Proximal business intelligence on the semantic web

This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to improve specific information access and transcoding but not on how the information can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology language and then re-used to provide the invisibility of pervasive access; uncovering more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO

Preference Dissemination by Sharing Viewpoints: Simulating Serendipity

IC3K 2015 will be held in conjunction with IJCCI 2015International audienceThe Web currently stores two types of content. These contents include linked data from the semantic Web and user contributions from the social Web. Our aim is to represent simplified aspects of these contents within a unified topological model and to harvest the benefits of integrating both content types in order to prompt collective learning and knowledge discovery. In particular, we wish to capture the phenomenon of Serendipity (i.e., incidental learning) using a subjective knowledge representation formalism, in which several " viewpoints " are individually interpretable from a knowledge graph. We prove our own Viewpoints approach by evidencing the collective learning capacity enabled by our approach. To that effect, we build a simulation that disseminates knowledge with linked data and user contributions, similar to the way the Web is formed. Using a behavioral model configured to represent various Web navigation strategies, we seek to optimize the distribution of preference systems. Our results outline the most appropriate strategies for incidental learning, bringing us closer to understanding and modeling the processes involved in Serendipity. An implementation of the Viewpoints formalism kernel is available. The underlying Viewpoints model allows us to abstract and generalize our current proof of concept for the indexing of any type of data set

THE INFLUENCE OF AFFECT ON PRODUCT EVALUATIONS AND ENDURING CONSUMPTION ENJOYMENT

This dissertation consists of two essays on the influence of affect on consumer intentions and behavioral responses. In the first essay, the influence of negative affect on consumer satiation is investigated. In the second essay, the influence of conceptual fluency, a positive affective response of “feeling right” during advertising evaluations, evoked by the structural properties of memory networks, is identified. In the first essay, how anticipated consumption variety influences consumers’ affective responses to slow satiation in the present is investigated. Prior research has focused on how cognitive appraisals of present variety influence consumers’ satiation rates. However, in addition to cognitively attending to the present, consumers also generate affective information regarding future consumption events (e.g. thinking about dessert while eating an entrée). Results indicate that more anticipated consumption variety reduces the amount of negative affect consumers experience during recurrent consumption, which is found to extend consumers’ present consumption enjoyment (reduced satiation rates). Further, the moderating roles of vice and virtue product perceptions and consumer emotional intelligence are also investigated, providing additional evidence of the proposed affective process mechanism while identifying boundary conditions for the effect. In the second essay, how the structural nature of semantic memory can produce affective responses, in the form of conceptual fluency, to influence consumers’ product behavioral intentions is investigated. Memory activations, generated by key words in advertising, can provide a temporary boost to the perceived desirability of a given product. However, memories are not activated in isolation. Rather, an entire network of interrelated concepts is activated along with the focal memory through various learned associations. Despite a great deal of knowledge detailing the phenomena of memory spreading activations, research has primarily focused on which memories are connected to each other, rather than on how activated memories are connected to their surrounding networks. This essay identifies consumers’ responses to the betweenness centrality (e.g. providing mediated access to other concepts in memory via the shortest path) of a focal word in advertising, rather than the activation of specific associations, as critical for advertising success

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad

The Semantic Web as a Platform Against Risk and Uncertainty in Agriculture

In this article, we discuss existing literature on DSS in agriculture, on DSS that use data available in the Semantic Web, and on Semantic Web initiatives focusing on agriculture information. Our goal is to assess the readiness of the Semantic Web as a platform to empower DSS that can keep risk and uncertainty in agriculture under control. Key agricultural activities targeted by DSS reported in literature are nutrient management, insect and pest management, land use and planning, environmental change and forecasting, and water and drought management. The most relevant use of Semantic Web in DSS, is in data analysis, as a means of making DSS more intelligent. There are initiatives to produce vocabularies and semantic repositories in the domain of agriculture. However, data and models are still isolated in specific domain repositories, and interoperability is still weak.IFIP Advances in Information and Communication Technology, vol. 506.Laboratorio de Investigación y Formación en Informática Avanzad