Design Patterns for a Knowledge-Driven Analytical Platform
https://doi.org/10.15514/ISPRAS-2022-34(2)-4
Abstract
The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. In order to be viable a modern knowledge-based analytical platform should be able to integrate heterogeneous information, present it to users in an understandable way and to support tools for functionality extensibility. In this paper we introduce an ontological approach to information integration and propose design patterns for developing analytical platform core functionality such as ontology repository management, domain-specific languages (DSLs) generation and source code round-trip synchronization with DSL-models.
About the Authors
Viktor Sergeevich ZAYAKIN
HSE University,
SEUSLAB LLC
Russian Federation
Russian Federation
Master’s Student at National Research University Higher School of Economics, Data Engineer at SEUSLAB LLC
Lyudmila Nikolaevna LYADOVA
HSE University
Russian Federation
Russian Federation
PhD in Computer Science, Associate Professor of the Department of Information Technologies in Business, HSE
Evgeny Andreevich RABCHEVSKIY
SEUSLAB LLC
Russian Federation
Russian Federation
CEO of SEUSLAB LLC
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Review
For citations:
ZAYAKIN V.S., LYADOVA L.N., RABCHEVSKIY E.A. Design Patterns for a Knowledge-Driven Analytical Platform. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2022;34(2):43-56. https://doi.org/10.15514/ISPRAS-2022-34(2)-4
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