Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Knowledge management support for enterprise distributed systems

Explosion of information and increasing demands on semantic processing web applications have software systems to their limits. To address the problem we propose a semantic based formal framework (ADP) that makes use of promising technologies to enable knowledge generation and retrieval. We argue that this approach is cost effective, as it reuses and builds on existing knowledge and structure. It is also a good starting point for creating an organisational memory and providing knowledge management functions

Updated version of final design and of the architecture of SEAMLESS-IF

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Approaches Regarding Business Logic Modeling in Service Oriented Architecture

As part of the Service Oriented Computing (SOC), Service Oriented Architecture (SOA) is a technology that has been developing for almost a decade and during this time there have been published many studies, papers and surveys that are referring to the advantages of projects using it. In this article we discuss some ways of using SOA in the business environment, as a result of the need to reengineer the internal business processes with the scope of moving forward towards providing and using standardized services and achieving enterprise interoperability.Business Rules, Business Processes, SOA, BPM, BRM, Semantic Web, Semantic Interoperability

An Ontology-Based Knowledge Modelling for Sustainable Entrepreneurship Domain

Sustainable entrepreneurship (SE) focuses on entrepreneurial activities taking into account environmental and social issues, not only by paying attention to economic issues. While the concept of SE is a prominent stream for modern entrepreneurship, the following questions arise: how enterprises can run a business and implement Sustainable Development Goals (SDG) and which factors can support or constrain SE? Research on SE suggests that that identifying and implementing sustainable development opportunity is more complex for the entrepreneur than the recognition of non-sustainable opportunity. Therefore this article contributes to the advancement of sustainable entrepreneurship research by offering an ontology-based approach collecting factors from the current literature review and incorporating different lines of research that can influence further sustainable entrepreneurial strategies. The applied research methodology exploits a previously elaborated bibliometric analysis which included a systematic literature review conducted using the PRISMA methodology. The condensed immense amount of bibliometric information and further co-occurrence analysis of keywords determining SE factors is a basis for constructing ontology-based model. This model offers a classification schema of sustainable entrepreneurship factors as well as a tool performing knowledge from being machine-readable to machine-understandable

The Semantic Web Revisited

The original Scientific American article on the Semantic Web appeared in 2001. It described the evolution of a Web that consisted largely of documents for humans to read to one that included data and information for computers to manipulate. The Semantic Web is a Web of actionable information--information derived from data through a semantic theory for interpreting the symbols.This simple idea, however, remains largely unrealized. Shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks; they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the Semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years. Furthermore, we see the use of ontologies in the e-science community presaging ultimate success for the Semantic Web--just as the use of HTTP within the CERN particle physics community led to the revolutionary success of the original Web. This article is part of a special issue on the Future of AI