Using Unified Personal Information in Workspaces

Knowledge workers (KWers) deal with personal information and use tools like, e.g., desktop workspaces to support their work. But KWer support is hindered by personal information fragmentation, i.e., applications keep a set of personal information while not interconnecting it. This thesis addresses this in the domains personal task management and meeting management by using a common unified personal information model as offered by the semantic desktop personal information management (PIM) system

Patterns for Providing Real-Time Guarantees in DOC Middleware - Doctoral Dissertation, May 2002

The advent of open and widely adopted standards such as Common Object Request Broker Architecture (CORBA) [47] has simplified and standardized the development of distributed applications. For applications with real-time constraints, including avionics, manufacturing, and defense systems, these standards are evolving to include Quality-of-Service (QoS) specifications. Operating systems such as Real-time Linux [60] have responded with interfaces and algorithms to guarantee real-time response; similarly, languages such as Real-time Java [59] include mechanisms for specifying real-time properties for threads. However, the middleware upon which large distributed applications are based has not yet addressed end-to-end guarantees of QoS specifications. Unless this challenge can be met, developers must resort to ad hoc solutions that may not scale or migrate well among different platforms. This thesis provides two contributions to the study of real-time Distributed Object Computing (DOC) middleware. First, it identifies potential bottlenecks and problems with respect to guaranteeing real-time performance in contemporary middleware. Experimental results illustrate how these problems lead to incorrect real-time behavior in contemporary middleware platforms. Second, this thesis presents designs and techniques for providing real-time QoS guarantees in DOC middleware in the context of TAO [6], an open-source and widely adopted implementation of real-time CORBA. Architectural solutions presented here are coupled with empirical evaluations of end-to-end real-time behavior. Analysis of the problems, forces, solutions, and consequences are presented in terms of patterns and frame-works, so that solutions obtained for TAO can be appropriately applied to other real-time systems

Semantic lifting and reasoning on the personalised activity big data repository for healthcare research

The fast growing markets of smart health monitoring devices and mobile applications provide opportunities for common citizens to have capability for understanding and managing their own health situations. However, there are many challenges for data engineering and knowledge discovery research to enable efficient extraction of knowledge from data that is collected from heterogonous devices and applications with big volumes and velocity. This paper presents research that initially started with the EC MyHealthAvatar project and is under continual improvement following the project’s completion. The major contribution of the work is a comprehensive big data and semantic knowledge discovery framework which integrates data from varied data resources. The framework applies hybrid database architecture of NoSQL and RDF repositories with introductions for semantic oriented data mining and knowledge lifting algorithms. The activity stream data is collected through Kafka’s big data processing component. The motivation of the research is to enhance the knowledge management, discovery capabilities and efficiency to support further accurate health risk analysis and lifestyle summarisation

Flexible Scheduling in Middleware for Distributed rate-based real-time applications - Doctoral Dissertation, May 2002

Distributed rate-based real-time systems, such as process control and avionics mission computing systems, have traditionally been scheduled statically. Static scheduling provides assurance of schedulability prior to run-time overhead. However, static scheduling is brittle in the face of unanticipated overload, and treats invocation-to-invocation variations in resource requirements inflexibly. As a consequence, processing resources are often under-utilized in the average case, and the resulting systems are hard to adapt to meet new real-time processing requirements. Dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling offers relief from the limitations of static scheduling. However, dynamic scheduling often has a high run-time cost because certain decisions are enforced on-line. Furthermore, under conditions of overload tasks can be scheduled dynamically that may never be dispatched, or that upon dispatch would miss their deadlines. We review the implications of these factors on rate-based distributed systems, and posits the necessity to combine static and dynamic approaches to exploit the strengths and compensate for the weakness of either approach in isolation. We present a general hybrid approach to real-time scheduling and dispatching in middleware, that can employ both static and dynamic components. This approach provides (1) feasibility assurance for the most critical tasks, (2) the ability to extend this assurance incrementally to operations in successively lower criticality equivalence classes, (3) the ability to trade off bounds on feasible utilization and dispatching over-head in cases where, for example, execution jitter is a factor or rates are not harmonically related, and (4) overall flexibility to make more optimal use of scarce computing resources and to enforce a wider range of application-specified execution requirements. This approach also meets additional constraints of an increasingly important class of rate-based systems, those with requirements for robust management of real-time performance in the face of rapidly and widely changing operating conditions. To support these requirements, we present a middleware framework that implements the hybrid scheduling and dispatching approach described above, and also provides support for (1) adaptive re-scheduling of operations at run-time and (2) reflective alternation among several scheduling strategies to improve real-time performance in the face of changing operating conditions. Adaptive re-scheduling must be performed whenever operating conditions exceed the ability of the scheduling and dispatching infrastructure to meet the critical real-time requirements of the system under the currently specified rates and execution times of operations. Adaptive re-scheduling relies on the ability to change the rates of execution of at least some operations, and may occur under the control of a higher-level middleware resource manager. Different rates of execution may be specified under different operating conditions, and the number of such possible combinations may be arbitrarily large. Furthermore, adaptive rescheduling may in turn require notification of rate-sensitive application components. It is therefore desirable to handle variations in operating conditions entirely within the scheduling and dispatching infrastructure when possible. A rate-based distributed real-time application, or a higher-level resource manager, could thus fall back on adaptive re-scheduling only when it cannot achieve acceptable real-time performance through self-adaptation. Reflective alternation among scheduling heuristics offers a way to tune real-time performance internally, and we offer foundational support for this approach. In particular, run-time observable information such as that provided by our metrics-feedback framework makes it possible to detect that a given current scheduling heuristic is underperforming the level of service another could provide. Furthermore we present empirical results for our framework in a realistic avionics mission computing environment. This forms the basis for guided adaption. This dissertation makes five contributions in support of flexible and adaptive scheduling and dispatching in middleware. First, we provide a middle scheduling framework that supports arbitrary and fine-grained composition of static/dynamic scheduling, to assure critical timeliness constraints while improving noncritical performance under a range of conditions. Second, we provide a flexible dispatching infrastructure framework composed of fine-grained primitives, and describe how appropriate configurations can be generated automatically based on the output of the scheduling framework. Third, we describe algorithms to reduce the overhead and duration of adaptive rescheduling, based on sorting for rate selection and priority assignment. Fourth, we provide timely and efficient performance information through an optimized metrics-feedback framework, to support higher-level reflection and adaptation decisions. Fifth, we present the results of empirical studies to quantify and evaluate the performance of alternative canonical scheduling heuristics, across a range of load and load jitter conditions. These studies were conducted within an avionics mission computing applications framework running on realistic middleware and embedded hardware. The results obtained from these studies (1) demonstrate the potential benefits of reflective alternation among distinct scheduling heuristics at run-time, and (2) suggest performance factors of interest for future work on adaptive control policies and mechanisms using this framework

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

Background: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. Results: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. Conclusions: This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. Availability: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql

Towards Middleware for Fault-tolerance in Distributed Real-time and Embedded Systems

Abstract. Distributed real-time and embedded (DRE) systems often require support for multiple simultaneous quality of service (QoS) properties, such as real-timeliness and fault tolerance, that operate within resource constrained environments. These resource constraints motivate the need for a lightweight middleware infrastructure, while the need for simultaneous QoS properties require the middleware to provide fault tolerance capabilities that respect time-critical needs of DRE systems. Conventional middleware solutions, such as Fault-tolerant CORBA (FT-CORBA) and Continuous Availability API for J2EE, have limited utility for DRE systems because they are heavyweight (e.g., the complexity of their feature-rich fault tolerance capabilities consumes excessive runtime resources), yet incomplete (e.g., they lack mechanisms that enable fault tolerance while maintaining real-time predictability). This paper provides three contributions to the development and standardization of lightweight real-time and fault-tolerant middleware for DRE systems. First, we discuss the challenges in realizing real-time faulttolerant solutions for DRE systems using contemporary middleware. Second, we describe recent progress towards standardizing a CORBA lightweight fault-tolerance specification for DRE systems. Third, we present the architecture of FLARe, which is a prototype based on the OMG real-time fault-tolerant CORBA middleware standardization efforts that is lightweight (e.g., leverages only those server-and client-side mechanisms required for real-time systems) and predictable (e.g., provides fault-tolerant mechanisms that respect time-critical performance needs of DRE systems)

Next generation, secure cloud-based pan-European information system for enhanced disaster awareness

Information management in disaster situations is challenging, yet critical for efficient response and recovery. Today information flows are difficult to establish, partial, redundant, overly complex or insecure, besides the interoperability between heterogeneous organisations is limited. This paper presents a novel system architecture that enables combining of several communication technologies in a secure manner. This supports creation of a pan-European 'Common Information Space' by rescue organizations that can enable more efficient and effective information management in disaster response. Moreover, this technology can be used for disaster preparedness (e.g., training, tutorials). The modular architecture is designed to consider future evolutions of technology by defining interfaces for the integration of new technologies and services