Conceptual structures in LEADing and best enterprise practices

Conceptual Structures, namely Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are beginning to make an impact in Industry. This is evidenced in LEAD as it seeks to provide its 3100+ industry practitioners in many Fortune 500 and public organisations with capabilities that can handle ontology and semantics. The existing ontology and semantics work in LEAD,supported by the Global University Alliance, is described and how CGs, FCA and their tools (e.g. CoGui, CG-FCA) enhance this endeavour

An ant colony optimization algorithm-based classification for the diagnosis of primary headaches using a website questionnaire expert system

The purpose of this research was to evaluate the classification accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified patients with 96.9412% overall accuracy. Diagnosis accuracies of migraine, tension-type, and cluster headaches were 98.2%, 92.4%, and 98.2% respectively. The ant colony optimization-based algorithm has a successful classification potential on headache diagnosis. On the other hand, headache diagnosis using a website-based algorithm will be useful for neurologists in order to gather quick and precise results as well as tracking patients for their headache symptoms and medication usage by using electronic records from the Internet

Fuzzy-GRA trust model for cloud risk management

Cloud computing is not adequately secure due to the currently used traditional trust methods such as global trust model and local trust model. These are prone to security vulnerabilities. This paper introduces a trust model based on the fuzzy mathematics and gray relational theory. Fuzzy mathematics and gray relational analysis (Fuzzy-GRA) aims to improve the poor dynamic adaptability of cloud computing. Fuzzy-GRA platform is used to test and validate the behavior of the model. Furthermore, our proposed model is compared to other known models. Based on the experimental results, we prove that our model has the edge over other existing models

A Transaction-oriented architecture for enterprise systems

Many enterprises risk business transactions based on information systems that are incomplete or misleading, given that 80-85% of all corporate information remains outside of their processing scope. It highlights that the bulk of information is too unstructured for these systems to process, but must be taken into account if those systems are to provide effective support. Computer technology nonetheless continues to become more and more predominant, illustrated by SAP A.G. recognising that 65-70% of the world's transactions are run using their technology. Using SAP as an illustrative case study, and by bringing in the benefits of technologies such as Service-Oriented Architecture (SOA), Business Process Management (BPM), Enterprise Architecture Frameworks (EA) and Conceptual Structures, a practical roadmap is identified to a Transaction-Oriented Architecture (TOA) that is predicated on the Transaction Concept. This concept builds upon the Resources-Events-Agents (REA) modelling pattern that is close to business reality. Enterprise systems can thus better incorporate that missing 80-85% of hitherto too-unstructured information thereby allowing enterprise systems vendors such as SAP, their competitors, customers, suppliers and partners to do an ever better job with the world's transactions

Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid

Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing.The proposed approach is based on a high level hybridization.The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix.Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average makespan values

Trust-Based Techniques for Collective Intelligence in Social Search Systems.

A key-issue for the effectiveness of collaborative decision support systems is the problem of the trustworthiness of the entities involved in the process. Trust has been always used by humans as a form of collective intelligence to support effective decision making process. Computational trust models are becoming now a popular technique across many applications such as cloud computing, p2p networks, wikis, e-commerce sites, social network. The chapter provides an overview of the current landscape of computational models of trust and reputation, and it presents an experimental study case in the domain of social search, where we show how trust techniques can be applied to enhance the quality of social search engine predictions

Analysing Trust Issues in Cloud Identity Environments

Trust acts as a facilitator for decision making in environments, where decisions are subject to risk and uncertainty. Security is one of the factors contributing to the trust model that is a requirement for service users. In this paper we ask, What can be done to improve end user trust in choosing a cloud identity provider? Security and privacy are central issues in a cloud identity environment and it is the end user who determines the amount of trust they have in any identity system. This paper is an in-depth literature survey that evaluates identity service delivery in a cloud environment from the perspective of the service user

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan