- -

Automating Data Integration in Adaptive and Data-Intensive Information Systems

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Automating Data Integration in Adaptive and Data-Intensive Information Systems

Show full item record

Galvão, J.; León-Palacio, A.; Costa, C.; Santos, MY.; Pastor López, O. (2020). Automating Data Integration in Adaptive and Data-Intensive Information Systems. Springer Nature. 20-34. https://doi.org/10.1007/978-3-030-63396-7_2

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/179348

Files in this item

Item Metadata

Title: Automating Data Integration in Adaptive and Data-Intensive Information Systems
Author: Galvão, João León-Palacio, Ana Costa, Carlos Santos, Maribel Yasmina Pastor López, Oscar
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Abstract:
[EN] Data acquisition is no longer a problem for organizations, as many efforts have been performed in automating data collection and storage, providing access to a wide amount of heterogeneous data sources that can be ...[+]
Subjects: Big Data , Data integration , Schema matching , Similarity measures
Copyrigths: Reserva de todos los derechos
ISBN: 978-3-030-63395-0
Source:
Information Systems. 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25-26, 2020, Proceedings. (issn: 1865-1348 )
DOI: 10.1007/978-3-030-63396-7_2
Publisher:
Springer Nature
Publisher version: https://doi.org/10.1007/978-3-030-63396-7_2
Conference name: 17th European, Mediterranean and Middle Eastern Conference on Information Systems (EMCIS 2020)
Conference place: Online
Conference date: Noviembre 25-26,2020
Series: Lecture Notes in Business Information Processing;402
Project ID:
info:eu-repo/grantAgreement/FCT//UID%2FCEC%2F00319%2F2019/
...[+]
info:eu-repo/grantAgreement/FCT//UID%2FCEC%2F00319%2F2019/
info:eu-repo/grantAgreement/FCT//PD%2FBDE%2F135100%2F2017/
info:eu-repo/grantAgreement/FEDER//POCI-01-0247-FEDER-039479/
info:eu-repo/grantAgreement/GVA//ACIF%2F2018%2F171//SOPORTE ONTOLOGICO Y TECNOLOGICO PARA EL DESARROLLO DE APLICACIONES BIG DATA/
info:eu-repo/grantAgreement///TIN2016-80811-P//UN METODO DE PRODUCCION DE SOFTWARE DIRIGIDO POR MODELOS PARA EL DESARROLLO DE APLICACIONES BIG DATA/
info:eu-repo/grantAgreement///PROMETEO%2F2018%2F176//GISPRO-GENOMIC INFORMATION SYSTEMS PRODUCTION/
[-]
Thanks:
This work has been supported by FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019, the Doctoral scholarship PD/BDE/135100/2017 and European Structural and Investment Funds in the FEDER ...[+]
Type: Comunicación en congreso Artículo Capítulo de libro

References

Krishnan, K.: Data Warehousing in the Age of Big Data. Newnes (2013)

Vaisman, A., Zimányi, E.: Data warehouses: next challenges. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2011. LNBIP, vol. 96, pp. 1–26. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27358-2_1

Costa, C., Santos, M.Y.: Evaluating several design patterns and trends in big data warehousing systems. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 459–473. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_28 [+]
Krishnan, K.: Data Warehousing in the Age of Big Data. Newnes (2013)

Vaisman, A., Zimányi, E.: Data warehouses: next challenges. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2011. LNBIP, vol. 96, pp. 1–26. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27358-2_1

Costa, C., Santos, M.Y.: Evaluating several design patterns and trends in big data warehousing systems. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 459–473. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_28

Bellahsene, Z., Bonifati, A., Duchateau, F., Velegrakis, Y.: On Evaluating Schema Matching and mapping. In: Bellahsene, Z., Bonifati, A., Rahm, E. (eds.) Schema Matching and Mapping, pp. 253–291. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-16518-4_9

Santos, M.Y., Costa, C., Galvão, J., Andrade, C., Pastor, O., Marcén, A.C.: Enhancing big data warehousing for efficient, integrated and advanced analytics - visionary paper. In: Cappiello, C., Ruiz, M. (eds.) CAiSE Forum 2019. LNBIP, vol. 350, pp. 215–226. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-21297-1_19

Bernstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching. Ten Years Later. PVLDB 4, 695–701 (2011)

Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: Proceedings of the 27th International Conference on Very Large Data Bases, pp. 49–58. Morgan Kaufmann Publishers Inc., San Francisco (2001)

Shirkhorshidi, A.S., Aghabozorgi, S., Wah, T.Y.: A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS ONE 10, e0144059 (2015). https://doi.org/10.1371/journal.pone.0144059

Xiao, C., Wang, W., Lin, X., Shang, H.: Top-k set similarity joins. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, pp. 916–927. IEEE Computer Society, Washington, DC (2009). https://doi.org/10.1109/ICDE.2009.111

Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Phys. Doklady 10, 707 (1966)

Jaccard, P.: Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr. Corbaz, Lausanne (1901)

Winkler, W.E.: String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage [microform]/William E. Winkler. Distributed by ERIC Clearinghouse, [Washington, D.C.] (1990)

Zhu, E., Nargesian, F., Pu, K.Q., Miller, R.J.: LSH ensemble: internet-scale domain search. Proc. VLDB Endow. 9, 1185–1196 (2016). https://doi.org/10.14778/2994509.2994534

Banek, M., Vrdoljak, B., Tjoa, A.M.: Using ontologies for measuring semantic similarity in data warehouse schema matching process. In: 2007 9th International Conference on Telecommunications, pp. 227–234 (2007). https://doi.org/10.1109/CONTEL.2007.381876

Deb Nath, R.P., Hose, K., Pedersen, T.B.: Towards a programmable semantic extract-transform-load framework for semantic data warehouses. In: Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP, pp. 15–24. ACM, New York (2015). https://doi.org/10.1145/2811222.2811229

Abdellaoui, S., Nader, F.: Semantic data warehouse at the heart of competitive intelligence systems: design approach. In: 2015 6th International Conference on Information Systems and Economic Intelligence (SIIE), pp. 141–145 (2015). https://doi.org/10.1109/ISEI.2015.7358736

El Hajjamy, O., Alaoui, L., Bahaj, M.: Semantic integration of heterogeneous classical data sources in ontological data warehouse. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, pp. 36:1–36:8. ACM, New York (2018). https://doi.org/10.1145/3230905.3230929

Maccioni, A., Torlone, R.: KAYAK: a framework for just-in-time data preparation in a data lake. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 474–489. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91563-0_29

Hai, R., Geisler, S., Quix, C.: Constance: an intelligent data lake system. In: Proceedings of the 2016 International Conference on Management of Data, pp. 2097–2100. ACM, New York (2016). https://doi.org/10.1145/2882903.2899389

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record