Abstrakt
NoSQL databases are becoming increasingly more popular due to their undeniable advantages in the context of storing and processing Big Data, mainly horizontal scalability and minimal requirement to define a schema upfront. In the absence of the explicit schema, however, an implicit schema inherent to the stored data still exists and it needs to be reverse engineered from the data.
Once inferred, it is of a great value to the stake-holders and database maintainers. Nevertheless, the problem of schema inference is non-trivial and is still the subject of ongoing research.
In this paper we provide a comparative analysis of five recent proposals of schema inference approaches targeting the JSON format. We provide both static and dynamic comparison of the approaches.
In the former case we compare various features. In the latter case we involve both functional and performance analysis.
Finally, we discuss remaining challenges and open problems.