5-b1d4-ef8b66a9bb63/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/4757ddf8-f390-4900-854b-5b3f7596c9bf/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILRecovery of scientific data using Intelligent Distributed Data Warehouse.pdf.jpgRecovery of scientific data using Intelligent Distributed Data Warehouse.pdf.jpgimage/jpeg42423https://repositorio.cuc.edu.co/bitstreams/3590317d-074f-4f25-991d-3989eeee4415/download2ec58bcb0254c903c889834644df7e5cMD55TEXTRecovery of scientific data using Intelligent Distributed Data Warehouse.pdf.txtRecovery of scientific data using Intelligent Distributed Data Warehouse.pdf.txttext/plain20992https://repositorio.cuc.edu.co/bitstreams/7a673749-fdc7-4f7e-aef5-df911debd514/downloade6502c36f98d25aa5fee18a796cccea4MD5611323/4842oai:repositorio.cuc.edu.co:11323/48422024-09-17 14:06:54.326http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares