Using webcrawling of publicly available websites to assess E-commerce relationships

We investigate e-commerce success factors concerning their impact on the success of commerce transactions between businesses companies. In scientific literature, many e-commerce success factors are introduced. Most of them are focused on companies' website quality. They are evaluated concerning companies' success in the business-to- consumer (B2C) environment where consumers choose their preferred e-commerce websites based on these success factors e.g. website content quality, website interaction, and website customization. In contrast to previous work, this research focuses on the usage of existing e-commerce success factors for predicting successfulness of business-to-business (B2B) ecommerce. The introduced methodology is based on the identification of semantic textual patterns representing success factors from the websites of B2B companies. The successfulness of the identified success factors in B2B ecommerce is evaluated by regression modeling. As a result, it is shown that some B2C e-commerce success factors also enable the predicting of B2B e-commerce success while others do not. This contributes to the existing literature concerning ecommerce success factors. Further, these findings are valuable for B2B e-commerce websites creation

JuxtaLearn D3.2 Performance Framework

This deliverable, D3.2, for Work Package 3 incorporating the pedagogy from WP2 and orchestration factors mapped in D3.1 reviews aspects of performance in the context of participative video making. It reviews literature on curiosity and engagement characteristics of interaction mechanisms for public displays and anticipates requirements for social network analysis of relevant public videos from WP6 task 6.3. Thus, to support JuxtaLearn performance it proposes a reflective performance framework that encompasses the material environment and objects required, the participants, and the knowledge needed

A Potentiality and Conceptuality Interpretation of Quantum Physics

We elaborate on a new interpretation of quantum mechanics which we introduced recently. The main hypothesis of this new interpretation is that quantum particles are entities interacting with matter conceptually, which means that pieces of matter function as interfaces for the conceptual content carried by the quantum particles. We explain how our interpretation was inspired by our earlier analysis of non-locality as non-spatiality and a specific interpretation of quantum potentiality, which we illustrate by means of the example of two interconnected vessels of water. We show by means of this example that philosophical realism is not in contradiction with the recent findings with respect to Leggett's inequalities and their violations. We explain our recent work on using the quantum formalism to model human concepts and their combinations and how this has given rise to the foundational ideas of our new quantum interpretation. We analyze the equivalence of meaning in the realm of human concepts and coherence in the realm of quantum particles, and how the duality of abstract and concrete leads naturally to a Heisenberg uncertainty relation. We illustrate the role played by interference and entanglement and show how the new interpretation explains the problems related to identity and individuality in quantum mechanics. We put forward a possible scenario for the emergence of the reality of macroscopic objects.Comment: 20 pages, 1 figur

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF