Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition

Search in the eye of the beholder: using the personal social dataset and ontology-guided input to improve web search efficiency

Proceedings of: Latin American Web Conference 2007 (LA-WEB 2007), 31 October-2 November 2007, Santiago (Chile)Among the challenges of searching the vast information source the Web has become, improving Web search efficiency by different strategies using semantics and the user generated data from Web 2.0 applications remains a promising and interesting approach. In this paper, we present the Personal Social Dataset and Ontology-guided Input strategies and couple them together, providing a proof of concept implementation.Publicad

AN IMPERATIVE FOCUS ON SEMANTIC WEB PRINCIPLES, LOGICS AND ITS APPLICATION

ABSTRAC

Geographic Information Systems for Real-Time Environmental Sensing at Multiple Scales

The purpose of this investigation was to design, implement, and apply a real-time geographic information system for data intensive water resource research and management. The research presented is part of an ongoing, interdisciplinary research program supporting the development of the Intelligent River® observation instrument. The objectives of this research were to 1) design and describe software architecture for a streaming environmental sensing information system, 2) implement and evaluate the proposed information system, and 3) apply the information system for monitoring, analysis, and visualization of an urban stormwater improvement project located in the City of Aiken, South Carolina, USA. This research contributes to the fields of software architecture and urban ecohydrology. The first contribution is a formal architectural description of a streaming environmental sensing information system. This research demonstrates the operation of the information system and provides a reference point for future software implementations. Contributions to urban ecohydrology are in three areas. First, a characterization of soil properties for the study region of the City of Aiken, SC is provided. The analysis includes an evaluation of spatial structure for soil hydrologic properties. Findings indicate no detectable structure at the scales explored during the study. The second contribution to ecohydrology comes from a long-term, continuous monitoring program for bioinfiltration basin structures located in the study area. Results include an analysis of soil moisture dynamics based on data collected at multiple depths with high spatial and temporal resolution. A novel metric is introduced to evaluate the long-term performance of bioinfiltration basin structures based on soil moisture observation data. Findings indicate a decrease in basin performance over time for the monitored sites. The third contribution to the field of ecohydrology is the development and application of a spatially and temporally explicit rainfall infiltration and excess model. The model enables the simulation and visualization of bioinfiltration basin hydrologic response at within-catchment scales. The model is validated against observed soil moisture data. Results include visualizations and stormwater volume calculations based on measured versus predicted bioinfiltration basin performance over time

Staging Transformations for Multimodal Web Interaction Management

Multimodal interfaces are becoming increasingly ubiquitous with the advent of mobile devices, accessibility considerations, and novel software technologies that combine diverse interaction media. In addition to improving access and delivery capabilities, such interfaces enable flexible and personalized dialogs with websites, much like a conversation between humans. In this paper, we present a software framework for multimodal web interaction management that supports mixed-initiative dialogs between users and websites. A mixed-initiative dialog is one where the user and the website take turns changing the flow of interaction. The framework supports the functional specification and realization of such dialogs using staging transformations -- a theory for representing and reasoning about dialogs based on partial input. It supports multiple interaction interfaces, and offers sessioning, caching, and co-ordination functions through the use of an interaction manager. Two case studies are presented to illustrate the promise of this approach.Comment: Describes framework and software architecture for multimodal web interaction managemen

An IoT architecture for decision support system in precision livestock

Sustainable animal production is a primary goal of technological development in the livestock industry. However, it is crucial to master the livestock environment due to the susceptibility of animals to variables such as temperature and humidity, which can cause illness, production losses, and discomfort. Thus, livestock production systems require monitoring, reasoning, and mitigating unwanted conditions with automated actions. The principal contribution of this study is the introduction of a self-adaptive architecture named e-Livestock to handle animal production decisions. Two case studies were conducted involving a system derived from the e-Livestock architecture, encompassing a Compost Barn production system - an environment and technology where bovine milk production occurs. The outcomes demonstrate the effectiveness of e-Livestock in three key aspects: (i) abstraction of disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and their incorporation into a single architecture specific to the livestock domain, (ii) support for the reuse and derivation of an adaptive self-architecture to support the engineering of a decision support system for the livestock subdomain, and (iii) support for empirical studies in a real smart farm to facilitate future technology transfer to the industry. Therefore, our research’s main contribution is developing an architecture combining machine learning techniques and ontology to support more complex decisions when considering a large volume of data generated on farms. The results revealed that the e-Livestock architecture could support monitoring, reasoning, forecasting, and automated actions in a milk production/Compost Barn environment.Na indústria pecuária, a produção animal sustentável é o principal objetivo do desenvolvimento tecnológico. Porém, é fundamental manter boas condições no ambiente devido à suscetibilidade dos animais a variáveis como temperatura e umidade, que podem causar doenças, perdas de produção e desconforto. Assim, os sistemas de produção pecuária requerem monitoramento, controle e mitigação das condições indesejadas através de ações automatizadas. A principal contribuição deste estudo é a introdução de uma arquitetura auto-adaptativa denominada e-Livestock para apoiar as decisões relacionadas à produção animal. Foram conduzidos dois estudos de caso, envolvendo a arquitetura e-Livestock, que foi utilizada no sistema de produção Compost Barn - ambiente e tecnologia onde ocorre a produção de gado leiteiro. Os resultados demonstraram a utilidade do e-Livestock para avaliar três aspectos principais: (i) abstração de tecnologias disruptivas baseadas em Internet das Coisas (IoT) e Inteligência Artificial, e sua incorporação em uma arquitetura única, específica para o domínio da pecuária, (ii) suporte para a reutilização e derivação de uma arquitetura auto-adaptativa para apoiar o desenvolvimento de uma aplicação de apoio à decisão para o subdomínio da pecuária e (iii) suporte para estudos empíricos em uma fazenda inteligente real para facilitar a transferência de tecnologia para a indústria. Portanto, a principal contribuição dessa pesquisa é o desenvolvimento de uma arquitetura combinando técnicas de machine learning e ontologia para apoiar decisões mais complexas ao considerar um grande volume de dados gerados nas fazendas. Os resultados revelaram que a arquitetura e-Livestock pode apoiar monitoramento, controle, previsão e ações automatizadas em um ambiente de produção de leite/Compost Barn.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Interoperability between heterogeneous and distributed biodiversity data sources in structured data networks

The extensive capturing of biodiversity data and storing them in heterogeneous information systems that are accessible on the internet across the globe has created many interoperability problems. One is that the data providers are independent of others and they can run systems which were developed on different platforms at different times using different software products to respond to different needs of information. A second arises from the data modelling used to convert the real world data into a computerised data structure which is not conditioned by a universal standard. Most importantly the need for interoperation between these disparate data sources is to get accurate and useful information for further analysis and decision making. The software representation of a universal or a single data definition structure for depicting a biodiversity entity is ideal. But this is not necessarily possible when integrating data from independently developed systems. The different perspectives of the real-world entity when being modelled by independent teams will result in the use of different terminologies, definition and representation of attributes and operations for the same real-world entity. The research in this thesis is concerned with designing and developing an interoperable flexible framework that allows data integration between various distributed and heterogeneous biodiversity data sources that adopt XML standards for data communication. In particular the problems of scope and representational heterogeneity among the various XML data schemas are addressed. To demonstrate this research a prototype system called BUFFIE (Biodiversity Users‘ Flexible Framework for Interoperability Experiments) was designed using a hybrid of Object-oriented and Functional design principles. This system accepts the query information from the user in a web form, and designs an XML query. This request query is enriched and is made more specific to data providers using the data provider information stored in a repository. These requests are sent to the different heterogeneous data resources across the internet using HTTP protocol. The responses received are in varied XML formats which are integrated using knowledge mapping rules defined in XSLT & XML. The XML mappings are derived from a biodiversity domain knowledgebase defined for schema mappings of different data exchange protocols. The integrated results are presented to users or client programs to do further analysis. The main results of this thesis are: (1) A framework model that allows interoperation between the heterogeneous data source systems. (2) Enriched querying improves the accuracy of responses by finding the correct information existing among autonomous, distributed and heterogeneous data resources. (3) A methodology that provides a foundation for extensibility as any new network data standards in XML can be added to the existing protocols. The presented approach shows that (1) semi automated mapping and integration of datasets from the heterogeneous and autonomous data providers is feasible. (2) Query enriching and integrating the data allows the querying and harvesting of useful data from various data providers for helpful analysis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo