Multi-table joins through bitmapped join indices
ACM SIGMOD Record
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
OLAP solutions: building multidimensional information systems
OLAP solutions: building multidimensional information systems
Bitmap index design and evaluation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses with CD Rom
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
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Multidimensional Database Technology
Computer
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Improving the Performance of High-Energy Physics Analysis through Bitmap Indices
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Database Systems Concepts
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
High-Dimensional Similarity Searches Using A Metric Pseudo-Grid
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
A Data and Query Model for Dynamic Playlist Generation
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
CompositeMap: a novel framework for music similarity measure
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
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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.