There are many different aspects to BIG DATA whether challenging Veracity or Variety they all require sophisticated statistical analysis, provided by machine learning. While Volume and Velocity are impossible without efficient technical solutions. Our research interests are spread among these directions. Most of our experience comes from the field of Information Retrieval and Databases.
Main research projects
Theoretical Machine Learning (ML)
Tree models, sequence analysis, ensembles, and GPU enabled ML
Search Engines (SE) and Information Retrieval (IR)
ML for Ranking, SE User Behaviour Analysis, SE performance, SE evaluation and Storage and processing of scientific and graph data
Information Management
Stream processing, declarative computation in BD environment. Efficient storage and index structures, e.g column-oriented DB. Optimization and execution of declarative queries and workflows. The holistic application, optimization, and tuning. Data quality. Consistency and high reliability.
Beside the research projects, we deliver special courses:
- Machine Learning @Computer Science Centre (https://compscicenter.ru/courses/machine-learning-1/2016-autumn/, https://compscicenter.ru/courses/machine-learning-2/2017-spring/)
- Search Engine Architecture @St.Petersburg State University
- Information Management @St.Petersburg State University
- Database algorithms @St.Petersburg State University
Students interested in research problems in the areas of our interest are welcome to join our lab. The best way to learn more about our research is to take our courses or attend our open seminars. New projects are launched regularly, sometimes it is also possible to join an ongoing project or extend its scope. Please contact the project leader for information on a specific project.
All students willing to join our projects must be skilled in either statistics or programming, preferably in both. The successful candidates will be invited to join one of the projects as a regular team member.
News
More postsPublications
- arXiv preprint,
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In Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems (DEBS '19). ACM, New York, NY, USA, 264-265.
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The 27th ACM International Conference on Information and Knowledge Management (CIKM ’18), October 22–26, 2018, Torino, Italy. ACM, New York, NY, USA, 10 pages
- In: Benczúr A., Thalheim B., Horváth T. (eds) Advances in Databases and Information Systems. ADBIS 2018. Lecture Notes in Computer Science, vol 11019. Springer, Cham,
- In: Benczúr A. et al. (eds) New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham,
- BeyondMR'18 Proceedings of the 5th ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond,
- Proceedings of the Third Conference on Software Engineering and Information Management. Saint Petersburg, Russia. CEUR Workshop Proceedings, 2135,
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Message from the editorsCEUR Workshop Proceedings, 1864,
- In Marite Kirikova, Kjetil Nørvåg, George A. Papadopoulos, Johann Gamper, Robert Wrembel, Jérôme Darmont, and Stefano Rizzi, editors, New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops, AMSD, BigNovelTI, DAS, SW4CH, DC, Nicosia, Cyprus, September 24-27, 2017, Proceedings, volume 767 of Communications in Computer and Information Science, pages 275–284. Springer,
- In Yassine Ouhammou, Mirjana Ivanovic, Alberto Abelló, and Ladjel Bellatreche, editors, Model and Data Engineering - 7th International Conference, MEDI 2017, Barcelona, Spain, October 4-6, 2017, Proceedings, volume 10563 of Lecture Notes in Computer Science, pages 208–222. Springer,
- CEUR Workshop Proceedings, 1864,
- Selected Papers of the XVIII International Conference on Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2016), Ershovo, Moscow Region, Russia,
Igor Kuralenok
Nikita Marshalkin
Boris Novikov
Mikhail Shavkunov
Aleksei Shpilman
Artem Trofimov