Abstract
In the paper we present an idea for a ‘Laboratory for Socio-Economic Data Analysis’ aiming to explore the use of extremely large amounts of socio-economic data from several sources of various degrees of heterogeneity. Based on these data, and on the state-of-the-art techniques in knowledge extraction and processing, we intend to deploy robust and high performance data analytics processes. Our goal is to enable the two data-bearing business partners Irish Times and Handelsblatt to use the data they have in conjunction with bigger data from external sources, to increase the value of their products and services offered and to reposition themselves in the market. We focus on two use cases that can produce tangible results in the analysis of socio-economic trends (e.g. unemployment, poverty) and socio-economic events (e.g. election tracking, bankruptcy) enabling better reporting, as well as timely decision support in crisis situations.
Chapter PDF
References
Arenas, M., et al.: - A Direct Mapping of Relational Data to RDF - W3C Recommendation (September 27, 2012), http://www.w3.org/TR/rdb-direct-mapping/
Bechhofer, S., et al.: Tackling the ontology acquisition bottleneck: An experiment in ontology reengineering, 2003 (April 2008), http://tinyurl.com/96w7ms
Bobillo, F., Straccia, U.: fuzzyDL: An expressive fuzzy description logic reasoner. In: Proceedings of FUZZ-2008 (2008)
Buitelaar, P., Cimiano, P.: Ontology Learning and Population. IOS Press (2008)
Castellanos, M., Ghosh, R., Dekhil, M., Ruiz, P., Bellad, S., Dayal, U., Hsu, M., Schreimann, M.: Tapping social media for sentiments in real-time. In: HP TechCon (2011)
Cherkassky, V., Mulier, F.: Learning from Data. Wiley-Interscience, New York (1998)
Das, S., et al.: - R2RML: RDB to RDF Mapping Language - W3C Recommendation (September 27, 2012), http://www.w3.org/TR/r2rml/
Dzeroski, S., Lavrac, N.: Relational Data Mining. Springer (2001)
Fearne, A.: Sustainable Food and Wine Value Chains, Adelaide Thinker in Residence, Department of the Premier and Cabinet (2009) ISBN 978-0-9804829-9-7
Feldman, R., Sanger, J.: The Text Mining Handbook. Cambridge University Press (2006)
Flood, M., Jagadish, H.V., Kyle, A., Olken, F., Raschid, L.: Using Data for Systemic Financial Risk Management. In: Proc. Fifth Biennial Conf. Innovative Data Systems Research (January 2011)
Fisher, D., DeLine, R., Czerwinski, M., Drucker, S.: Interactions with big data analytics. Interactions 19(3), 50–59 (2012)
Haase, P., Voelker, J.: Ontology learning and reasoning - dealing with uncertainty and inconsistency. In: Proceedings of the URSW2005 Workshop, pp. 45–55 (November 2005)
Hartig, O.: Querying Trust in RDF Data with tSPARQL. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 5–20. Springer, Heidelberg (2009)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2001)
Hustadt, U., Motik, B., Sattler, U.: Data complexity of reasoning in very expressive description logics. In: Proc. IJCAI 2005, pp. 466–471. Professional Book Center (2005)
Jagadish, H.V.: Challenges and Opportunities with Big Data (2012), http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf (last accessed date: June 28, 2012)
Keogh, E., Smyth, P.: A probabilistic approach to fast pattern matching in time series databases. In: Third International Conference on Knowledge Discovery and Data Mining (1997)
Kiefer, C., Bernstein, A., Stocker, M.: The fundamentals of isparql: A virtual triple approach for similarity-based semantic web tasks. In: Aberer, K., et al. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 295–309. Springer, Heidelberg (2007)
Maedche, A.: Emergent semantics for ontologies. In: Emergent Semantics, IEEE Intelligent Systems. IEEE Press (2002)
Maedche, A., Staab, S.: Ontology learning. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, ch. 9, pp. 173–190. Springer (2004)
Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (SIGMOD 2010), pp. 1155–1158. ACM, New York (2010), http://doi.acm.org/10.1145/1807167.1807306 , doi:10.1145/1807167.1807306
Mazzieri, M.: A fuzzy RDF semantics to represent trust metadata. In: Proceedings of SWAP 2004 (2004)
Oren, E., Guéret, C., Schlobach, S.: Anytime query answering in RDF through evolutionary algorithms. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 98–113. Springer, Heidelberg (2008)
Ottens, K., Aussenac-Gilles, N., Gleizes, M.-P., Camps, V.: Dynamic ontology co-evolution from texts: Principles and case study. In: Proceedings of ESOE 2007 Workshop, CEUR-WS, pp. 70–83 (2007)
Russom, P.: ‘Big Data Analytics’, TWDI Best Practices Report, Q4 (2011)
Schueler, B., Sizov, S., Staab, S., Tran, D.T.: Querying for meta knowledge. In: Proceedings of WWW 2008. ACM (2008)
Udrea, O., Deng, Y., Ruckhaus, E., Subrahmanian, V.S.: A graph theoretical foundation for integrating RDF ontologies. In: Proceedings of AAAI 2005 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Ryan, J., Heilmann, D., Handschuh, S. (2013). SEDA_Lab: Towards a Laboratory for Socio-Economic Data Analysis. In: Camarinha-Matos, L.M., Scherer, R.J. (eds) Collaborative Systems for Reindustrialization. PRO-VE 2013. IFIP Advances in Information and Communication Technology, vol 408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40543-3_75
Download citation
DOI: https://doi.org/10.1007/978-3-642-40543-3_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40542-6
Online ISBN: 978-3-642-40543-3
eBook Packages: Computer ScienceComputer Science (R0)