INFRAWEBS semantic web service development on the base of knowledge management layer

The paper gives an overview about the ongoing FP6-IST INFRAWEBS project and describes the main layers and software components embedded in an application oriented realisation framework. An important part of INFRAWEBS is a Semantic Web Unit (SWU) – a collaboration platform and interoperable middleware for ontology-based handling and maintaining of SWS. The framework provides knowledge about a specific domain and relies on ontologies to structure and exchange this knowledge to semantic service development modules. INFRAWEBS Designer and Composer are sub-modules of SWU responsible for creating Semantic Web Services using Case-Based Reasoning approach. The Service Access Middleware (SAM) is responsible for building up the communication channels between users and various other modules. It serves as a generic middleware for deployment of Semantic Web Services. This software toolset provides a development framework for creating and maintaining the full-life-cycle of Semantic Web Services with specific application support

A Constrained, Possibilistic Logical Approach for Software System Survivability Evaluation

In this paper, we present a logical framework to facilitate users in assessing a software system in terms of the required survivability features. Survivability evaluation is essential in linking foreign software components to an existing system or obtaining software systems from external sources. It is important to make sure that any foreign components/systems will not compromise the current system’s survivability properties. Given the increasing large scope and complexity of modern software systems, there is a need for an evaluation framework to accommodate uncertain, vague, or even ill-known knowledge for a robust evaluation based on multi-dimensional criteria. Our framework incorporates user-defined constrains on survivability requirements. Necessity-based possibilistic uncertainty and user survivability requirement constraints are effectively linked to logic reasoning. A proof-of-concept system has been developed to validate the proposed approach. To our best knowledge, our work is the first attempt to incorporate vague, imprecise information into software system survivability evaluation

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Interval Neutrosophic Sets and Logic: Theory and Applications in Computing

A neutrosophic set is a part of neutrosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a powerful general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Here, we define the set-theoretic operators on an instance of a neutrosophic set, and call it an Interval Neutrosophic Set (INS). We prove various properties of INS, which are connected to operations and relations over INS. We also introduce a new logic system based on interval neutrosophic sets. We study the interval neutrosophic propositional calculus and interval neutrosophic predicate calculus. We also create a neutrosophic logic inference system based on interval neutrosophic logic. Under the framework of the interval neutrosophic set, we propose a data model based on the special case of the interval neutrosophic sets called Neutrosophic Data Model. This data model is the extension of fuzzy data model and paraconsistent data model. We generalize the set-theoretic operators and relation-theoretic operators of fuzzy relations and paraconsistent relations to neutrosophic relations. We propose the generalized SQL query constructs and tuple-relational calculus for Neutrosophic Data Model. We also design an architecture of Semantic Web Services agent based on the interval neutrosophic logic and do the simulation study

Ontology-based knowledge representation and semantic search information retrieval: case study of the underutilized crops domain

The aim of using semantic technologies in domain knowledge modeling is to introduce the semantic meaning of concepts in knowledge bases, such that they are both human-readable as well as machine-understandable. Due to their powerful knowledge representation formalism and associated inference mechanisms, ontology-based approaches have been increasingly adopted to formally represent domain knowledge. The primary objective of this thesis work has been to use semantic technologies in advancing knowledge-sharing of Underutilized crops as a domain and investigate the integration of underlying ontologies developed in OWL (Web Ontology Language) with augmented SWRL (Semantic Web Rule Language) rules for added expressiveness. The work further investigated generating ontologies from existing data sources and proposed the reverse-engineering approach of generating domain specific conceptualization through competency questions posed from possible ontology users and domain experts. For utilization, a semantic search engine (the Onto-CropBase) has been developed to serve as a Web-based access point for the Underutilized crops ontology model. Relevant linked-data in Resource Description Framework Schema (RDFS) were added for comprehensiveness in generating federated queries. While the OWL/SWRL combination offers a highly expressive ontology language for modeling knowledge domains, the combination is found to be lacking supplementary descriptive constructs to model complex real-life scenarios, a necessary requirement for a successful Semantic Web application. To this end, the common logic programming formalisms for extending Description Logic (DL)-based ontologies were explored and the state of the art in SWRL expressiveness extensions determined with a view to extending the SWRL formalism. Subsequently, a novel fuzzy temporal extension to the Semantic Web Rule Language (FT-SWRL), which combines SWRL with fuzzy logic theories based on the valid-time temporal model, has been proposed to allow modeling imprecise temporal expressions in domain ontologies

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable