Oleksiy Khriyenko

General Adaption Framework: Enabling Interoperability for Industrial Web Resources

Integration of heterogeneous applications and data sources into an interoperable system is one of the most relevant challenges for many knowledge-based corporations nowadays. Development of a global environment that would support knowledge transfer from human experts to automated Web services, which are able to learn, is a very profit-promising and challenging task. The domain of industrial maintenance is not an exception. This paper outlines in detail an approach for adaptation of heterogeneous Web resources into a unified environment as a first step toward interoperability of smart industrial resources, where distributed human experts and learning Web services are utilized by various devices for self monitoring and self diagnostics. The proposed General Adaptation Framework utilizes a potential of the Semantic Web technology and primarily focuses on the aspect of a semantic adaptation (or mediation) of existing widely used models of data representation to RDF-based semantically rich format. To perform the semantic adaptation of industrial resources, the approach of two-stage transformation (syntactical and semantic) is elaborated and implemented for monitoring of a concrete industrial device with underlying XML-based data representation model as a use case.

RgbDF: Resource Goal and Behaviour Description Framework

Agent-oriented approach has proven to be very efficient in engineering complex distributed software environments with dynamically changing conditions. The efficiency of underlying modelling framework for this domain is undoubtedly of a crucial importance. Currently, a model-driven architecture has been the most popular and developed for purposes of modelling different aspects of multi-agent systems, including behaviour of individual agents. UML is utilized as a basis for this modelling approach and variety of existing UML-based modelling tools after slight extension are reused. This paper proposes an ontology-driven approach to modelling agent behaviour as an emerging paradigm that originates from the Semantic Web wave. The proposed approach aims at modelling a proactive behaviour of (web-)resources through their representatives: software agents. In general, the presented research puts efforts into investigation of beneficial features of ontology-based agent modelling in comparison with conventional model-driven approaches.

Emotional Business Intelligence : Enabling experience-centric business with the FeelingsExplorer

The domain of Emotional Business Intelligence (EBI) aims to support business-relevant emotional and emotion-aware decisions in addition to rational decision making. EBI originates from three root domains: Emotional Business, Emotional Intelligence and Business Intelligence (BI). In this paper we emphasize emotional empowerment of the traditional BI function; outline its main characteristics as a business working model of an emotionally smart, continuously learning organization; and introduce a first candidate of the EBI Toolkit, the FeelingsExplorer (FE). FE is a mash-up browser based on 4i (“ForEye”) technology, capable of visualizing objects in an emotional semantic space and thereby supporting decision making on emotional grounds. It takes metadata as input and visualizes the personalized “emotional similarity” of products, services, and customers. Different scenarios for FE application and overall implications of EBI for business and human technology are discussed. Decision support systems, decision making, context-aware computing, affective computing, emotive data, Business Intelligence, Customer Experience

Customer Feedback System - Evolution towards Semantically-enhanced Systems.

The digital economy requires services be created in nearly real time – while continuously listening to the customer. Managing and analysing the data collected about products and customers become very critical. Successful companies must collect data regarding customer behaviour in a sensible manner, understand their customers and engage in constant interaction with them. Nowadays, having a huge data storage capacity, everyone collects data and hopes that it will be useful someday. But, it is frustrating when you do not know whether something useful will come out of it. It is not a problem to collect data, but it is very difficult to analyse it. To utilize the data they collect and analyse customer feedback quickly, companies require automation of customer feedback processing. To hear a real voice of a customer, companies are trying to engage customer to the feedback provisioning process. Therefore, the paper reviews digitalized customer feedback strategies, highlights challenges of a feedback gathering and further computation. As a result, paper presents an approach for semantic enhancement of a customer feedback system.

Emotional Business Intelligence

The domain of Emotional Business Intelligence (EBI) aims to support business-relevant emotional and emotion-aware decisions in addition to rational decision making. EBI originates from three root domains: Emotional Business, Emotional Intelligence and Business Intelligence (BI). In this paper we emphasize emotional empowerment of the traditional BI function; outline its main characteristics as a business working model of an emotionally smart, continuously learning organization; and introduce a first candidate of the EBI Toolkit, the FeelingsExplorer (FE). FE is a mash-up browser based on 4i (“ForEye”) technology, capable of visualizing objects in an emotional semantic space and thereby supporting decision making on emotional grounds. It takes metadata as input and visualizes the personalized “emotional similarity” of products, services, and customers. Different scenarios for FE application and overall implications of EBI for business and human technology are discussed.

Cognitive Computing supported Medical Decision Support System for Patient’s Driving Assessment

To smartly utilize a huge and constantly growing volume of data, improve productivity and increase competitiveness in various fields of life; human requires decision making support systems that efficiently process and analyze the data, and, as a result, significantly speed up the process. Similarly to all other areas of human life, healthcare domain also is lacking Artificial Intelligence (AI) based solution. A number of supervised and unsupervised Machine Learning and Data Mining techniques exist to help us to deal with structured data. However, in a real life, we pretty much deal with unstructured data that hides useful knowledge and valuable information inside human-readable plain texts, images, audio and video. Therefore, such IT giants as IBM, Google, Microsoft, Intel, Facebook, etc., as well as variety of SMEs are actively elaborating different Cognitive Computing services and tools to get a value from unstructured data. Thus, the paper presents feasibility study of IBM Watson cognitive computing services and tools to address the issue of automated health records processing to support doctor’s decision for patient’s driving assessment.

Propaganda Barometer : A Supportive Tool to Improve Media Literacy Towards Building a Critically Thinking Society

To smartly consume a huge and constantly growing volume of information, to identify fake news and resist propaganda in the context of Information Warfare, to improve personal critical thinking capabilities and increase media literacy, people require supportive environment with sophisticated technology facilitated tools. With rapid development of media, widespread popularity of social networks and fast growing amount of information distribution channels, propaganda and information warfare enter an absolutely new digital technology supported cyber era. Propaganda mining is not a trivial and very time consuming process for human. And, as with any new technology, human need certain time to understand its actual purpose, learn and adapt own behavior making consumption of a technology more valuable, beneficial and enjoyable. To make adoption faster and minimize possible harmful influence, we need to find a proper way to apply currently available technologies and knowledge for elaboration of supportive tool that helps information consumers to become more independent, insightful, and critical.

An Intelligent Learning Support System

Fast-growing technologies are shaping many aspects of societies. Educational systems, in general, are still rather traditional: learner applies for school or university, chooses the subject, takes the courses, and finally graduates. The problem is that labor markets are constantly changing and the needed professional skills might not match with the curriculum of the educational program. It might be that it is not even possible to learn a combination of desired skills within one educational organization. For example, there are only a few universities that can provide high-quality teaching in several different areas. Therefore, learners may have to study specific modules and units somewhere else, for example, in massive open online courses. A person, who is learning some particular content from outside of the university, could have some knowledge gaps which should be recognized. We argue that it is possible to respond to these challenges with adaptive, intelligent, and personalized learning systems that utilize data analytics, machine learning, and Semantic Web technologies. In this paper, we propose a model for an Intelligent Learning Support System that guides learner during the whole lifecycle using semantic annotation methodology. Semantic annotation of learning materials is done not only on the course level but also at the content level to perform semantic reasoning about the possible learning gaps. Based on this reasoning, the system can recommend extensive learning material.

Smart Educational Process Based on Personal Learning Capabilities

Personalized learning is increasingly gaining popularity, especially with the development of information technology and modern educational resources for learning. Each person is individual and has different knowledge background, different kind of memory, different learning speed. Teacher can adapt learning course, learning instructions or learning material according to the majority of learners in class, but that means that learning process is not adapted to the personality of each individual learner. That is why it is important to have smart educational process based on personal learning capabilities. This paper presents a literature survey on different learning systems which detects learning progress and based on that a model of smart educational system which use knowledge engineering and Watson technology is proposed. This system is relevant both for basic education and for adult education.

Customer Perception Driven Product Evolution : Facilitation of Structured Feedback Collection

Competitive environment not only requires effective advertising strategies from the product producers and service providers, but also to do comprehensive and sufficient analysis of their customers to understand their needs and expectations. Successfully involving customers into a product/service co-creation process, companies more likely increase their future revenue. Customer feedback analysis is widely applied in marketing and product development. Among other challenges (e.g. customer engagement, feedback collection, etc.) automation of customer feedback analysis becomes very demanding task and requires advance intelligent tools to understand customers’ product perception and preferences. Since, mining of free text feedbacks (which is still the most representing form of the real voice of the customer) is challenging, this work presents an approach towards customer-supported transformation of feedback into structured data. Further analysis and manipulation with semantically enhanced customer feedback and product/service description makes possible to automatically generate useful changes in existing products or even a new product description that takes into account actual needs and preferences of customers.