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Knowledge-based Recommendation for Polyglot Persistence - by Philipp Eisenhuth and Stefan Jablonski


The paper "Knowledge-based Recommendation for Polyglot Persistence" authored by Philipp Eisenhuth and Stefan Jablonski has been accepted for presentation and publication at the First International Workshop on Composable Data Management Systems (CDMS 2022). Below you can find the abstract of the paper.

Knowledge-based Recommendation for Polyglot Persistence
Oftentimes modern applications have converging functionalities, which cannot all be handled with one type of data management system efficiently. This leads to applications requiring a variety of different data management systems for their individual components. Besides the traditional relational database management systems, new systems have been developed in the last few decades that are subsumed under the term NoSQL. The selection of suitable data management systems for each of the application components has potentially great impact on different aspects like performance. Therefore, this selection process needs sophisticated analysis and requires expert knowledge. To reduce the time required for this process and to enable an adequate selection even for developer teams without experts covering all kinds of data management systems, we suggest an approach based on a knowledge-based recommender system. We define its required input and describe how it processes it. As a proof of concept, we show the application of our approach for an exemplary use case. The database recommendation process is embedded in the overall design phases of applications with a polyglot data management landscape.

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