Multi-model data encompasses structurally distinct data, including relational, document, graph, key/value, columnar, etc., managed within a single system, such as a multi-model database or a data lake. Querying multi-model data requires strategies that balance unification and integration across diverse models and query languages. This paper presents DortDB, an extensible framework enabling cross-model queries combining well-known query languages and offering intuitive flexibility and optimization via a unified algebra. Though a small-scale in-memory prototype is to be demonstrated, its principles can be extended to distributed systems.

In this paper, we focus on the problem of evolution management of multi-model data. With the changing user requirements, the schema and the data need to be adapted to preserve the expected functionality of a multi-model application. We introduce a tool MM-evocat based on utilising the category theory. We show that the core of the tool, i.e., the categorical representation of multi-model data, enables us to grasp all the specifics of the individual models and their possible combinations. Its simple but powerful formal basis enables unique and robust support for evolution management.

Multi-model data is organised in various mutually interlinked formats and models, often with contradictory features. In addition, its structure may change over time, and its size can grow to the extremes of Big Data. In terms of research and practical processing, this creates one of the most complex challenges of effective data management.

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.

One of the most challenging issues in the era of Big Data is the Variety of the data. In general, there are two solutions to directly manage multi-model data currently: a single integrated multi-model database system or a tightly-integrated middleware over multiple single-model data stores. In this tutorial, we review and compare these two approaches giving insights on their advantages, trade-offs, and research opportunities. In particular, we dive into four key aspects of technology for both types of systems, namely (1) theoretical foundation of multi-model data management, (2) storage strategies for multi-model data, (3) query languages across models, and (4) query evaluation and its optimization. We provide a comparison of performance for the two approaches and discuss related open problems and remaining challenges. The slides of this tutorial can be found at http://udbms.cs.helsinki.fi/?tutorials/CIKM2018.

As more businesses realized that data, in all forms and sizes, is critical to making the best possible decisions, we see the continued growth of systems that support massive volume of non-relational or unstructured forms of data. Nothing shows the picture more starkly than the Gartner Magic quadrant for operational database management systems, which assumes that, by 2017, all leading operational DBMSs will offer multiple data models, relational and NoSQL, in a single DBMS platform. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach reduces significantly integration, migration, development, maintenance, and operational issues. Therefore, a challenging research work is how to develop efficient consolidated single data management platform covering both relational data and NoSQL to reduce integration issues, simplify operations, and eliminate migration issues. In this tutorial, we review the previous work on multi-model data management and provide the insights on the research challenges and directions for future work. The slides and more materials of this tutorial can be found at http://udbms.cs.helsinki.fi/?tutorials/edbt2017.

This paper introduces BDgen, a generator of Big Data targeting various types of users, implemented as a general and easily extensible framework. It is divided into a scalable backend designed to generate Big Data on clusters and a frontend for user-friendly definition of the structure of the required data, or its automatic inference from a sample data set. In the first release we have implemented generators of two commonly used formats (JSON and CSV) and the support for general grammars. We have also performed preliminary experimental comparisons confirming the advantages and competitiveness of the solution.

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.

XML has achieved the leading role among languages for data representation and, thus, the amount of related technologies and applications exploiting them grows fast. However, only a small percentage of applications is static and remains unchanged since its first deployment. Most of the applications change with newly coming user requirements and changing environment. In this paper we describe a framework and a methodology for management of evolution and change propagation throughout XML applications. We also introduce its proof-of-concept implementation called eXolutio, which has been developed and improved in our research group during last few years. We also provide an evaluation of the methodology in the domain of electronic health.

The most common applications of the today's IT world are information systems. The problems related to their design and implementation have sufficiently been solved. However, the true problems occur when an IS is already deployed and user requirements change. Currently this situation requires a skilled IT expert who knows all system components and, hence, is able to identify and modify all the affected parts. However, not always we have such an expert, whereas for complex systems it is a very hard and error-prone task. In this paper we introduce DaemonX -- an evolution management framework, which enables to manage evolution of complex applications efficiently and correctly. Using the idea of plug-ins, it enables to model almost any kind of a data format (currently XML, UML, ER, and BPMN). Since it preserves relationships among the modeled constructs, it naturally supports propagation of changes to all related affected parts. We describe the general proposal of the framework and, then, its architecture and implementation.

In this paper we introduce Strigil, a framework for automated data extraction. It represents an easily configurable tool that enables one to retrieve a data from textual or weak-structured documents. The paper contains description of the framework architecture and its important components. Additionally, we propose a scraping language inspired by the XSL transformations designed to extract data from different kinds of documents. Although there are many different approaches focused on various aspects of data scraping, they are usually very specialized to a concrete domain or a data source. We compare these solutions and discuss their advantages and disadvantages. Our scraping language is designed to work with an ontology to map scraped data directly to classes and attributes.

In this paper we introduce a method to infer a Schematron schema from a set of XML documents. We analyze different aspect of Schematron schema generation. Since the automatic inferring of XML documents is not a new problem, we will introduce only a single method that we will use in our experimental implementation. In the implementation we generate a grammar using the introduced inferring method and we allow the user to modify the grammar. The grammar is then transformed into Schematron schema by the use of our algorithm. Experimental results are a part of the paper.

In this paper we propose a technique for evaluating similarity of XML Schema fragments. Firstly, we define classes of structurally and semantically equivalent XSD constructs. Then we propose a similarity measure that is based on the idea of edit distance utilized to XSD constructs and enables one to involve various additional similarity aspects. In particular, we exploit the equivalence classes and semantic similarity of element/attribute names. Using experiments we show the behavior and advantages of the proposal.