9th International Conference on Similarity Search and Applications (SISAP 2016)
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When : 2016-10-24
Where : Tokyo, Japan
Submission Deadline : 2016-05-16
Categories : Computer Science & Information Technology , Database Management Systems
9th International Conference on Similarity Search and Applications (SISAP 2016)
October, 24 - 26, 2016
Tokyo, Japan
Call For Papers
The 9th International Conference on Similarity Search and Applications (SISAP) is an annual forum for researchers and application developers in the area of similarity data management. It aims at the technological problems shared by numerous application domains, such as data mining, information retrieval, multimedia, computer vision, pattern recognition, computational biology, geography, biometrics, machine learning, and many others that make use of similarity search as a necessary supporting service. SISAP is devoted to all issues surrounding the theory, design, analysis, practice, and application of content-based and feature-based similarity search.
Topics
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* Similarity queries - k-NN, range, reverse k-NN, top-k, approximate, etc.
* Similary measures - graph, structural, time series, complex data, tensors, secondary similarity, etc.
* Similarity operations - joins, ranking, classification, categorization, filtering, etc.
* Indexing and access methods for similarity-based processing
* Evaluation techniques for similarity queries and operations
* Merging/combining multiple similarity modalities
* Cost models and analysis for similarity data processing
* Scalability issues and high-performance similarity data management
* Feature selection and extraction for similarity search
* Multimedia retrieval systems
* Security and privacy of similarity search
* Similarity for forensics and security
* Distributed similarity search
* Similarity search cloud services
* Languages for similarity databases
* Applications of similarity-based operations
* Visual analytics for similarity-based operations
* Test collections and benchmarks
* Performance studies and comparisons
* Industrial applications and case studies
* Theory - models of similarity, intrinsic dimensionality, discriminability and contrast, manifolds and subspaces, etc.