Towards Autonomic Computing: Effective Event Management

Autonomic Computing is emerging as a significant new approach for the design of computing systems. Its goal is the production of systems that are self-managing, self-healing, self-protecting and self-optimizing. Achieving this goal will involve techniques from both Software Engineering and Artificial Intelligence. This paper discusses one particular aspect of Autonomic Computing: event management. It considers the range of event handling techniques in use, particularly in relation to distributed systems. Intelligent approaches are illustrated using the example of event handling in telecommunication systems. In particular, the telecom survivable network architecture is analyzed to identify lessons and potential pitfalls for Autonomic Computing

Discovery of association rules from medical data -classical and evolutionary approaches

The paper presents a method of association rules discovering from medical data using the evolutionary approach. The elaborated method (EGAR) uses a genetic algorithm as a tool of knowledge discovering from a set of data, in the form of association rules. The method is compared with known and common method - FPTree. The developed computer program is applied for testing the proposed method and comparing the results with those produced by FPTree. The program is the general and flexible tool for the rules generation task using different data sets and two embodied methods. The presented experiments are performed using the actual medical data from the Wroclaw Clinic

JCLEC Meets WEKA!

WEKA has recently become a very referenced DM tool. In spite of all the functionality it provides, it does not include any framework for the development of evolutionary algorithms. An evolutionary computation framework is JCLEC, which has been successfully employed for developing several EAs. The combination of both may lead in a mutual bene t. Thus, this paper proposes an intermediate layer to connect WEKA with JCLEC. It also presents a study case which samples the process of including a JCLEC's EA into WEK

Evolutionary algorithms : concepts and applications

Evolutionary algorithms are a family of stochastic problem-solving techniques, within the broader category of what we might call \u201cnatural-metaphor models\u201d, together with neural networks, ant systems, etc. They find their inspiration in biology and, in particular, they are based on mimicking the mechanisms of what we know as \u201cnatural evolution\u201d. During the last twenty-five years these techniques have been applied to a large number of problems of great practical and economic importance with excellent results. This paper presents a survey of these techniques and a few sample applications

Implementation of context-aware workflows with Multi-agent Systems

Systems in Ambient Intelligence (AmI) need to manage workflows that represent users’ activities. These workflows can be quite complex, as they may involve multiple participants, both physical and computational, playing different roles. Their execution implies monitoring the development of the activities in the environment, and taking the necessary actions for them and the workflow to reach a certain end. The context-aware approach supports the development of these applications to cope with event processing and regarding information issues. Modeling the actors in these context-aware workflows, where complex decisions and interactions must be considered, can be achieved with multi-agent systems. Agents are autonomous entities with sophisticated and flexible behaviors, which are able to adapt to complex and evolving environments, and to collaborate to reach common goals. This work presents architectural patterns to integrate agents on top of an existing context-aware architecture. This allows an additional abstraction layer on top of context-aware systems, where knowledge management is performed by agents.This approach improves the flexibility of AmI systems and facilitates their design. A case study on guiding users in buildings to their meetings illustrates this approach

Cognitive Computing Creates Value In Healthcare and Shows Potential for Business Value

This research paper examines cognitive computing relative to how businesses in healthcare may use cognitive systems to analyze big data to create a competitive advantage. It explains the underlying technologies, such as machine learning and natural language processing, and gives an overview of the technology driving the world\u27s most popular cognitive computing system, IBM Watson. It examines case studies that show businesses applying cognitive systems to derive value from big data and discusses how this may be used to develop business value and provide analysis for strategic processing. It also touches on challenges of cognitive computing. The paper concludes with lessons learned and future research

Text mining with exploitation of user\u27s background knowledge : discovering novel association rules from text

The goal of text mining is to find interesting and non-trivial patterns or knowledge from unstructured documents. Both objective and subjective measures have been proposed in the literature to evaluate the interestingness of discovered patterns. However, objective measures alone are insufficient because such measures do not consider knowledge and interests of the users. Subjective measures require explicit input of user expectations which is difficult or even impossible to obtain in text mining environments. This study proposes a user-oriented text-mining framework and applies it to the problem of discovering novel association rules from documents. The developed system, uMining, consists of two major components: a background knowledge developer and a novel association rules miner. The background knowledge developer learns a user\u27s background knowledge by extracting keywords from documents already known to the user (background documents) and developing a concept hierarchy to organize popular keywords. The novel association rule miner discovers association rules among noun phrases extracted from relevant documents (target documents) and compares the rules with the background knowledge to predict the rule novelty to the particular user (useroriented novelty). The user-oriented novelty measure is defined as the semantic distance between the antecedent and the consequent of a rule in the background knowledge. It consists of two components: occurrence distance and connection distance. The former considers the co-occurrences of two keywords in the background documents: the more the shorter the distance. The latter considers the common connections of with others in the concept hierarchy. It is defined as the length of the connecting the two keywords in the concept hierarchy: the longer the path, distance. The user-oriented novelty measure is evaluated from two perspectives: novelty prediction accuracy and usefulness indication power. The results show that the useroriented novelty measure outperforms the WordNet novelty measure and the compared objective measures in term of predicting novel rules and identifying useful rules