The 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 (to be announced soon) for more details.

The SISAP initiative (www.sisap.org) is 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 Springer Lecture Notes in Computer Science (LNCS) proceedings. SISAP is a CORE B conference.

A small selection of the best papers presented at the conference will be recommended for inclusion in a special issue of Elsevier Information Systems. These extended versions will be subject to a second round of peer review at the journal.

There will be a Best Paper and Best Student Paper Award.

SISAP 2024 will take place at Brown University in Providence, RI, USA.

UPDATE APRIL 15th: SISAP 2024 will follow a double-blind review process, meaning reviewers will not know the names of the authors and the authors will not know the names of their reviewers. Author submission guidelines will be released soon.

CLICK HERE to register