Abstract
Several business applications such as marketing basket analysis, clickstream analysis, fraud detection and churning migration analysis demand gradient data analysis. By employing gradient data analysis one is able to identify trends, outliers and answering “what-if” questions over large databases. Gradient queries were first introduced by Imielinski et al [1] as the cubegrade problem. The main idea is to detect interesting changes in a multidimensional space (MDS). Thus, changes in a set of measures (aggregates) are associated with changes in sector characteristics (dimensions). MDS contains a huge number of cells which poses great challenge for mining gradient cells on a useful time. Dong et al [2] have proposed gradient constraints to smooth the computational costs involved in such queries. Even by using such constraints on large databases, the number of interesting cases to evaluate is still large. In this work, we are interested to explore best cases (Top-K cells) of interesting multidimensional gradients. There several studies on Top-K queries, but preference queries with multidimensional selection were introduced quite recently by Dong et al [9]. Furthermore, traditional Top-K methods work well in presence of convex functions (gradients are non-convex ones). We have revisited iceberg cubing for complex measures, since it is the basis for mining gradient cells. We also propose a gradient-based cubing strategy to evaluate interesting gradient regions in MDS. Thus, the main challenge is to find maximum gradient regions (MGRs) that maximize the task of mining Top-K gradient cells. Our performance study indicates that our strategy is effective on finding the most interesting gradients in multidimensional space.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Imielinski, T., Khachiyan, L., Abdulghani, A.: Cubegrades: Generalizing Association Rules. Data Mining and Knowledge Discovery (2002)
Dong, G., Han, J., Lam, J.M.W., Pei, J., Wang, K., Zou, W.: Mining Constrained Gradients in Large Databases. IEEE Transactions on Knowledge Discovery and Data Engineering (2004)
Sarawagi, S., Agrawal, R., Megiddo, N.: Discovery-Driven Exploration of OLAP Data Cubes. In: Proc. Int. Conference on Extending Database Technology (EDBT) (1998)
Sarawagi, S., Sathe, G.: i3: Intelligent, Interactive Investigaton of OLAP data cubes. In: Proc. Int. Conference on Management of Data (SIGMOD) (2000)
Sathe, G., Sarawagi, S.: Intelligent Rollups in Multidimensional OLAP Data. In: Proc. Int. Conference on Very Large Databases (VLDB) (2001)
Chang, Y., Bergman, L., Castelli, V., Li, M.L.C., Smith, J.: Onion technique: Indexing for linear optimization queries. In: Proc. Int. Conference on Management of Data (SIGMOD) (2000)
Hristidis, V., Koudas, N., Papakonstantinou, Y.: Prefer: A system for the efficient execution of multi-parametric ranked queries. In: Proc. Int. Conference on Management of Data (SIGMOD) (2001)
Bruno, N., Chaudhuri, S., Gravano, L.: Top-k selection queries over relational databases: Mapping strategies and performance evaluation. ACM Transactions on Database Systems (2002)
Dong, X., Han, J., Cheng, H., Xiaolei, L.: Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach. In: Proc. Int. Conference on Very Large Databases (VLDB) (2006)
Han, J., Pei, J., Dong, G., Wank, K.: Efficient Computation of Iceberg Cubes with Complex Measures. In: Proc. Int. Conference on Management of Data (SIGMOD) (2001)
Li, X., Han, J., Gonzalez, H.: High-dimensional OLAP: A Minimal Cubing Approach. In: Proc. Int. Conference on Very Large Databases (VLDB) (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alves, R., Belo, O., Ribeiro, J. (2007). Mining Top-K Multidimensional Gradients. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-74553-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74552-5
Online ISBN: 978-3-540-74553-2
eBook Packages: Computer ScienceComputer Science (R0)