Internet of Things (IoT) technology and Smart Home (SH) solutions are a growing area of research and development, particularly within health and social care where they have potential to offer information and support for self-management and independent living. However, successful design and deployment of these technologies are predicated on a clear understanding of user aspirations, needs, and requirements. This paper presents findings from seven one-to-one interviews with care staff who contributed their experiences representing a diverse range of job roles and categories of service-users supported. Our participants were from a regional supported-living provider with facilities for people with learning disabilities. The interviews provided insight into care service provision and service-users' needs, as well as helping to identify areas of the current service that can be supported by IoT based intelligent systems. The feedback that staff provided was based on their knowledge and understanding of the needs of specific service user groups and included information regarding the appropriateness and applicability of the technologies, as well as the appeal, acceptability and usability of these solutions.

Abstract This paper investigates the utility of unsupervised machine learning and data visualisation for tracking changes in user activity over time. This is done through analysing unlabelled data generated from passive and ambient smart home sensors, such as motion sensors, which are considered less intrusive than video cameras or wearables. The challenge in using unlabelled passive and ambient sensors data for activity recognition is to find practical methods that can provide meaningful information to support timely interventions based on changing user needs, without the overhead of having to label the data over long periods of time. The paper addresses this challenge to discover patterns in unlabelled sensor data using kernel density estimation (KDE) for pre-processing the data, together with t-distributed stochastic neighbour embedding and uniform manifold approximation and projection for visualising changes. The methodology is developed and tested on the Aruba CASAS smart home dataset and focusses on discovering and tracking changes in kitchen-based activities. The traditional approach of using sliding windows to segment the data requires a priori knowledge of the temporal characteristics of activities being identified. In this paper, we show how an adaptive approach for segmentation, KDE, is a suitable alternative for identifying temporal clusters of sensor events from unlabelled data that can represent an activity. The ability to visualise different recurring patterns of activity and changes to these over time is illustrated by mapping the data for separate days of the week. The paper then demonstrates how this can be used to track patterns over longer time-frames which could be used to help highlight differences in the user’s day-to-day behaviour. By presenting the data in a format that can be visually reviewed for temporal changes in activity over varying periods of time from unlabelled sensor data, opens up the opportunity for carers to then initiate further enquiry if variations to previous patterns are noted. This is seen as an accessible first step to enable carers to initiate informed discussions with the service user to understand what may be causing these changes and suggest appropriate interventions if the change is found to be detrimental to their well-being.

Business systems these days need to be agile to address the needs of a changing world. Business modelling requires process management to be highly adaptable with the ability to support dynamic workflows, inter-application integration (potentially between businesses) and process reconfiguration. Designing in the ability to cater for evolution is critical to success. To handle change, systems need the capability to adapt as and when necessary to changes in users' requirements. Using our implementation of a self-describing system, a so-called description-driven approach, new versions of data structures or processes can be created alongside older versions providing a log of changes to the underlying data schema and enabling the gathering of traceable ("provenance") data. The CRISTAL software, which originated at CERN for handling physics data, uses versions of stored descriptions to define data and workflows which can be evolved over time and thereby to handle evolving system needs. It has been customised for use in business as the Agilium-NG product. This paper reports on how the Agilium-NG software has enabled the deployment of an unique business process management solution that can be dynamically evolved to cater for changing user requirements.

The significance of provenance in various settings has emphasised its potential in the policy-making process for analytics in Smart Cities. At present, there exists no framework that can capture the provenance in a policy-making setting. This research therefore aims at defining a novel framework, namely, the Policy Cycle Provenance (PCP) Framework, to capture the provenance of the policymaking process. However, it is not straightforward to design the provenance framework due to a number of associated policy design challenges. The design challenges revealed the need for an adaptive system for tracking policies therefore a model-driven approach has been considered in designing the PCP framework. Also, suitability of a networking approach is proposed for designing workflows for tracking the policy-making process.

A policy-making process entails large sources of data for its creation, the tracking of which can provide a significant insight into a process and data that was being employed for its creation. However, a process employed for creating policies varies with each policy case due to different nature and requirements of policies. Therefore, a flexible approach is required to capture process details of different policies. Thus a novel autonomous provenance capturing technique has been introduced that is inspired from IP packet switching networking concepts. This novel technique is used with a model-driven approach to enable flexibility in a system. It is expected that this new technique will be able to systematically capture provenance in a dynamic sociotechnical environments.

Providing an appropriate level of accessibility to and tracking of data and process elements in large volumes of medical data is essential. Researchers require systems that provide traceability of information through provenance data capture and management to support clinical analyses. A Virtual Research Environment (VRE) in which data sets and workflows are described, captured, managed and instantiated across biomedical applications would provide an ideal platform for supporting collaborative analyses acting as a knowledge repository for research. We propose a solution that aims to provide detailed data and process traceability to support research analysis that is generically applicable across medical systems.

One essential requirement for supporting analytics for Big Medical Data systems is the provision of a suitable level of traceability to data or processes ('Items') in large volumes of data. Systems should be designed from the outset to support usage of such Items across the spectrum of medical use and over time in order to promote traceability, to simplify maintenance and to assist analytics. The philosophy proposed in this paper is to design medical data systems using a 'description-driven' approach in which meta-data and the description of medical items are saved alongside the data, simplifying item re-use over time and thereby enabling the traceability of these items over time and their use in analytics. Details are given of a big data system in neuroimaging to demonstrate aspects of provenance data capture, collaborative analysis and longitudinal information traceability. Evidence is presented that the description-driven approach leads to simplicity of design and ease of maintenance following the adoption of a unified approach to Item management.

Providing an appropriate level of accessibility to and tracking of data or process elements in large volumes of medical data, is an essential requirement in the Big Data era. Researchers require systems that provide traceability of information through provenance data capture and management to support their clinical analyses. We present an approach that has been adopted in the neuGRID and N4U projects, which aimed to provide detailed traceability to support research analysis processes in the study of biomarkers for Alzheimer's disease, but is generically applicable across medical systems. To facilitate the orchestration of complex, large-scale analyses in these projects we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of data and workflow usage over time in neuGRID and N4U.

Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. This provenance of scientific workflows has been effectively carried out in Grid based scientific workflow systems. However, recent adoption of Cloud-based scientific workflows present an opportunity to investigate the suitability of existing approaches or propose new approaches to collect provenance information from the Cloud and to utilize it for workflow repeatability in the Cloud infrastructure. This paper presents a novel approach that can assist in mitigating this challenge. This approach can collect Cloud infrastructure information from an outside Cloud client along with workflow provenance and can establish a mapping between them. This mapping is later used to re-provision resources on the Cloud for workflow execution. The reproducibility of the workflow execution is performed by: (a) capturing the Cloud infrastructure information (virtual machine configuration) along with the workflow provenance, (b) re-provisioning the similar resources on the Cloud and re-executing the workflow on them and (c) by comparing the outputs of workflows. The evaluation of the prototype suggests that the proposed approach is feasible and can be investigated further. Moreover, there is no reference reproducibility model exists in literature that can provide guidelines to achieve this goal in Cloud. This paper also attempts to present a model that is used in the proposed design to achieve workflow reproducibility in the Cloud environment.

With the recent increase in data volumes and algorithm complexity, clinical researchers are increasingly facing problems associated with information tracking, analysis reproducibility and the verification of scientific outputs. Systems are required that provide provenance data capture and management as support for analyses. This paper presents a provenance enabled system that is used in the N4U project, which aims to provide detailed trace ability to support research analysis processes in the study of biomarkers for Alzheimer's disease. To facilitate the orchestration of complex, large-scale analyses in this project we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track the evolution of both data and workflows in N4U.

In the age of the Cloud and so-called 'big data' systems must be increasingly flexible, reconfigurable and adaptable to change in addition to being developed rapidly.As a consequence, designing systems to cater for evolution is becoming critical to their success.To be able to cope with change, systems must have the capability of reuse and the ability to adapt as and when necessary to changes in requirements.Allowing systems to be self-describing is one way to facilitate this.To address the issues of reuse in designing evolvable systems, this paper proposes a so-called description-driven approach to systems design.This approach enables new versions of data structures and processes to be created alongside the old, thereby providing a history of changes to the underlying data models and enabling the capture of provenance data.The efficacy of the description-driven approach is exemplified by the CRISTAL project.CRISTAL is based on description-driven design principles; it uses versions of stored descriptions to define various versions of data which can be stored in diverse forms.This paper discusses the need for capturing holistic system description when modelling large-scale distributed systems.

In recent years, malware has grown extremely rapidly in complexity and rates of system infection. Current generation anti-virus and anti-malware software provides system protection through the use of locally installed monitoring agents, which are dependent upon vendor generated signature and heuristic based rules. However, because these monitoring agents are installed within the systems they are trying to protect, they themselves are potential targets of attack by malware. Pathogen overcomes this issue by using a real-time system monitoring and analysis framework that utilises Virtual Machine introspection (VMI) to allow the monitoring of a system without the need for any locally installed agents. One of the main research problems in VMI is how to parse and interpret the memory of an executing system from outside of that system. Pathogen's contribution is a lightweight introspection framework that bridges the semantic gap.

Scientific workflows have become the primary mechanism for conducting analyses on distributed computing infrastructures such as grids and clouds. In recent years, the focus of optimization within scientific workflows has primarily been on computational tasks and workflow makespan. However, as workflow-based analysis becomes ever more data intensive, data optimization is becoming a prime concern. Moreover, scientific workflows can scale along several dimensions: (i) number of computational tasks, (ii) heterogeneity of computational resources, and the (iii) size and type (static versus streamed) of data involved. Adapting workflow structure in response to these scalability challenges remains an important research objective. Understanding how a workflow graph can be restructured in an automated manner (through task merge, for instance), to address constraints of a particular execution environment is explored in this work, using a multi-objective evolutionary approach. Our approach attempts to adapt the workflow structure to achieve both compute and data optimization. The question of when to terminate the evolutionary search in order to conserve computations is tackled with a novel termination criterion. The results presented in this article demonstrate the feasibility of the termination criterion and demonstrate that significant optimization can be achieved with a multi-objective approach.

Large-scale neuroscience research projects are necessary in order to make significant progress in the study of degenerative brain diseases. At present the effectiveness of such efforts is being somewhat restricted by the absence of specifically tailored computing infrastructures. The neuGRID project aims to address this through the provision of a high-level service oriented infrastructure that enables complex neuro-science research. One of the principle aims of this work is to develop portable services that can be re-used in a larger set of related medical applications to access distributed computing resources. These services will provide high-level functionality that will support workflow authoring and planning, provenance storage and retrieval, querying against heterogeneous data sources as well as security and data anonymization amongst others. This paper introduces the neuGRID service architecture and outlines the design of two specific services, namely the Pipeline Service and the Glueing Service. A proof of concept implementation to evaluate the neuGRID design approach has been developed.

In recent years, the tremendous increase in the use of medical knowledge-discovery and decision-support applications has often required clinical researchers to write complex database queries. The users of these data analysis systems are normally unaware of the semantic relationships between the concepts stored in a database. In order to provide automated query formulation services, some mechanism for generating queries is required. In this regard, as reported in [1], domain ontologies can be used to formulate relational database queries in order to simplify the data access of the underlying data sources. However, the provision of such a query generation facility requires managing complex mappings between domain ontologies and relational data sources. In this regard, this paper discusses our approach to define mappings between domain ontologies and database schemas to support the ontology assisted relational query formulation process. This approach has been applied to the integrated medical database schema of the EU funded Health-e-Child (HeC) project.

The integration of heterogeneous biomedical information is one important step towards providing the level of personalization required in the next generation of healthcare provision. In order to provide the computer-based decision support systems needed to access this integrated healthcare information it will be necessary to handle the semantics of (amongst other things) medical protocols. The EC FP6 Health-e-Child project aims to develop an integrated healthcare platform for European paediatrics and decision support tools to access personalized health information. This paper introduces both the integrated data model in the Health-e-Child project and through a case study using the brain tumour protocols it demonstrates the semantic annotation of patient data acquired in the project using UMLS as the primary source of semantic data.

End users of recent biomedical information systems are often unaware of the storage structure and access mechanisms of the underlying data sources and can require simplified mechanisms for writing domain specific complex queries. This research aims to assist users and their applications in formulating queries without requiring complete knowledge of the information structure of the underlying data sources. To achieve this, query reformulation techniques and algorithms have been developed that can interpret ontology-based search criteria and associated domain knowledge in order to reformulate a relational query. These query reformulation algorithms exploit the semantic relationships and assertion capabilities of OWL-DL based domain ontologies. In this paper, this approach is applied to the integrated database schema of the EU funded Health-e-Child (HeC) project with the aim of providing ontology assisted query reformulation techniques to simplify the global access that is needed to millions of medical records across the UK and Europe.

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patient's medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence- based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health- e-Child project.

This paper presents an overview of the MammoGrid project and some of its achievements. In terms of the global grid project, and European research in particular, the project has successfully demonstrated the capacity of a grid-based system to support effective collaboration between physicians, including handling and querying image databases, as well as using grid services, such as image standardization and computer-aided detection (CADe) of suspect or indicative features. In terms of scientific results, in radiology, there have been significant epidemiological findings in the assessment of breast density as a risk factor, but the results for CADe are less clear-cut. Finally, the foundations of a technology transfer process to establish a working "MammoGrid plus" system in Spain through the company Maat GKnowledge and the collaboration of CIEMAT and hospitals in Extremadura

The use of meta-schedulers for resource management in large-scale distributed systems often leads to a hierarchy of schedulers. In this paper, we discuss why existing meta-scheduling hierarchies are sometimes not sufficient for Grid systems due to their inability to re-organise jobs already scheduled locally. Such a job re-organisation is required to adapt to evolving loads which are common in heavily used Grid infrastructures. We propose a peer-topeer scheduling model and evaluate it using case studies and mathematical modelling. We detail the DIANA (Data Intensive and Network Aware) scheduling algorithm and its queue management system for coping with the load distribution and for supporting bulk job scheduling. We demonstrate that such a system is beneficial for dynamic, distributed and self-organizing resource management and can assist in optimizing load or job distribution in complex Grid infrastructures.

E-learning can be loosely defined as a wide set of applications and processes, which uses available electronic media (and tools) to deliver vocational education and training. With its increasing recognition as an ubiquitous mode of instruction and interaction in the academic as well as corporate world, the need for a scaleable and realistic model is becoming important. In this paper, we introduce SELF; a semantic grid-based e-learning framework. SELF aims to identify the key-enablers in a practical grid-based e-learning environment and to minimize technological reworking by proposing a well-defined interaction plan among currently available tools and technologies. We define a dichotomy with e-learning specific application layers on top and semantic grid-based support layers underneath. We also map the latest open and freeware technologies with various components in SELF.

High energy physics (HEP) and other scientific communities have adopted service oriented architectures (SOA) as part of a larger grid computing effort. This effort involves the integration of many legacy applications and programming libraries into a SOA framework. The grid analysis environment (GAE) (Lingen et al., 2004) is such a service oriented architecture based on the Clarens grid services framework (Steenberg et al., 2004) and is being developed as part of the compact muon solenoid (CMS) experiment at the large hadron collider (LHC) at European Laboratory for Particle Physics (CERN). Clarens provides a set of authorization, access control, and discovery services, as well as XMLRPC and SOAP access to all deployed services. Two implementations of the Clarens Web services framework (Python and Java) offer integration possibilities for a wide range of programming languages. This paper describes the Java implementation of the Clarens Web services framework called 'JClarens' and several Web services of interest to the scientific and grid community that have been deployed using JClarens.

This work presents a perspective on the Healthgrid initiative which involves European projects deploying pioneering applications of Grid technology in the health sector. In the last couple of years, several Grid projects have been funded on health related issues at national and European levels. A crucial issue is to maximize their cross fertilization in the context of an environment where data of medical interest can be stored and made easily available to the different actors in health care, physicians, health care centres and administrations, and of course the citizens. The Healthgrid initiative, represented by the Healthgrid association, was initiated to bring the necessary long term continuity, to reinforce and promote awareness of the possibilities and advantages linked to the deployment of Grid technologies in health. Technologies to address the specific requirements for medical applications are under development. Results from the DataGrid and other projects are given as examples of early applications.

In building models for manufacturing, product information has most often been handled separately from process information. The integration of product and process models in a unified data model could provide the means by which information could be shared across a manufacturing enterprise throughout the system lifecycle from design to production. Recently, attempts have been made to integrate these two separate views of systems through identifying common data models. This paper relates description-driven systems to multi-layer architectures and reveals where existing design patterns facilitate the integration of product and process models and where patterns are missing or where existing patterns require enrichment for this integration. It reports on the construction of a so-called description-driven system which integrates product data management (PDM) and workflow management (WfM) data models through a common meta-model.

As enterprises, data and functions become increasingly complex and distributed the need for information systems to be both customisable and interoperable also increases. Large scale engineering and scientific projects demand flexibility in order to evolve over time and to interact with external systems (both newly designed and legacy in nature) while retaining a degree of conceptual simplicity. The design of such systems is heavily dependent on the flexibility and accessibility of the data model describing the enterprise's repository. The model must provide interoperability and reusability so that a range of applications can access the enterprise data. Making the repository self-describing, based on 'meta-object' structures, ensures that knowledge about the repository structure is available for applications to interrogate and to navigate around for the extraction of application-specific data. In this paper, a large application is described which uses a meta-object based repository to capture engineering data in a large data warehouse. It shows that adopting a meta-modeling approach to repository design provides support for interoperability and a sufficiently flexible environment in which system evolution and reusability can be handled.

At a time when many companies are embracing business process re-engineering and are under pressure to reduce "time-to-market", the management of product information from creative design through to manufacture has become increasingly important. Traditionally, design engineers have employed product data management systems to coordinate and control access to documented versions of product designs. However, these systems provide control only at the collaborative design level and are seldom used beyond design. Workflow management systems, on the other hand, are employed to coordinate and support the more complex and repeatable work processes of the production environment. Most commercial workflow products cannot support the highly dynamic activities found both in the design stages of product development and in rapidly evolving workflow definitions. The integration of product data management with workflow management could provide support for product development from initial CAD/CAM collaborative design through to the support and optimisation of production workflow activities. This paper investigates such an integration and proposes a philosophy for the support of product data throughout the full development and production lifecycle.

The Compact Muon Solenoid (CMS) high energy physics experiment will comprise several large high resolution detectors each of which will be constructed out of over a million precision parts and will be produced and assembled during the next decade by specialised centres distributed world-wide. Each constituent part of each detector must be accurately measured and tested locally prior to its ultimate assembly and integration in the experimental area at CERN. The CRISTAL (Concurrent Repositories and Information System for Tracking Assembly and production Lifecycles) system is a prototype being developed to monitor and control the production and assembly process of 110000 lead tungstate (PbWO/sub 4/) mono-crystals, and their associated fast electronics, to be installed in the CMS Electromagnetic Calorimeter (ECAL). The software will be generic in design and hence reusable for other CMS detector groups. The paper discusses the distributed computing problems and design issues posed by this project.

The CERN Research and Development project (RD-38), named CICERO, aims to identify and design the main building blocks of a generic control information system based on distributed objects. The project is producing an integrating framework (named Cortex) into which user real-time control objects will ultimately be plugged (and played) and a control information system to support its configuration and management. Development of Cortex is following the ESA PSS-05-02 software engineering standards. Cortex is providing an environment which allows real-time control systems to share information, control and analysis functions which presents a uniform human interface; which permits upgrades and additions without code modification; and which is sufficiently generic to allow its use both by existing or future control systems at CERN and by industrial real time control systems. It provides both high level data access, abstracting objects to a level appropriate for on-line control and low level data access to allow views of experimental sub-components for detailed real-time control. Additionally, the Cortex system shall enable developers to grow their control systems from a lab-based test system to the complete experimental system and is therefore both scaleable and flexible to change. Technical solutions are being identified in CICERO which could later be the major components of a basic turnkey control system for future medium to large scale HEP experiments and accelerators as well as for industrial control systems. This paper outlines the modelling concepts behind Cortex.