Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition

The quality of decisions made in business and government relates directly to the quality of the information used to formulate the decision. This information may be retrieved from an organization's knowledge base (Intranet) or from the World Wide Web. Intelligence services Intranet held information can be efficiently manipulated by technologies based upon either semantics such as ontologies, or statistics such as meaning-based computing. These technologies require complex processing of large amount of textual information. However, they cannot currently be effectively applied to Web-based search due to various obstacles, such as lack of semantic tagging. A new approach proposed in this paper supports Web-based search for intelligence information utilizing evidence-based natural language processing (NLP). This approach combines traditional NLP methods for filtering of Web-search results, Grounded Theory to test the completeness of the evidence, and Evidential Analysis to test the quality of gathered information. The enriched information derived from the Web-search will be transferred to the intelligence services knowledge base for handling by an effective Intranet search system thus increasing substantially the information for intelligence analysis. The paper will show that the quality of retrieved information is significantly enhanced by the discovery of previously unknown facts derived from known facts

A Windows Phone 7 Oriented Secure Architecture for Business Intelligence Mobile Applications

This paper present and implement a Windows Phone 7 Oriented Secure Architecture for Business Intelligence Mobile Application. In the developing process is used a Windows Phone 7 application that interact with a WCF Web Service and a database. The types of Business Intelligence Mobile Applications are presented. The Windows mobile devices security and restrictions are presented. The namespaces and security algorithms used in .NET Compact Framework for assuring the application security are presented. The proposed architecture is showed underlying the flows between the application and the web service.Security, Secure Architecture, Mobile Applications, Business Intelligence, Web Service

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

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Competitive Intelligence and Internet Sources

In the Knowledge Age to maintain profitability and in some cases to remain in the market, companies must focus their actions in activities such as collecting, filtering, and disseminating information about market, about competitors and their actions. Those are part of Competitive Intelligence (CI) concept. In digital age, most of the information needed for CI projects is available on the web. This paper focuses on this field and presents a mix of directions that companies need to take into consideration in their CI projects in order to achieve the goals.competitive intelligence, web mining, information

AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

Territorial intelligence : The contribution Web 3.0 technologies in practice the territorial intelligence

The term "Territorial intelligence 3.0" refers to the usage of the web 3.0 technologies, such as the mobile web, web applications and the semantic web, in the process of Territorial intelligence. The territorial intelligence represents an offensive and a defensive attitude with all implications in terms of the information generated on global markets. The concept, the origin and the foundation of the above-mentioned term emerging in two distinct communities, one brings together practitioners of territorial intelligence developed around the institutional field, it is the case of top-down territorial intelligence. And the other community, brings together theorists searches in the multidisciplinary academic field, it comes from research on the economy, geopolitics, knowledge management and the discipline of information and communication technology sciences, this is the case of bottom-up territorial intelligence. The Web 3.0 technologies, combine, on the one hand, web 2.0 technologies; the community Web (social networks: Linked in, Twitter, Facebook, etc.) and the collaborative Web (Wikipedia and Weblogs) (Quoniam & Lucien, 2009), and, on the other hand, smartphones, the internet of objects (Internet of Things), cloud computing technology and big data. “Web 3.0 is the combination of smart phones, social networks, Web 2.0, cloud computing and emerging business models as explained above” (Russell et al., 2016), web 3.0 practitioners consider that much of the world's information being correlated and frankly opening up to the general population, combine between these two concepts: Generating the management strategic territorial information founded on Web 3.0 and working in favor of the territory. There are generally two types of territorial intelligence 3.0. The first one is the top-Down Territorial Intelligence 3.0, it’s the evolution of the national policy of competitive Intelligence 3.0 at the local level, but the term “competitive Intelligence 3.0” has been subjected to the same web evolution. And the second one is the bottom up territorial intelligence 3.0, it is manifested by the contribution of the actors of the territory in the process of local development through the technology of web 3.0. The goal of our research is to propose a conceptual model base on a theoretical in the context of territorial intelligence in a digital sphere by web 3.0 technology. This model studied the process the contribute Web 3.0 technology to the practice of territorial intelligence and to meet them in.The term "Territorial intelligence 3.0" refers to the usage of the web 3.0 technologies, such as the mobile web, web applications and the semantic web, in the process of Territorial intelligence. The territorial intelligence represents an offensive and a defensive attitude with all implications in terms of the information generated on global markets. The concept, the origin and the foundation of the above-mentioned term emerging in two distinct communities, one brings together practitioners of territorial intelligence developed around the institutional field, it is the case of top-down territorial intelligence. And the other community, brings together theorists searches in the multidisciplinary academic field, it comes from research on the economy, geopolitics, knowledge management and the discipline of information and communication technology sciences, this is the case of bottom-up territorial intelligence. The Web 3.0 technologies, combine, on the one hand, web 2.0 technologies; the community Web (social networks: Linked in, Twitter, Facebook, etc.) and the collaborative Web (Wikipedia and Weblogs) (Quoniam & Lucien, 2009), and, on the other hand, smartphones, the internet of objects (Internet of Things), cloud computing technology and big data. “Web 3.0 is the combination of smart phones, social networks, Web 2.0, cloud computing and emerging business models as explained above” (Russell et al., 2016), web 3.0 practitioners consider that much of the world's information being correlated and frankly opening up to the general population, combine between these two concepts: Generating the management strategic territorial information founded on Web 3.0 and working in favor of the territory. There are generally two types of territorial intelligence 3.0. The first one is the top-Down Territorial Intelligence 3.0, it’s the evolution of the national policy of competitive Intelligence 3.0 at the local level, but the term “competitive Intelligence 3.0” has been subjected to the same web evolution. And the second one is the bottom up territorial intelligence 3.0, it is manifested by the contribution of the actors of the territory in the process of local development through the technology of web 3.0. The goal of our research is to propose a conceptual model base on a theoretical in the context of territorial intelligence in a digital sphere by web 3.0 technology. This model studied the process the contribute Web 3.0 technology to the practice of territorial intelligence and to meet them in