Personalized configuration of immaterial products

Product configuration deals with design of a new product from existing components. Recently, research on product configuration has shifted to the stage of conceptual modelling. Conceptual product models do not depend on the modelling purpose and therefore can be tailored to the current customer needs. The paper proposes an ontology-based scenario for configuration of immaterial products. The scenario suggests three product configuration operations: removal, supplement, and change. Product customization is supported by involvement of the customer in the process of configuration and by using information from the customer profile. The scenario execution is demonstrated by a particular case of configuration of a mobile operator product in the form of supplement this product with a service. An ontology for mobile product operator is proposed. OWL and SPARQL are used for ontology specification and querying

Ontology for cyber-physical-social systems self-organisation

Cyber-Physical-Social Systems (CPSSs) integrate various resources from physical, cyber, and social worlds. Efficient interaction of these resources is essential for CPSSs operation. Ontologies do not only provide for semantic operability between different resources but also provide means to create sharable ontology-based context models specified for actual settings. Usage of the context supports situation-driven behavior of CPSSs resources and thus is an enabler for their self-organisation. The present research inherits the idea of context ontologies usage for modelling context in CPSSs. In this work, an upper level context ontology for CPSSs is proposed. This ontology is applied in the domain of self-organising resource network

Knowledge Fusion Patterns for Design of Context-Aware Decision Support Systems

The objective of the research is to offer a methodology providing a feasible functionality of context-aware systems for the users. The systems are considered consisting of a set of knowledge sources. These sources are involved in knowledge fusion processes to support the systems functionalities. The core of the methodology is knowledge fusion patterns. They characterize knowledge sources involved in the knowledge fusion processes in terms of preservation/change of the sources' autonomies and structures. In the methodology, the patterns are used to specify the requirements to the knowledge sources from the systems to ensure the full systems functionalities. Matching the system's requirements against the user's requirements to the system functionality and the user's constraints on the sources of information and knowledge enables to offer feasible system functionality to the users

Human-computer cloud for decision support in tourism: Approach and architecture

Tourism is one of the most dynamic and fastest-growing economic sectors, where on-the-fly information and decision support are more actual than ever. Tourist decision support systems today leverage a variety of technologies both machine-driven (GIS or knowledge-based inference) and human-driven (recommendation systems). This paper applies a novel concept of human-computer cloud as an architectural approach to building decision support systems in tourism (both from the tourist's perspective, and from destination management organization's perspective). This concept serves as a unifying basis for using human-based resources, allowing to virtualize them much like “ordinary” computing resources. Particularly, the paper identifies the list of typical decision support tasks in tourism domain, and then maps them to a multi-tiered conceptual architecture of cloud services. The proposed architecture is illustrated by the discussion of two scenarios - one for tourist perspective and one for destination management organization

Situation Detection Based on Knowledge Fusion Patterns

We begin by a thorough revision of four-dimensional superspace supergravity. We present curved superspace geometry, for arbitrary N, including torsion, curvature and Bianchi identities. We motivate the choice of torsion constraints. Next we move to the particular cases of N = 1, 2. In both cases we sho