Abstract
Measurement of Performances Indicators (PIs) in highly distributed environments, especially in networked organisations, is particularly critical because of heterogeneity issues and sparsity of data. In this paper we present a semantics-based approach for dynamic calculation of PIs in the context of sparse distributed data marts. In particular, we propose to enrich the multidimensional model with the formal description of the structure of an indicator given in terms of its algebraic formula and aggregation function. Upon such a model, a set of reasoning-based functionalities are capable to mathematically manipulate formulas for dynamic aggregation of data and computation of indicators on-the-fly, by means of recursive application of rewriting rules based on logic programming.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
We do not focus on this step as it depends on the specific technology used for storage.
- 3.
It is straightforward to see that the result \(R(q_c)\) of the query \(q_c\), derived by applying the Rule 1 to the query q, is a subset of R(q).
- 4.
As the procedure explores the search space, if a solution exists, it is found whatever rule is chosen, although the order is critical w.r.t. execution time.
- 5.
As described in Sect. 3, in this drill-down for ACME1 we consider only the cities x such that the relations \(partOf_{A_1}(x,Spain)\) hold in the data mart.
- 6.
- 7.
Experiments have been carried on a personal computer powered by an Intel Core i7-3630QM with 8 GB memory, running Linux Fedora 20.
References
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)
Diamantini, C., Genga, L., Potena, D., Storti, E.: Collaborative building of an ontology of key performance indicators. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 148–165. Springer, Heidelberg (2014)
Diamantini, C., Potena, D., Storti, E.: SemPI: a semantic framework for the collaborative construction and maintenance of a shared dictionary of performance indicators. Future Generation Comput. Syst. (2015). http://dx.doi.org/10.1016/j.future.2015.04.011
Golfarelli, M., Rizzi, S.: Data Warehouse Design: Modern Principles and Methodologies, 1st edn. McGraw-Hill Inc, New York (2009)
Diamantini, C., Potena, D., Storti, E.: Extending drill-down through semantic reasoning on indicator formulas. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 57–68. Springer, Heidelberg (2014)
Sterling, L., Bundy, A., Byrd, L., O’Keefe, R., Silver, B.: Solving symbolic equations with press. J. Symb. Comput. 7(1), 71–84 (1989)
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2001)
Lenzerini, M.: Data integration: a theoretical perspective. In: Proceedings of the Twenty-first ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. PODS 2002, pp. 233–246. ACM, New York, NY, USA (2002)
Halevy, A.Y.: Answering queries using views: a survey. VLDB J. 10(4), 270–294 (2001)
Cohen, S., Nutt, W., Sagiv, Y.: Rewriting queries with arbitrary aggregation functions using views. ACM Trans. Database Syst. 31(2), 672–715 (2006)
Golfarelli, M., Mandreoli, F., Penzo, W., Rizzi, S., Turricchia, E.: Olap query reformulation in peer-to-peer data warehousing. Inf. Syst. 37(5), 393–411 (2012)
Tseng, F.S., Chen, C.W.: Integrating heterogeneous data warehouses using xml technologies. J. Inf. Sci. 31(3), 209–229 (2005)
Neumayr, B., Anderlik, S., Schrefl, M.: Towards Ontology-based OLAP: Datalog-based Reasoning over Multidimensional Ontologies. In: Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP, pp. 41–48 (2012)
Prat, N., Megdiche, I., Akoka, J.: Multidimensional models meet the semantic web: defining and reasoning on owl-dl ontologies for olap. In: Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP. DOLAP 2012, pp. 17–24. ACM, New York, NY, USA (2012)
Xie, G.T., Yang, Y., Liu, S., Qiu, Z., Pan, Y., Zhou, X.: EIAW: towards a business-friendly data warehouse using semantic web technologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 857–870. Springer, Heidelberg (2007)
Kehlenbeck, M., Breitner, M.H.: Ontology-based exchange and immediate application of business calculation definitions for online analytical processing. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 298–311. Springer, Heidelberg (2009)
Priebe, T., Pernul, G.: Ontology-based integration of OLAP and information retrieval. In: Proceedings of DEXA Workshops, pp. 610–614 (2003)
Horkoff, J., Barone, D., Jiang, L., Yu, E., Amyot, D., Borgida, A., Mylopoulos, J.: Strategic business modeling: representation and reasoning. Softw. Syst. Model. 13(3), 1015–1041 (2012)
Popova, V., Sharpanskykh, A.: Modeling organizational performance indicators. Inf. Syst. 35(4), 505–527 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Diamantini, C., Potena, D., Storti, E. (2015). Semantics-Based Multidimensional Query Over Sparse Data Marts. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-22729-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22728-3
Online ISBN: 978-3-319-22729-0
eBook Packages: Computer ScienceComputer Science (R0)