mPower: A component-based development framework for multi-agent systems to support business processes

One of the obstacles preventing the widespread adoption of multi-agent systems in industry is the difficulty of implementing heterogeneous interactions among participating agents via asynchronous messages. This difficulty arises from the need to understand how to combine elements of various content languages, ontologies, and interaction protocols in order to construct meaningful and appropriate messages. In this paper mPower, a component-based layered framework for easing the development of multi-agent systems, is described, and the facility for customising the components for reuse in similar domains is explained. The framework builds on the JADE-LEAP platform, which provides a homogeneous layer over diverse operating systems and hardware devices, and allows ubiquitous deployment of applications built on multi-agent systems both in wired and wireless environments. The use of the framework to develop mPowermobile , a multi-agent system to support mobile workforces, is reported

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data

Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the user’s ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data. We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive

A Semantic-Based Information Management System to Support Innovative Product Design

International competition and the rapidly global economy, unified by improved communication and transportation, offer to the consumers an enormous choice of goods and services. The result is that companies now require quality, value, time to market and innovation to be successful in order to win the increasing competition. In the engineering sector this is traduced in need of optimization of the design process and in maximization of re-use of data and knowledge already existing in the company. The “SIMI-Pro” (Semantic Information Management system for Innovative Product design) system addresses specific deficiencies in the conceptual phase of product design when knowledge management, if applied, is often sectorial. Its main contribution is in allowing easy, fast and centralized collection of data from multiple sources and in supporting the retrieval and re-use of a wide range of data that will help stylists and engineers shortening the production cycle. SIMI-Pro will be one of the first prototypes to base its information management and its knowledge sharing system on process ontology and it will demonstrate how the use of centralized network systems, coupled with Semantic Web technologies, can improve inter-working activities and interdisciplinary knowledge sharing

Ontology-based model abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models’ information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models

Link Before You Share: Managing Privacy Policies through Blockchain

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable Information (PII). We have developed a novel framework to automatically track details about how a users' PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE International Conference on Big Data (IEEE BigData 2017) December 14, 2017, Boston, MA, US