Abstract
Data Warehouse (DW) and OLAP systems are effective solutions for the online analysis of large volumes of data structured as cubes. Usually organizations and enterprises require several cubes for their activities. In this context, we define a new kind of queries: “Top-k Cubes queries”. Top-K cubes queries allow searching the most relevant k-cubes among a collection of cubes. Then, in this paper we propose a first framework for Top-K cubes queries where queries are expressed in natural language to meet the easiness need of unskilled IT decision-makers. An implementation in a ROLAP architecture is also provided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kuchmann-Beauger, N., Aufaure, M.-A.: A natural language interface for data warehouse question answering. In: Muñoz, R., Montoyo, A., Métais, E. (eds.) NLDB 2011. LNCS, vol. 6716, pp. 201–208. Springer, Heidelberg (2011)
Giacometti, A., Marcel, P., Negre, E.: A framework for recommending OLAP queries. In: 11th International Workshop on Data Warehousing and OLAP, DOLAP 2008, pp. 73–80. Proceedings of the ACM, New York (2008)
Golfarelli, M., Rizzi, S., Biondi, P.: myOLAP: An approach to express and evaluate OLAP preferences. IEEE Trans. Knowl. Data Eng. 23(7), 1050–1064 (2011)
Jerbi, H., Ravat, F., Teste, O., Zurfluh, G.: Preference-based recommendations for OLAP analysis. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 467–478. Springer, Heidelberg (2009)
Kozmina, N.: Adding recommendations to OLAP reporting tool. In: 5th International Conference on Enterprise Information Systems, vol. 1, pp. 169–176. ICEIS, France (2013)
Khemiri, R., Bentayeb, F.: FIMIOQR: Frequent itemsets mining for interactive OLAP query recommendation. In: The Fifth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA), pp. 9–14. DBKDA, Seville, Spain (2013)
Xin, D., Han, J., Cheng, H., Li, X.: Answering Top-k queries with multi-dimensional selections: The ranking cube approach. In: 32nd International Conference on Very Large Data Bases, pp. 463–474. Korea (2006)
Luo, Z.W., Ling, T.-W., Ang, C.-H., Lee, S.-Y., Cui, B.: Range top/bottom k queries in OLAP sparse data cubes. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, p. 678. Springer, Heidelberg (2001)
Loh, Z.X., Ling, T.-W., Ang, C.-H., Lee, S.-Y.: Adaptive method for range top-k queries in OLAP data cubes. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, p. 648. Springer, Heidelberg (2002)
Ding, B., Zhao, B., Lin, C.X., Han, J., Zhai, C.: Topcells: keyword-based search of Top-k aggregated documents in text cube. In: IEEE International Conference Data Eng. (ICDE), pp. 381–384 (2010)
Sagayaraj Francis, F., Xavier, P.P.: An effective method to answer OLAP queries using R*-Trees in distributed environment. Int. J. Comput. Appl. IJCA 107 (2014)
Bimonte, S., Pradel, M., Boffety, D., Tailleur, A., André, G., Bzikha, R., Chanet, J.-P.: A New sensor-based spatial OLAP architecture centered on an agricultural farm energy-use diagnosis tool. IJDSST 5(4), 1–20 (2013)
Abelló, A., Darmont, J., Etcheverry, L., Golfarelli, M., Mazón López, J.N., Naumann, F., Vossen, G.: Fusion cubes: towards self-service business intelligence. Int. J. Data Warehousing Min. IJDWM 9(2), 66–88. USA (2013)
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
Djiroun, R., Bimonte, S., Boukhalfa, K. (2015). A First Framework for Top-K Cubes Queries. In: Jeusfeld, M., Karlapalem, K. (eds) Advances in Conceptual Modeling. ER 2015. Lecture Notes in Computer Science(), vol 9382. Springer, Cham. https://doi.org/10.1007/978-3-319-25747-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-25747-1_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25746-4
Online ISBN: 978-3-319-25747-1
eBook Packages: Computer ScienceComputer Science (R0)