A framework for defining and analysing access policies in requirements models

Enforcing access policies derived from management control principles is a way by which organisations protect their information assets. The minimum privileges principle is an example of a management control principle, which specifies that users should only have access to resources they require to carry out their duties. Requirements models use actors to specify their access policies. Actors normally represent roles that users adopt, however a role can have different meanings, such as a position in an organisation or the assignment of a task, and can therefore be misleading. Current requirements modelling approaches do not provide a systematic way of defining roles for incorporation into access policies, and therefore we can not ensure that they satisfy management control principles. In this thesis we address the need to provide precise role definitions by developing a framework that facilitates the derivation of roles from the organisational context. The framework consists of a metamodel, which enables the organisational context to be represented and related to actors; a set of heuristics for deriving the organisational context; and a set of language constructs for formulating access policies, and verifying them using scenarios. We use the meta-model and language constructs that we developed to extend an existing requirements modelling language, the i* framework, and in particular a formal version of it, formal Tropos, to define and verify access policies definitions satisfying the minimum privileges principle. We also investigate the use of automated tool checking by translating the formal Tropos definitions into the specification language Alloy, which is supported by a tool that automatically checks assertions, to ensure consistency of the access policy definitions. We carry out a detailed case study taken from the literature to verify the extensions to the i* framework and the tool supported analysis. The framework presented in this thesis makes a novel contribution to the modelling of access policies as requirements, enabling us to define access policies using actors derived from the organisational context, that satisfy the minimum privileges principle

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Reasoning over Ontologies with Hidden Content: The Import-by-Query Approach

There is currently a growing interest in techniques for hiding parts of the signature of an ontology Kh that is being reused by another ontology Kv. Towards this goal, in this paper we propose the import-by-query framework, which makes the content of Kh accessible through a limited query interface. If Kv reuses the symbols from Kh in a certain restricted way, one can reason over Kv U Kh by accessing only Kv and the query interface. We map out the landscape of the import-by-query problem. In particular, we outline the limitations of our framework and prove that certain restrictions on the expressivity of Kh and the way in which Kv reuses symbols from Kh are strictly necessary to enable reasoning in our setting. We also identify cases in which reasoning is possible and we present suitable import-by-query reasoning algorithms

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes

In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10

Critical Analysis of Clinical Document Clustering Technique with Special Reference to Non-Matrix Factorization

Clinical records containing significant prescription and side effect data, a huge number of documents are normally analyzed. A critical piece of the data in those reports contains unstructured substance, whose examination by PC assessors is difficult to be performed. We proposed a joining system for isolating medication names and sign names from clinical notes by applying Nonnegative Matrix Factorization (NMF) and multi-see NMF to bundle clinical notes into vital gatherings reliant on test incorporate networks. Our exploratory outcomes demonstrate that multi-see NMF is a best technique for clinical record bunching. In addition, we find that utilizing extricated prescription/side effect names to group clinical archives beats simply utilizing words. Bunching calculations are regularly utilized for exploratory information examination. Vast measure of information investigated