An ontology based approach to data surveillance

Nowadays the terrorist threat took proportions that concern governments and the national security organizations, all over the world. A successful terrorist incident usually brings catastrophic results. However if a terrorist attack can be predicted and characterized, it may be possible to organize a proper intervention in order to avoid it or to reduce its impact. The management of information is becoming an important issue in the domain of security information systems. The information access and association, analysis and assessment, and finally exploitation have become the focus for all security information services and governments. Current surveillance approaches are not very efficient leading innocent citizen to the confrontation of law enforcement services. One reason for this, result from the difficulties of the current system to extract knowledge or concepts abstracted from massive databases of information. Knowledge based methods, such as ontologies can integrate data surveillance, and enable a proper data analyse improving the performance of the security information services. This paper intends to present a perspective about the use of ontologies in the context of data surveillance, and present its importance in the current security services domain.(undefined

Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described

A multilevel approach to big data analysis using analytic tools and actor network theory

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness. Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis. Method: In the study, the qualitative methods were employed from the interpretivist approach perspective. Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks. Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness

Disease surveillance systems

Recent advances in information and communication technologies have made the development and operation of complex disease surveillance systems technically feasible, and many systems have been proposed to interpret diverse data sources for health-related signals. Implementing these systems for daily use and efficiently interpreting their output, however, remains a technical challenge. This thesis presents a method for understanding disease surveillance systems structurally, examines four existing systems, and discusses the implications of developing such systems. The discussion is followed by two papers. The first paper describes the design of a national outbreak detection system for daily disease surveillance. It is currently in use at the Swedish Institute for Communicable Disease Control. The source code has been licenced under GNU v3 and is freely available. The second paper discusses methodological issues in computational epidemiology, and presents the lessons learned from a software development project in which a spatially explicit micro-meso-macro model for the entire Swedish population was built based on registry data

Модели контекстно-управляемых систем поддержки принятия решений в динамических структурированных областях

Some existing approaches to representation and organization of contexts in different information systems are analyzed. A two-level context management model for intelligent decision support in dynamic structured domains is proposed. A model for description of information resources of an open information environment is given. A technology model of context-aware decision support system is designed.Анализируются известные подходы к описанию и формированию контекста в различных информационных средах. Предлагается двухуровневая модель управления контекстом для организации интеллектуальной поддержки принятия решений в динамических структурированных областях. Приводится модель описания ресурсов открытой информационной среды для моделирования текущей ситуации. Определена технологическая модель контекстно-управляемой системы интеллектуальной поддержки принятия решений