International Journal of Data Mining, Modelling and Management

Zieliński, Bartosz; Maślanka, Paweł; Sobieski, Ścibor

doi: 10.1504/IJDMMM.2019.100384pmid: N/A

Allegories abstract and generalise (in the categorical framework) the algebra of binary relations. Arrows in an allegory enjoy a lot of properties and structure available for plain binary relations. At the same time, allegories are sufficiently general to allow the description within the same uniform framework of the lattice valued (e.g., fuzzy) relations and some more general structures. The paper presents a conceptual data modelling formalism which uses the language of allegories. We will provide examples demonstrating expressiveness of this formalism. While most of the examples are meant to be interpreted in the allegory of sets and binary relations, we also show the usefulness of using other allegories, such as the allegory of sets and lattice valued relations, with which one can model replicated data or data stored in a valid time temporal database.

Janga, Prudhvi; Davis, Karen C.

doi: 10.1504/IJDMMM.2019.100385pmid: N/A

The availability of vast amounts of heterogeneous XML web data motivates finding efficient methods to search, integrate, query, and present this data. The structure of XML documents is useful for achieving these tasks; however, not every XML document on the web includes a schema. We discuss challenges and solutions in the area of generation and integration of XML schemas. We propose and implement a framework for efficient schema extraction and integration from heterogeneous XML document collections collected from the web. Our approach introduces the schema extended context-free grammar (SECFG) to model XML schemas, including detection of attributes, data types, and element occurrences. Unlike other implementations, our approach supports the generation of XML schemas in any XML schema language, e.g., DTD or XSD. We compare our approach with other proposed approaches and conclude that we offer the same or better functionality more efficiently and with greater flexibility. The approach we propose is flexible enough to facilitate integration of and translation to tabular (relational) data.

Moremedi, Kobamelo; Poll, John Andrew Van Der

doi: 10.1504/IJDMMM.2019.100386pmid: N/A

Specification plays a pivotal role in software engineering to facilitate the development of highly dependable software. Various techniques for specification work have been developed to provide for precise and unambiguous specifications. Z is a formal specification language that is based on a strongly-typed fragment of Zermelo-Fraenkel set theory and first-order logic to provide for provably correct specifications. While diagrammatic specification languages may lack precision, they may, owing to their visual characteristics be a lucrative option for advocates of semi-formal specification techniques. In this research, we investigate the extent to which diagrammatic notations may capture the essence of, e.g., a Z specification. Several diagrammatic notations are considered and combined for this purpose. A case study is employed towards the end to evaluate the utility of the diagrammatic notation developed in this article. Comparisons on the merits of a diagrammatic notation are presented to further determine their feasibility.

Cuzzocrea, Alfredo; Grasso, Giorgio Mario; Nolich, Massimiliano

doi: 10.1504/IJDMMM.2019.100387pmid: N/A

Managing big data in distributed environments is a critical research challenge that has driven the attention from the community. In this context, there are several issues to be faced-off, including: 1) dealing with massive and heterogeneous data; 2) inconsistency problems; 3) query optimisation bottlenecks, and so forth. Clinical data represent a vibrant case of big data, due to both practical as well as methodological challenges exposed by such data. Following these considerations, in this paper we present an architecture for the storage, exchange and use of health data for administrative and epidemiological purposes, which focuses on the patient, who in a safe and easy way can make use of their data for therapeutic and research purposes. The proposed architecture would bring benefits both to patients, giving them the desired centrality in the care process, and to health administration, which could exploit the same infrastructure for better addressing health policies.