Hybrid solutions to the feature interaction problem

In this paper we assume a competitive marketplace where the features are developed by different enterprises, which cannot or will not exchange information. We present a classification of feature interaction in this setting and introduce an on-line technique which serves as a basis for the two novel <i>hybrid</i> approaches presented. The approaches are hybrid as they are neither strictly off-line nor on-line, but combine aspects of both. The two approaches address different kinds of feature interactions, and thus are complimentary. Together they provide a complete solution by addressing interaction detection and resolution. We illustrate the techniques within the communication networks domain

A structured approach to VO reconfigurations through Policies

One of the strength of Virtual Organisations is their ability to dynamically and rapidly adapt in response to changing environmental conditions. Dynamic adaptability has been studied in other system areas as well and system management through policies has crystallized itself as a very prominent solution in system and network administration. However, these areas are often concerned with very low-level technical aspects. Previous work on the APPEL policy language has been aimed at dynamically adapting system behaviour to satisfy end-user demands and - as part of STPOWLA - APPEL was used to adapt workflow instances at runtime. In this paper we explore how the ideas of APPEL and STPOWLA can be extended from workflows to the wider scope of Virtual Organisations. We will use a Travel Booking VO as example.Comment: In Proceedings FAVO 2011, arXiv:1204.579

Optimizing Feature Interaction Detection

© 2017, Springer International Publishing AG. The feature interaction problem has been recognized as a general problem of software engineering. The problem appears when a combination of features interacts generating a conflict, exhibiting a behaviour that is unexpected for the features considered in isolation, possibly resulting in some critical safety violation. Verification of absence of critical feature interactions has been the subject of several studies. In this paper, we focus on functional interactions and we address the problem of the 3-way feature interactions, i.e. interactions that occur only when three features are all included in the system, but not when only two of them are. In this setting, we define a widely applicable definition framework, within which we show that a 3 (or greater)-way interaction is always caused by a 2-way interaction, i.e. that pairwise sampling is complete, hence reducing to quadratic the complexity of automatic detection of incorrect interaction

Towards Activity Context using Software Sensors

Service-Oriented Computing delivers the promise of configuring and reconfiguring software systems to address user's needs in a dynamic way. Context-aware computing promises to capture the user's needs and hence the requirements they have on systems. The marriage of both can deliver ad-hoc software solutions relevant to the user in the most current fashion. However, here it is a key to gather information on the users' activity (that is what they are doing). Traditionally any context sensing was conducted with hardware sensors. However, software can also play the same role and in some situations will be more useful to sense the activity of the user. Furthermore they can make use of the fact that Service-oriented systems exchange information through standard protocols. In this paper we discuss our proposed approach to sense the activity of the user making use of software

Composition Context for Web Services Selection

Often there are several services providing similar functionality, moving the problem of selecting the most suitable to the forefront of interest. In this paper we consider the selection of services in a dynamic environment with changing requirements. In previous work we considered selecting services in isolation, here we present an enhancement to select services in their relation to each other to gain a global optimal solution which nevertheless respects local criteria. Novel contributions are the definition of a composition context and the global multi-criteria optimization mechanism

Learning Disease Causality Knowledge from Web of Health Data

Health information becomes importantly valuable to protect public health in the current coronavirus situation. Especially, knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will enable developing causality focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for doing inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies. The experimental work has processed 801 diseases in total from the UK NHS website linking with DBpedia datasets. As the result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently

Automated context-aware service selection for collaborative systems

Service-Oriented Architecture (SOA) can provide a paradigm for constructing context-aware collaboration systems. Particularly, the promise of inexpensive context-aware collaboration devices and context-awareness for supporting the selection of suitable services at run-time have provoked growing adoptation of SOA in collaborative systems. In this paper, we introduce an approach for selecting the most suitable service within a SOA based collaboration system, where suitability depends on the user’s context. The approach includes context modelling, generation of context-aware selection criteria and a suitable service selection methodology

Context-aware automatic service selection

Service-Oriented Architecture (SOA) is a paradigm for developing next generation distributed systems. SOA introduces an opportunity to build dynamically configurable distributed systems by invoking suitable services at runtime, which makes the systems being more flexible to be integrated and easily to be reused. With fast growing numbers of offered services, automatically identifying suitable services becomes a crucial issue. A new and interesting research direction is to select a service which is not only suitable in general but also suitable towards a particular requester's needs and services context at runtime. This dissertation proposes an approach for supporting automatic context-aware service selection and composition in a dynamic environment. The main challenges are: (1) specifying context information in a machine usable form; (2) developing a service selection method which can choose the adequate services by use the context information; (3) introducing context-awareness into the service composition process. To address the challenges, we employ Semantic Web technology for modelling context information and service capabilities to automatically generate service selection criteria at runtime. Meanwhile, a Type-based Logic Scoring Preference Extended (TLE) service selection method is developed to adequately and dynamically evaluate and aggregate the context-aware criteria. In addition, we introduce the composition context and a Backward Composition Context based Service Selection algorithm (BCCbSS) for composing suitable services on the y in a fault-tolerant manner. Furthermore, this dissertation describes the design and implementation of the method and algorithm. Experimental evaluation results demonstrate that the TLE method and BCCbSS algorithm provide an efficient and scalable solution to deal with the context-aware service selection problem both in single service selection and composition scenarios. Our research results make a further step to develop highly automated and dynamically adaptive systems in the future.EThOS - Electronic Theses Online ServiceGBUnited Kingdo