Automatic service categorisation through machine learning in emergent middleware

The modern environment of mobile, pervasive, evolving services presents a great challenge to traditional solutions for enabling interoperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To overcome this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface description. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmaking between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project

Sensory semantic user interfaces (SenSUI)

Rapid evolution of the World Wide Web with its underlying sources of data, knowledge, services and applications continually attempts to support a variety of users, with different backgrounds, requirements and capabilities. In such an environment, it is highly unlikely that a single user interface will prevail and be able to fulfill the requirements of each user adequately. Adaptive user interfaces are able to adapt information and application functionalities to the user context. In contrast, pervasive computing and sensor networks open new opportunities for context aware platforms, one that is able to improve user interface adaptation reacting to environmental and user sensors. Semantic web technologies and ontologies are able to capture sensor data and provide contextual information about the user, their actions, required applications and environment. This paper investigates the viability of an approach where semantic web technologies are used to maximize the efficacy of interface adaptation through the use of available ontology

Machine learning for emergent middleware

Highly dynamic and heterogeneous distributed systems are challenging today's middleware technologies. Existing middleware paradigms are unable to deliver on their most central promise, which is offering interoperability. In this paper, we argue for the need to dynamically synthesise distributed system infrastructures according to the current operating environment, thereby generating "Emergent Middleware'' to mediate interactions among heterogeneous networked systems that interact in an ad hoc way. The paper outlines the overall architecture of Enablers underlying Emergent Middleware, and in particular focuses on the key role of learning in supporting such a process, spanning statistical learning to infer the semantics of networked system functions and automata learning to extract the related behaviours of networked systems

Final CONNECT Architecture

Interoperability remains a fundamental challenge when connecting heterogeneous systems which encounter and spontaneously communicate with one another in pervasive computing environments. This challenge is exasperated by the highly heterogeneous technologies employed by each of the interacting parties, i.e., in terms of hardware, operating system, middleware protocols, and application protocols. The key aim of the CONNECT project is to drop this heterogeneity barrier and achieve universal interoperability. Here we report on the revised CONNECT architecture, highlighting the integration of the work carried out to integrate the CONNECT enablers developed by the different partners; in particular, we present the progress of this work towards a finalised concrete architecture. In the third year this architecture has been enhanced to: i) produce concrete CONNECTors, ii) match networked systems based upon their goals and intent, and iii) use learning technologies to find the affordance of a system. We also report on the application of the CONNECT approach to streaming based systems, further considering exploitation of CONNECT in the mobile environment

SOME APPROACHES TO TEXT MINING AND THEIR POTENTIAL FOR SEMANTIC WEB APPLICATIONS

In this paper we describe some approaches to text mining, which are supported by an original software system developed in Java for support of information retrieval and text mining (JBowl), as well as its possible use in a distributed environment. The system JBowl1 is being developed as an open source software with the intention to provide an easily extensible, modular framework for pre-processing, indexing and further exploration of large text collections. The overall architecture of the system is described, followed by some typical use case scenarios, which have been used in some previous projects. Then, basic principles and technologies used for service-oriented computing, web services and semantic web services are presented. We further discuss how the JBowl system can be adopted into a distributed environment via technologies available already and what benefits can bring such an adaptation. This is in particular important in the context of a new integrated EU-funded project KP-Lab2 (Knowledge Practices Laboratory) that is briefly presented as well as the role of the proposed text mining services, which are currently being designed and developed there

Project Final Report Use and Dissemination of Foreground

This document is the final report on use and dissemination of foreground, part of the CONNECT final report. The document provides the lists of: publications, dissemination activities, and exploitable foregroun

BIM and asset management (AM) interoperability towards the adoption of digital twins

Digital Twin (DT) is seen as the new shift paradigm for digital transformation in the post-pandemic-built environment sector. Learning from the implementation of Building Information Modelling (BIM) for Asset Management (AM), there are recurring themes that the interoperability challenge is the key barrier to firstly overcome. The absence of a critical overview of issues and research related to BIM-AM interoperability, is hindering the achievement of sustainable and effective integration between the two systems. Therefore, the aim of this paper is to review and analyse the trends and overview of the BIM-AM interoperability publications, identifies and classifies the major research topics on BIM-AM interoperability and finally, proposes future research interoperability directions for DT initiative adoption. To achieve the research aim, a systematic review of the work done to achieve interoperability using a wide range of digital technologies published between 2010 and 2020 related to the interoperability challenges between BIM, and AM and real-time data sources was conducted

Middle-out domain-specific aspect languages and their application in agent-based modelling runtime inspection

Domain-Specific Aspect Languages (DSALs) are a valuable tool for separating cross-cutting concerns, particularly within fields with endemic cross-cutting practices. Agent-Based Modelling (ABM) runtime inspection, which cuts across the core concern of model development, serves as a prime example. Despite their usefulness, DSALs face multiple adoption issues: the literature regarding their development and use is incohesive, coupling to a weave target hinders re-use, and available tooling is immature compared to Domain-Specific Languages (DSLs). We believe these issues can be aided by furthering DSL middle-out techniques for DSALs.We first define the background of what a DSAL is and how they may be used, moving onto how we can use DSL techniques to further DSALs. We develop a middle-out semantic model approach for developing domain-level DSALs with transparent aspect orientation using adaptions of DSL techniques. We have implemented the approach for model-specific DSALs for the in-house framework Animaux, and as middleware-specific DSAL for agent messages in the JADE framework, which can be specialised to models using extension DSALs. We give illustrative result cases using our implementations to provide a base of the user development costs and performance of this approach.In conclusion, we believe the adoption of these technologies aids ABM applications and encourage future work in similar fields. This thesis has given a base philosophy toward DSLs, a novel approach for the development of middle-out DSALs and illustrative cases of this approach

Dynamic Connector Synthesis: Principles, Methods, Tools and Assessment

CONNECT adopts a revolutionary approach to the seamless networking of digital systems, that is, onthe- fly synthesis of the connectors via which networked systems communicate. Within CONNECT, the role of the WP3 work package is to devise automated and efficient approaches to the synthesis of such emergent connectors, provided the behavioral specification of the components to be connected. Thanks to WP3 scientific and technology development, emergent connectors can be synthesized on the fly as networked systems get discovered, implementing the necessary mediation between networked systems' protocols, from application down to middleware layers. This document being the final report about WP3 achievements, it outlines both: (i) specific contributions over the reporting period, and (ii) overall contributions in the area of automated, on-the-fly protocol mediation, from theory to supporting tool