Abstract
An increasing number of applications include recommender systems that have to perform search in a non-metric similarity space, thus creating an increasing demand for efficient yet flexible indexing techniques to facilitate similarity search. This demand is further fueled by the growing volume of data available to recommender systems.
This paper addresses the demand in the specific domain of music recommendation. The paper presents the Music On Demand framework where music retrieval is performed in a continuous, stream-based fashion. Similarity measures between songs, which are computed on high-dimensional feature spaces, often do not obey the triangular inequality, meaning that existing indexing techniques for high-dimensional data are infeasible.
The most prominent contribution of the paper is the proposal of an indexing approach that is effective for non-metric similarities. This is achieved by using a number of bitmap indexes combined with effective bitmap compression techniques. Experiments show that the approach scales well.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Silberschatz, A., Korth, H., Sudershan, S.: Database System Concepts, 4th edn. McGraw-Hill, New York (2005)
Aucouturier, J., Pachet, F.: Music Similarity Measures: What’s the Use? In: Proc. of ISMIR, pp. 157–163 (2002)
Aucouturier, J.-J., Pachet, F.: Improving Timbre Similarity: How high’s the sky? Journal of Negative Results in Speech and Audio Sciences 1(1) (2004)
Zhang, Q.X.B., Shen, J., Wang, Y.: CompositeMap: a Novel Framework for Music Similarity Measure. In: Proc. of SIGIR, pp. 403–410 (1999)
Chan, C.Y., Ioannidis, Y.E.: Bitmap Index Design and Evaluation. In: Proc. of SIGMOD, pp. 355–366 (1998)
Ciaccia, P., Patella, M., Zezula, P.: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In: Proc. of VLDB, pp. 426–435 (1997)
Digout, C., Nascimento, M.A.: High-Dimensional Similarity Searches Using A Metric Pseudo-Grid. In: Proc of ICDEW, pp. 1174–1183 (2005)
Jensen, C.A., Mungure, E., Pedersen, T.B., Sørensen, K.: A Data and Query Model for Dynamic Playlist Generation. In: Proc. of MDDM (2007)
Kimball, R., Reeves, L., Thornthwaite, W., Ross, M., Thornwaite, W.: The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses. Wiley, Chichester (1998)
Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. Wiley, Chichester (2002)
Logan, B., Salomon, A.: A Music Similarity Function based on Signal Analysis. In: Proc. of ICME, pp. 745–748 (2001)
Lübbers, D.: SoniXplorer: Combining Visualization and Auralization for Content-Based Exploration of Music Collections. In: Proc. of ISMIR, pp. 590–593 (2005)
Mandel, M., Ellis, D.: Song-Level Features and Support Vector Machines for Music Classification. In: Proc. of ISMIR, pp. 594–599 (2005)
Neumayer, R., Dittenbach, M., Rauber, A.: PlaySOM and PocketSOMPlayer, Alternative Interfaces to Large Music Collections. In: Proc. of ISMIR, pp. 618–623 (2005)
O’Neil, P., Graefe, G.: Multi-table Joins Through Bitmapped Join Indices. ACM SIGMOD Record 24(3), 8–11 (1995)
O’Neil, P., Quass, D.: Improved Query Performance with Variant Indexes. In: Proc. of SIGMOD, pp. 38–49 (1997)
Pampalk, E.: Speeding up Music Similarity. In: Proc. of MIREX (2005)
Pampalk, E., Flexer, A., Widmer, G.: Improvements of Audio-Based Music Similarity and Genre Classification. In: Proc. of ISMIR, pp. 628–633 (2005)
Pampalk, E., Pohle, T., Widmer, G.: Dynamic Playlist Generation Based on Skipping Behavior. In: Proc. of ISMIR, pp. 634–637 (2005)
Pedersen, T.B., Jensen, C.S.: Multidimensional Database Technology. IEEE Computer 34(12), 40–46 (2001)
Pohle, T., Pampalk, E., Widmer, G.: Generating Similarity-based Playlists Using Traveling Salesman Algorithms. In: Proc. of DAFx, pp. 220–225 (2005)
Stockinger, K., Düllmann, D., Hoschek, W., Schikuta, E.: Improving the Performance of High-Energy Physics Analysis through Bitmap Indices. In: Ibrahim, M., Küng, J., Revell, N. (eds.) DEXA 2000. LNCS, vol. 1873, pp. 835–845. Springer, Heidelberg (2000)
Thomsen, E.: OLAP Solutions: Building Multidimensional Information Systems. Wiley, Chichester (1997)
Wu, K., Otoo, E.J., Shoshani, A.: Optimizing Bitmap Indices With Efficient Compression. ACM TODS 31(1), 1–38 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jensen, C.A., Mungure, E.M., Pedersen, T.B., Sørensen, K., Deliège, F. (2010). Effective Bitmap Indexing for Non-metric Similarities. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15364-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-15364-8_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15363-1
Online ISBN: 978-3-642-15364-8
eBook Packages: Computer ScienceComputer Science (R0)