Abstract
In traditional OLAP systems, roll-up and drill-down operations over data cubes exploit fixed hierarchies defined on discrete attributes, which play the roles of dimensions, and operate along them. New emerging application scenarios, such as sensor networks, have stimulated research on OLAP systems, where even continuous attributes are considered as dimensions of analysis, and hierarchies are defined over continuous domains. The goal is to avoid the prior definition of an ad-hoc discretization hierarchy along each OLAP dimension. Following this research trend, in this paper we propose a novel method, founded on a density-based hierarchical clustering algorithm, to support roll-up and drill-down operations over OLAP data cubes with continuous dimensions. The method hierarchically clusters dimension instances by also taking fact-table measures into account. Thus, we enhance the clustering effect with respect to the possible analysis. Experiments on two well-known multidimensional datasets clearly show the advantages of the proposed solution.
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Notes
In our implementation, the clustering algorithm used in the third phase is the well-known DBSCAN (Ester et al. 1996) algorithm which performs a density-based clustering.
References
Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In J.B. Bocca, M. Jarke, C. Zaniolo (Eds.), VLDB’94, Proceedings of 20th international conference on very large data bases, 12–15 Sept 1994, Santiago de Chile, Chile (pp. 487–499). Morgan Kaufmann.
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P. (2005). Automatic subspace clustering of high dimensional data. Data Mining and Knowledge Discovery, 11(1), 5–33.
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D.A., Rabkin, A., Stoica, I., Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
Broder, A.Z. (2002). A taxonomy of web search. SIGIR Forum, 36(2), 3–10.
Cattell, R. (2010). Scalable sql and nosql data stores. SIGMOD Record, 39(4), 12–27.
Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and olap technology. SIGMOD Record, 26(1), 65–74.
Chen, Q., Dayal, U., Hsu, M. (2000). An olap-based scalable web access analysis engine. In Y. Kambayashi, M.K. Mohania, A.M. Tjoa (Eds.), DaWaK, Lecture notes in computer science (Vol. 1874, pp. 210–223). Springer.
Cuzzocrea, A. (2006). Improving range-sum query evaluation on data cubes via polynomial approximation. Data and Knowledge Engineering, 56(2), 85–121.
Cuzzocrea, A., & Serafino, P. (2011). Clustcube: An olap-based framework for clustering and mining complex database objects. In SAC.
Cuzzocrea, A., & Wang, W. (2007). Approximate range-sum query answering on data cubes with probabilistic guarantees. Journal of Intelligent Information Systems, 28(2), 161–197.
Cuzzocrea, A., Saccà, D., Serafino, P. (2007). Semantics-aware advanced olap visualization of multidimensional data cubes. International Journal of Data Warehousing and Mining, 3(4), 1–30.
Cuzzocrea, A., Furfaro, F., Saccà, D. (2009). Enabling olap in mobile environments via intelligent data cube compression techniques. Journal of Intelligent Information Systems, 33(2), 95–143.
Delis, A., Faloutsos, C., Ghandeharizadeh, S., (Eds.) (1999). In SIGMOD 1999, proceedings ACM SIGMOD international conference on management of data, 1–3 June 1999. Philadelphia, PA: ACM Press.
Dong, G., Han, J., Lam, J.M.W., Pei, J., Wang, K. (2001). Mining multi-dimensional constrained gradients in data cubes. In P.M.G. Apers, P. Atzeni, S. Ceri, S. Paraboschi, K. Ramamohanarao, R.T. Snodgrass (Eds.), VLDB (pp. 321–330). Morgan Kaufmann.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD (pp. 226–231).
Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X. (1998). Incremental clustering for mining in a data warehousing environment. In A. Gupta, O. Shmueli, J. Widom (Eds.), VLDB (pp. 323–333). Morgan Kaufmann.
Gao, B., Liu, T.-Y., Ma, W.-Y. (2006). Star-structured high-order heterogeneous data co-clustering based on consistent information theory. In Proceedings of the 6th International Conference on Data Mining, ICDM ’06 (pp. 880–884). Washington, DC: IEEE Computer Society.
Goil, S., & Choudhary, A.N. (2001). Parsimony: an infrastructure for parallel multidimensional analysis and data mining. Journal of Parallel and Distributed Computing, 61(3), 285–321.
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H. (1997). Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Mining and Knowledge Discovery, 1(1), 29–53.
Guha, S., Rastogi, R., Shim, K. (2001). Cure: an efficient clustering algorithm for large databases. Information Systems, 26(1), 35–58.
Gunopulos, D., Kollios, G., Tsotras, V.J., Domeniconi, C. (2005). Selectivity estimators for multidimensional range queries over real attributes. VLDB Journal, 14(2), 137–154.
Han, J. (1998). Towards on-line analytical mining in large databases. SIGMOD Record, 27(1), 97–107.
Han, J., Chee, S.H.S., Chiang, J.Y. (1998). Issues for on-line analytical mining of data warehouses (extended abstract). In SIGMOD’98 workshop on research issues on Data Mining and Knowledge Discovery (DMKD’98).
Hinneburg, A., & Keim, D.A. (1999). Clustering methods for large databases: From the past to the future. In A. Delis, C. Faloutsos, S. Ghandeharizadeh (Eds.), SIGMOD 1999, Proceedings ACM SIGMOD international conference on management of data, 1–3 June 1999, Philadelphia, PA, USA (p. 509). ACM Press.
Ienco, D., Robardet, C., Pensa, R., Meo, R. (2012). Parameter-less co-clustering for star-structured heterogeneous data. Data Mining and Knowledge Discovery, 26(2), 1–38.
Imieliński, T., Khachiyan, L., Abdulghani, A. (2002). Cubegrades: generalizing association rules. Data Mining and Knowledge Discovery, 6(3), 219–257.
Kotidis, Y., & Roussopoulos, N. (2013). Dynamat: A dynamic view management system for data warehouses. In A. Delis, C. Faloutsos, S. Ghandeharizadeh (Eds.), SIGMOD 1999, proceedings ACM SIGMOD international conference on management of data, 1–3 June 1999, Philadelphia, PA, USA (pp. 371–382). ACM Press.
Kriegel, H.-P., Kröger, P., Zimek, A. (2009). Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. Transactions on Knowledge Discovery from Data, 3(1), Article 1.
Messaoud, R.B., Rabaséda, S.L., Boussaid, O., Missaoui, R. (2006). Enhanced mining of association rules from data cubes. In I.-Y. Song, P. Vassiliadis (Eds.), DOLAP (pp. 11–18). ACM.
Ng, R.T. & Han, J. (2002). Clarans: a method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering, 14(5), 1003–1016.
Parsaye, K. (1997). Olap and data mining: bridging the gap. Database Programming and Design, 10, 30–37.
Pio, G., Ceci, M., Loglisci, C., D’Elia, D., Malerba, D. (2012). Hierarchical and overlapping co-clustering of mrna: mirna interactions. In L.D. Raedt, C. Bessière, D. Dubois, P. Doherty, P. Frasconi, F. Heintz, P.J.F. Lucas (Eds.), ECAI, frontiers in artificial intelligence and applications (Vol. 242, pp. 654–659). IOS Press.
Pio, G., Ceci, M., D’Elia, D., Loglisci, C., Malerba, D. (2013). A novel biclustering algorithm for the discovery of meaningful biological correlations between micrornas and their target genes. BMC Bioinformatics, 14(Suppl 7), S8.
Sarawagi, S. (2001). idiff: Informative summarization of differences in multidimensional aggregates. Data Mining and Knowledge Discovery, 5(4), 255–276.
Sarawagi, S., Agrawal, R., Megiddo, N. (1998). Discovery-driven exploration of olap data cubes. In H.-J. Schek, F. Saltor, I. Ramos, G. Alonso (Eds.), EDBT, Lecture notes in computer science (Vol. 1377, pp. 168–182). Springer.
Shanmugasundaram, J., Fayyad, U.M., Bradley, P.S. (1999). Compressed data cubes for olap aggregate query approximation on continuous dimensions. In KDD (pp. 223–232).
Sheikholeslami, G., Chatterjee, S., Zhang, A. (2000). Wavecluster: a wavelet based clustering approach for spatial data in very large databases. VLDB Journal, 8(3–4), 289–304.
SPAETH (2013). Cluster Analysis Datasets. Available at: http://people.sc.fsu.edu/~jburkardt/datasets/spaeth/spaeth.html.
Stojanova, D., Ceci, M., Appice, A., Dzeroski, S. (2011). Network regression with predictive clustering trees. In D. Gunopulos, T. Hofmann, D. Malerba, M. Vazirgiannis (Eds.), ECML/PKDD (3), Lecture notes in computer science (Vol. 6913, pp. 333–348). Springer.
Stojanova, D., Ceci, M., Appice, A., Dzeroski, S. (2012). Network regression with predictive clustering trees. Data Mining and Knowledge Discovery, 25(2), 378–413.
Vens, C., Schietgat, L., Struyf, J., Blockeel, H., Kocev, D., Dzeroski, S. (2010). Predicting gene functions using predictive clustering trees. Springer.
Watson, H.J., & Wixom, B. (2007). The current state of business intelligence. IEEE Computer, 40(9), 96–99.
Yin, X., Han, J., Yu, P.S. (2007). Crossclus: user-guided multi-relational clustering. Data Mining and Knowledge Discovery, 15(3), 321–348.
Zhang, T., Ramakrishnan, R., Livny, M. (1996). Birch: An efficient data clustering method for very large databases. In H. V. Jagadish, I. S. Mumick (Eds.), SIGMOD conference (pp. 103–114). ACM Press.
Zhu, H. (1998). On-line analytical mining of association rules. M.Sc. thesis, Computing Science, Simon Fraser University.
Acknowledgements
The authors thank Lynn Rudd for reading through the paper. This work is in partial fulfillment of the requirements of the Italian project VINCENTE PON02_00563_3470993 “A Virtual collective INtelligenCe ENvironment to develop sustainable Technology Entrepreneurship ecosystems”.
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Ceci, M., Cuzzocrea, A. & Malerba, D. Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering. J Intell Inf Syst 44, 309–333 (2015). https://doi.org/10.1007/s10844-013-0268-1
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DOI: https://doi.org/10.1007/s10844-013-0268-1