Healthcare organisations collect detailed data on the care that they deliver. This data can be used to identify issues, including deviations from care standards and recommendations, and opportunities for improvement; it can be used also to support the development of new technologies and treatments. The volume and complexity of the data means that automated techniques such as process mining are needed to support the extraction and analysis of relevant information. This paper explains how the ontological information held in clinical terminologies can be used to facilitate process extraction and analysis, by connecting and aggregating clinical events through the classification of diagnoses made and treatments performed. The approach is demonstrated through application to data collected on care delivered to patients with cancer in a major hospital. The results are compared with those obtained from benchmark datasets using approaches in which connections and aggregations are proposed and curated by domain experts. This comparison highlights the potential, and the shortcomings, of ontology-based extraction and analysis in healthcare process mining. Supplementary Information: The online version contains supplementary material available at 10.1007/s10844-025-00942-8.

Scientific progress is increasingly dependent upon the acquisition, processing, and analysis of large volumes of data. The validity of results and the safety of applications rely upon an adequate understanding of the real-world semantics of this data: its intended interpretation, and the context in which it is acquired and processed. This presents a challenge: interpretations vary, context is infinite, and either may change over time. This paper addresses that challenge. It introduces a language for the description of real-world semantics that allows for multiple, evolving interpretations, together with a high degree of automation in the capture and creation of contextual metadata. The language itself has a mathematical semantics, and supports a notion of semantic interoperability closely related to existing, formal notions of refinement. The language represents a scalable approach in three respects: it is compositional, in terms of composing real-world semantics piece by piece; it allows for multiple perspectives, allowing the parallel development of different interpretations; and it supports automatic transformations to and from implementation languages. The practical application of the approach is illustrated with examples from large-scale medical research.

The value of integrated data relies upon common data points having an accessible, consistent interpretation; to achieve this at scale requires appropriate informatics support. This paper explains how a model-driven approach to software engineering and data management, in which software artefacts are generated automatically from data models, and models are used as metadata, can achieve this. It introduces a simple data modelling language, consistent with standard object modelling notations, together with a set of tools for model creation,maintenance, and deployment. It reports upon the application of this approach in the provision of informatics support for two large-scale clinical research initiatives.

Model-driven engineering is the generation of software artefacts from abstract models. This is achieved through transformations that encode domain knowledge and implementation strategies. The same transformations can be used to produce quite different systems, or to produce successive versions of the same system. A model-driven approach can thus reduce the cost of development. It can also reduce the cost of verification: if the transformations are shown or assumed to be correct, each new system or version can be verified in terms of its model, rather than its implementation. This paper introduces an approach to model-driven engineering that is particularly suited to the development of critical information systems. The language of the models, and the language of the transformations, are amenable to formal analysis. The transformation strategy, and the associated development methodology, are designed to preserve systems integrity and availability.

The CancerGrid approach to software support for clinical trials is based on two principles: careful curation of semantic metadata about clinical observations, to enable subsequent data integration, and model-driven generation of trial-specific software artefacts from a trial protocol, to streamline the software development process. This paper explains the approach, presents four varied case studies, and discusses the lessons learned.

This paper reports upon ten years of experience in the development and application of model-driven technology. The technology in question was inspired by work on formal methods: in particular, by the B toolkit. It was used in the development of a number of information systems, all of which were successfully deployed in real world situations. The paper reports upon three systems: one that informed the design of the technology, one that was used by an internal customer, and one that is currently in use outside the development organisation. It records a number of lessons regarding the application of model-driven techniques.

BACKGROUND: The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this data amongst researchers in different physical locations. RESULTS: We have developed an open source web-based application for remote scoring of TMA images, which exploits the value of Microsoft Silverlight Deep Zoom to provide a intuitive interface for zooming and panning around digital images. We use and extend existing XML-based standards to ensure that the data collected can be archived and that our system is interoperable with other standards-compliant systems. CONCLUSION: The application has been used for multi-centre scoring of TMA slides composed of tissues from several Phase III breast cancer trials and ten different studies participating in the International Breast Cancer Association Consortium (BCAC). The system has enabled researchers to simultaneously score large collections of TMA and export the standardised data to integrate with pathological and clinical outcome data, thereby facilitating biomarker discovery.

This paper is concerned with the creation of 'macros' in workflow visualization as a support tool to increase the efficiency of data curation tasks. We propose computation of candidate macros based on their usage in large collections of workflows in data repositories. We describe an efficient algorithm for extracting macro motifs from workflow graphs. We discovered that the state transition information, used to identify macro candidates, characterizes the structural pattern of the macro and can be harnessed as part of the visual design of the corresponding macro glyph. This facilitates partial automation and consistency in glyph design applicable to a large set of macro glyphs. We tested this approach against a repository of biological data holding some 9,670 workflows and found that the algorithmically generated candidate macros are in keeping with domain expert expectations.

Glyph-based visualization can offer elegant and concise presentation of multivariate information while enhancing speed and ease in visual search experienced by users. As with icon designs, glyphs are usually created based on the designers' experience and intuition, often in a spontaneous manner. Such a process does not scale well with the requirements of applications where a large number of concepts are to be encoded using glyphs. To alleviate such limitations, we propose a new systematic process for glyph design by exploring the parallel between the hierarchy of concept categorization and the ordering of discriminative capacity of visual channels. We examine the feasibility of this approach in an application where there is a pressing need for an efficient and effective means to visualize workflows of biological experiments. By processing thousands of workflow records in a public archive of biological experiments, we demonstrate that a cost-effective glyph design can be obtained by following a process of formulating a taxonomy with the aid of computation, identifying visual channels hierarchically, and defining application-specific abstraction and metaphors.

A presentation of an Artificial Intelligence (AI) called Visuo that stores and guesses quantitative visual-spatial magnitudes (e.g., sizes of objects). In this analysis, Visuo is used to store polar (angle and distance) relationships between objects in images. It uses a database of tagged images as its memory and approximates unexperienced magnitudes by analogy with semantically related concepts. This shows the transferring of information from high semantically related concepts yielding significantly higher accuracy in angle and distance estimations over using medium or low semantically similar items.

Abstract Economies and diseconomies of scale—gains and losses in productivity arising from increases in project size—are of considerable, practical importance in software engineering. However, there is no consensus as to whether, in general, economies or diseconomies exist with respect to project size; neither is there any consensus on the relationship among project size, team size, and overall productivity. Using evidence from a large database of projects, this paper derives a simple parametric model for estimating development effort in terms of size and productivity factors. The model is compared with popular estimation models—the Putnam model and COCOMO II—and a possible explanation is provided for the lack of consensus. By considering also the value of the software produced, the model provides a basis for the calculation of the optimal team size for a given task, with respect to the anticipated net benefit from the development activity. Copyright © 2011 John Wiley & Sons, Ltd.

This paper describes an approach to the formulation and classification of security requirements in eScience. It explains why it is untenable to suggest that `one size fits all', and that what is an appropriate security solution in one context may not be at all appropriate in another. It proposes a framework for the description of eScience security in a number of different dimensions, in terms of measures taken and controls achieved. A distinctive feature of the framework is that these descriptions are organised into a set of discrete criteria, in most cases presented as levels of increasing assurance. The intended framework should serve as a basis for the systematic analysis of security solutions, facilitating the processes of design and approval, as well as for the identification of expectations and best practice in particular domains. The possible usage of the framework, and the value of the approach, is demonstrated in the paper through application to the design of a national data sharing service.

The potential impact of model-driven engineering is considerable, and far extends beyond the initial development and delivery of a system. The automatic generation of systems from abstract models also facilitates evolution: the definitive description of structure and behaviour remains accessible, and changes are more easily planned and accommodated. Within the domain of information systems, this is useful only if the data held in the system can be migrated easily to the next version. This paper shows how migration can be facilitated by means of a metamodelling approach, in which a separate model of changes is used as a basis for the automatic generation of semantics-preserving, platform-specific data transformations.

Many information systems hold data of considerable value, and are subject to complex constraints and business rules. In these systems, update operations are often carefully described, to the extent that it is possible to derive a formal specification of their applicability and effect. Where an update is performed by means of a workflow, as a combination of operations, then it may be advantageous to calculate properties of that workflow from the specifications of the operations involved. This paper introduces a formal notation for the description and analysis of workflows, similar to the well-known languages of guarded commands or generalised substitutions. This notation is given a behavioural semantics corresponding to a sequential, transformational view of the information system. This semantics can then be used to determine the suitability of proposed guards-constraints upon the enactment of workflows-and to establish whether one workflow will interfere with the progress of another.

The CancerGrid consortium is developing open- standards cancer informatics to address the challenges posed by modern cancer clinical trials. This paper presents the service-oriented software paradigm implemented in CancerGrid to derive clinical trial information management systems for collaborative cancer research across multiple institutions. Our proposal is founded on a combination of a clinical trial (meta)model and WSRF (Web Services Resource Framework), and is currently being evaluated for use in early phase trials. Although primarily targeted at cancer research, our approach is readily applicable to other areas for which a similar information model is available.

Cancer clinical trials pose significant challenges to the e-Science community. The information technology required to enable this kind of large-scale, collaborative science will need to support easy and rapid development and deployment of reliable and flexible software systems that enable syntactic, semantic and computational interoperability. CancerGrid, an e-Science consortium funded by the UK Medical Research Council, is addressing these challenges through the development of model-driven, service-oriented technology for cancer informatics. This poster presents recent significant efforts in CancerGrid, resulting in the metamodel-based automated generation of WSRF (Web Services Resource Framework) compliant trial management systems. The most important advantages of our approach are discussed.

It is a common phenomenon for research projects to collect and analyse valuable data using ad-hoc information systems. These costly-to-build systems are often composed of incompatible variants of the same modules, and record data in ways that prevent any meaningful result analysis across similar projects. We present a framework that uses a combination of formal methods, model-driven development and service-oriented architecture (SOA) technologies to automate the generation of data management systems for cancer clinical trial research, an area particularly affected by these problems. The SOA solution generated by the framework is based on an information model of a cancer clinical trial, and comprises components for both the collection and analysis of cancer research data, within and across clinical trial boundaries. While primarily targeted at cancer research, our approach is readily applicable to other areas for which a similar information model is available.

Distributed hash tables (DHTs) can be used as the basis of a resilient lookup service in unstable environments: local routing tables are updated to reflected changes in the network; efficient routing can be maintained in the face of participant node failures. This fault-tolerance is an important aspect of modern, decentralised data storage solutions. In architectures that employ DHTs, the choice of algorithm for data replication and maintenance can have a significant impact upon performance and reliability. This paper presents a comparative analysis of replication algorithms for architectures based upon a specific design of DHT. It presents also a novel maintenance algorithm for dynamic replica placement, and considers the reliability of the resulting designs at the system level. The performance of the algorithms is examined using simulation techniques; significant differences are identified in terms of communication costs and latency.

We show that visio‐spatial representations and reasoning can be used as a similarity metric for case‐based protein structure prediction. Our system retrieves pairs of α‐helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict a similar three‐dimensional protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case‐based reasoning.

Abstract This paper defines a new denotational semantics for the language of Communicating Sequential Processes (CSP). The semantics lies between the existing traces and failures models of CSP, providing a treatment of non-determinism in terms of singleton failures . Although the semantics does not represent a congruence upon the full language, it is adequate for sequential tests of non-deterministic processes. This semantics corresponds exactly to a commonly used notion of data refinement in Z and Object-Z: an abstract data type is refined when the corresponding process is refined in terms of singleton failures. The semantics is used to explore the relationship between data refinement and process refinement, and to derive a rule for data refinement that is both sound and complete.

Many approaches to software specification and design make use of invariants: statements whose truth is preserved under various operations upon a system or component. Approaches that involve the construction of object-oriented or entity-relationship models require the expression of a particular kind of global invariant, concerning associations between objects or entities. This paper shows how association invariants can be expressed in a new, object-based formal language. It then explains how these expressions can be used to determine pre- and post-conditions for local operations, sufficient to ensure that the invariants are maintained. These conditions - and the program text to implement them - can be generated automatically. This makes it easier to produce correct implementations of an object-oriented design.

This paper describes existing practices in the supervision and assessment of projects undertaken by part-time, postgraduate students in Software Engineering. It considers this aspect of the learning experience, and the educational issues raised, in the context of existing literature-much of which is focussed upon the experience of full-time, undergraduate students. The importance of these issues will increase with the popularity of part-time study at a postgraduate level; the paper presents a set of guidelines for project supervision and assessment.

Abstract This paper defines a formal semantics for a subset of the Unified Modeling Language (UML). It shows how suitable combinations of class, object, state, and sequence diagrams can be associated with patterns of interaction, expressed in the event notation of Communicating Sequential Processes (CSP). The diagram semantics is then extended to give a meaning to complete models – suitable combinations of diagrams – and thus a concurrency semantics for object models written in UML. This model semantics is in turn used to define a theory of refinement, based upon existing notions of data and process refinement.

Abstract The theory of timed Communicating Sequential Processes is a mathematical approach to the design and analysis of timed distributed systems. This paper extends the language of timed CSP to include a general treatment of recursion. A semantics for mutual recursion is introduced, together with a sufficient condition for the necessary fixpoint to be unique. The resulting language has the familiar unwinding property of process algebra, and exhibits a number of useful algebraic identities. A theory of recursion induction is formulated, and a simple example is presented to illustrate its use.