The paper presents a novel approach to online application of formalized rules for medical treatment procedures when processing data from personal medical devices. The rules are formalized by using a rule-based reasoning approach and are applied in order to enhance patient safety and support physicians in their daily work. The presented approach relies on dividing data processing into two stages: (1) the event processing stage and (2) the knowledge application stage. At the event processing stage raw data produced by personal medical devices is transformed into an aggregated/correlated form, as required by the rules for treatment procedures. At the knowledge application stage formalized rules are applied to transformed data, resulting in execution of various support actions. This paper describes how rules for treatment of patients suffering from cardiovascular diseases can be expressed in terms of an event processing statement set and a rule engine knowledge base. The technical feasibility of the proposed approach is supported by a detailed description of the TeleCARE remote healthcare framework - an implementation of the proposed approach along with evaluation performed using a large number of simulated personal medical devices.
Abstract. Recent advances in the development of information systems have led to increased complexity and cost in terms of the required maintenance and management. On the other hand, systems built in accordance with modern architectural paradigms, such as Service Oriented Architecture (SOA), posses features enabling extensive adaptation, not present in traditional systems. Automatic adaptation mechanisms can be used to facilitate system management. The goal of this work is to show that automatic adaptation can be effectively implemented in SOA systems using machine learning algorithms. The presented concept relies on a combination of clustering and reinforcement learning algorithms. The paper discusses assumptions which are necessary to apply machine learning algorithms to automatic adaptation of SOA systems, and presents a machine learning-based management framework prototype. Possible benefits and disadvantages of the presented approach are discussed and the approach itself is validated with a representative case study.
This paper describes a programming toolkit developed in the PL-Grid project, named QStorMan, which supports storage QoS provisioning for data-intensive applications in distributed environments. QStorMan exploits knowledgeoriented methods for matching storage resources to non-functional requirements, which are defined for a data-intensive application. In order to support various usage scenarios, QStorMan provides two interfaces, such as programming libraries or a web portal. The interfaces allow to define the requirements either directly in an application source code or by using an intuitive graphical interface.The first way provides finer granularity, e.g., each portion of data processed by an application can define a different set of requirements. The second method is aimed at legacy applications support, which source code can not be modified. The toolkit has been evaluated using synthetic benchmarks and the production infrastructure of PL-Grid, in particular its storage infrastructure, which utilizes the Lustre file system.
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