SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
DEVise: integrated querying and visual exploration of large datasets
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Testers and visualizers for teaching data structures
SIGCSE '99 The proceedings of the thirtieth SIGCSE technical symposium on Computer science education
Evaluating a class of distance-mapping algorithms for data mining and clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
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
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Amdb: A Visual Access Method Development Tool
UIDIS '99 Proceedings of the 1999 User Interfaces to Data Intensive Systems
Cluster-preserving Embedding of Proteins
Cluster-preserving Embedding of Proteins
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
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The MAMView framework is a data exploration tool that allows developers and users of Metric Access Methods (MAMs) to explore and share dynamic and interactive 3D presentations of a MAM, making the understanding of those structures easier. It is able to create visual representations of metric datasets, including high-dimensional and non-dimensional information. This is achieved by using an extension of the FastMap algorithm. This framework was developed as a practical tool that has been successfully applied to study existing MAMs, helping both new and experienced users to better understand them. The MAMView was also applied to a new under development MAM. With MAMView in hands, the development team of this MAM was able to drill-down its algorithms, quickly finding problems and also potential points for improvement and optimizations. Our focus on this work is on proposing an intuitive yet powerful visualization framework that can be easily employed to build intuitive visual presentations that can bypass the drawback of MAMs dealing with datasets with no spatial representation. Besides MAMView being a powerful visualization tool, its greatest strengths are the ability to interactively explore a visual presentation of a MAM at any level of detail, and the ability to seamlessly query and produce graphical representations in XML format that can be straightforward executed. This paper presents the MAMView framework and its main techniques, describes the current tool, and reports on our experiences in applying it to real applications.