Similarity search and applications : 10th International Conference, SISAP 2017, Munich, Germany, October 4-6, 2017, Proceedings

Description

This book constitutes the refereed proceedings of the 10th International Conference on Similarity Search and Applications, SISAP 2017, held in Munich, Germany, in October 2017. The 23 full papers presented were carefully reviewed and selected from 53 submissions. The papers deal with issues surrounding the theory, design, analysis, practice, and application of content-based and feature-based similarity search. They are organized in the following topical sections: approximate similarity search; improving similarity search methods and applications; distances for complex objects; outlier detection; indexing and applications; and applications and specific domains. Front Matter ....Pages I-XI Front Matter ....Pages 1-1 The Power of Distance Distributions: Cost Models and Scheduling Policies for Quality-Controlled Similarity Queries (Paolo Ciaccia, Marco Patella)....Pages 3-16 Cache and Priority Queue Based Approximation Technique for a Stream of Similarity Search Queries (Filip Nalepa, Michal Batko, Pavel Zezula)....Pages 17-33 ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms (Martin Aumüller, Erik Bernhardsson, Alexander Faithfull)....Pages 34-49 Front Matter ....Pages 51-51 Sketches with Unbalanced Bits for Similarity Search (Vladimir Mic, David Novak, Pavel Zezula)....Pages 53-63 Local Intrinsic Dimensionality I: An Extreme-Value-Theoretic Foundation for Similarity Applications (Michael E. Houle)....Pages 64-79 Local Intrinsic Dimensionality II: Multivariate Analysis and Distributional Support (Michael E. Houle)....Pages 80-95 High-Dimensional Simplexes for Supermetric Search (Richard Connor, Lucia Vadicamo, Fausto Rabitti)....Pages 96-109 Improving k-NN Graph Accuracy Using Local Intrinsic Dimensionality (Michael E. Houle, Vincent Oria, Arwa M. Wali)....Pages 110-124 Front Matter ....Pages 125-125 Dynamic Time Warping and the (Windowed) Dog-Keeper Distance (Jörg P. Bachmann, Johann-Christoph Freytag)....Pages 127-140 Fast Similarity Search with the Earth Mover’s Distance via Feasible Initialization and Pruning (Merih Seran Uysal, Kai Driessen, Tobias Brockhoff, Thomas Seidl)....Pages 141-155 A New Perspective on the Tree Edit Distance (Stefan Schwarz, Mateusz Pawlik, Nikolaus Augsten)....Pages 156-170 Front Matter ....Pages 171-171 Good and Bad Neighborhood Approximations for Outlier Detection Ensembles (Evelyn Kirner, Erich Schubert, Arthur Zimek)....Pages 173-187 Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection (Erich Schubert, Michael Gertz)....Pages 188-203 Front Matter ....Pages 205-205 Scalable Similarity Search for Molecular Descriptors (Yasuo Tabei, Simon J. Puglisi)....Pages 207-219 Self-indexed Motion Planning (Angello Hoyos, Ubaldo Ruiz, Eric Tellez, Edgar Chavez)....Pages 220-233 Practical Space-Efficient Data Structures for High-Dimensional Orthogonal Range Searching (Kazuki Ishiyama, Kunihiko Sadakane)....Pages 234-246 Semantic Similarity Group By Operators for Metric Data (Natan A. Laverde, Mirela T. Cazzolato, Agma J. M. Traina, Caetano Traina Jr.)....Pages 247-261 Succinct Quadtrees for Road Data (Kazuki Ishiyama, Koji Kobayashi, Kunihiko Sadakane)....Pages 262-272 Front Matter ....Pages 273-273 On Competitiveness of Nearest-Neighbor-Based Music Classification: A Methodological Critique (Haukur Pálmason, Björn Þór Jónsson, Laurent Amsaleg, Markus Schedl, Peter Knees)....Pages 275-283 DS-Prox: Dataset Proximity Mining for Governing the Data Lake (Ayman Alserafi, Toon Calders, Alberto Abelló, Oscar Romero)....Pages 284-299 DeepBrowse: Similarity-Based Browsing Through Large Lists (Extended Abstract) (Haochen Chen, Arvind Ram Anantharam, Steven Skiena)....Pages 300-314 Malware Discovery Using Behaviour-Based Exploration of Network Traffic (Jakub Lokoč, Tomáš Grošup, Přemysl Čech, Tomáš Pevný, Tomáš Skopal)....Pages 315-323 Visual Analytics and Similarity Search: Concepts and Challenges for Effective Retrieval Considering Users, Tasks, and Data (Daniel Seebacher, Johannes Häußler, Manuel Stein, Halldor Janetzko, Tobias Schreck, Daniel A. Keim)....Pages 324-332 Back Matter ....Pages 333-333