In this vision paper, we introduce an idea of a framework that would enable us to model, represent, and manage multi-model data in a unified and abstract way. Its core idea exploits constructs provided by category theory, which is sufficiently general but still simple enough to cover any of the logical data models used in contemporary databases. Focusing on promising features and taking into account mature and verified principles, we overview the key parts of the framework and outline open questions and research directions that need to be further investigated. The ultimate objective is to pursue the idea of a self-tuning system that would permit us to collapse the traditionally understood conceptual and logical layers into just a single model allowing for unified handling of schemas, data instances, as well as queries.

Following the current trend, most of the well-known database systems, being relational, NoSQL, or NewSQL, denote themselves as multi-model. This industry-driven approach, however, lacks plenty of important features of the traditional DBMSs. The primary problem is a design of an optimal multi-model schema and its sufficiently general and efficient representation. In this paper, we provide an overview and discussion of the promising approaches that could potentially be capable of solving these issues, along with a summary of the remaining open problems.

In the recent years a new type of NoSQL databases, called graph databases (GDBs), has gained significant popularity due to the increasing need of processing and storing data in the form of a graph. The objective of this paper is a research on possibilities and limitations of GDBs and conducting an experimental comparison of selected GDB implementations. For this purpose the requirements of a universal GDB benchmark have been formulated and an extensible benchmarking tool, called BlueBench, has been developed.

Recently eXtensible Markup Language (XML) has achieved the leading role among languages for data representation and, thus, we can witness a massive boom of corresponding techniques for managing XML data. Most of the processing techniques, however, suffer from various bottlenecks worsening their time and/or space efficiency. We assume that the main reason is they consider XML collections too globally, involving all their possible features, although real-world data are often much simpler. Even though some techniques do restrict the input data, the restrictions are mostly unnatural. This paper aims to introduce Analyzer—a complex framework for performing statistical analyses of real-world documents. Exploitation of results of these analyses is a classical way how data processing can be optimized in many areas. Although this intent is legitimate, ad hoc and dedicated analyses soon become obsolete, they are usually built on insufficiently extensive collections and are difficult to repeat. Analyzer represents an easily extensible framework, which helps the user with gathering documents, managing analyses and browsing computed reports.