13^th International Conference on Similarity Search and Applications, SISAP 2020

The conference proceedings are available here.

The 13^th 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 retrieval, computer vision, pattern recognition, computational biology, geography, biometrics, machine learning, and many others that need similarity searching as a necessary supporting service. Please see the call for papers for more details.

This year's SISAP features a special session on Artificial Intelligence and Similarity & Similarity Techniques in Machine Learning.

SISAP 2020 also features a doctoral symposium. If you are a PhD student, please consider submitting a paper about your project.

The SISAP initiative (www.sisap.org) aims to become a forum to exchange real-world, challenging and innovative examples of applications, new indexing techniques, common test-beds and benchmarks, source code and up-to-date literature through its web page, serving the similarity search community. Traditionally, SISAP puts emphasis on the distance-based searching, but in general the conference concerns both the effectiveness and efficiency aspects of any similarity search problem.

The series started in 2008 as a workshop and has developed over the years into an international conference with Lecture Notes in Computer Science (LNCS) proceedings.

The following awards were given out at SISAP 2020:

• Best paper: Accelerating Metric Filtering by Improving Bounds on Estimated Distances. Vladimir Mic and Pavel Zezula.
• Best student paper: ADBID: Angle Based Intrinsic Dimensionality. Erik Thordsen and Erich Schubert.
• Best doctoral symposium paper: Temporal Similarity of Trajectories in Graphs. Shima Moghtasedi.

Congratulations to the authors!

The following papers received an invitation to a special issue in Information Systems:

• ADBID: Angle Based Intrinsic Dimensionality. Erik Thordsen and Erich Schubert.
• Parallelizing Filter-Verification based Exact Set Similarity J oins on Multicores. Fabian Fier, Tianzheng Wang, Eric Zhu and Johann-Christoph Freytag.
• Accelerating Metric Filtering by Improving Bounds on Estimated Distances. Vladimir Mic and Pavel Zezula.
• BETULA: Numerically Stable CF-Trees for BIRCH Clustering. Andreas Lang and Erich Schubert.
• GTT: Guiding the Tensor Train Decomposition. Mao-Lin Li, Maria Luisa Sapino and K. Selcuk Candan.