AN APPROACH FOR ANALYZING THE EFFECTS OF RISKS ON BUSINESS PROCESSES USING SEMANTIC ANNOTATIONS

The management of risks has gained a lot of attention in the last years. Among the current challenges in this domain are the integration of risk management in the strategic planning and performance management across business units and organizational structures, the assessment of a company’s risk bearing capacity, and the improvement of the methods of risk measurement. In order to support the elicitation of risks in business processes, measure their impact on a company’s return and provide reports for regulatory authorities, it can be reverted to technology-oriented knowledge management. In this context we propose an approach that uses semantically annotated models to represent the influence of risks on business activities based on the concepts provided by a risk knowledge base. By transferring the annotated model information to the knowledge base, inference rules can be applied to analyze the effects of risks on the business processes during subsequent capacity simulations. For a first evaluation the approach has been implemented using the ADOxx meta modeling platform, the Protégé ontology management toolkit and the Jess rule engine. Finally, the use of the implementation is shortly illustrated by reverting to a sample business process from the domain of banking

Ontology creation for wireless capsule endoscopy videos

In this paper we study multimedia ontology for Wireless Capsule Endoscopy (WCE) videos by enhancing its existing data structure. The ‘wireless capsule’ is a tiny disposable video camera that transmits 2 ~ 3 frames per second for a period of 8 ~ 11 hours. There are open problems in WCE, such as bleeding detection, as it is hard to identify accurately, using low-level features, i.e., color values. In addition, the physicians have to examine the videos continuously for two hours or more, which becomes restrictive. There have been research attempts to reduce this review time. However, they suffer from low accuracy and sensitivity, and do not process WCE videos with an efficient information data structure. To address this problem, we propose a new data structure named ‘multimedia ontology for WCE videos’ formed by combining medical and multimedia domain knowledge. Ontology represents a structure to describe the concepts and relationships in a specific domain with relevant data and its terminology. We define two types of ontology, i.e., generic and specific ontology. Generic ontology represents the broad concepts in WCE videos, such as medical terms, anatomic information, video format, etc., while specific ontology is a data-driven one including color, location, and region of images. The process of creating multimedia ontology consists of three steps: (1)collection of raw data from WCE videos, such as video data format, feature values, meta-data information and anomalies, (2) classification of the raw data into concepts including generic and specific ontology, and (3) identification of relationship between two concepts such as ‘Is-A’, ‘Part-Of’, and ‘Has-A’. This WCE Ontology structure can be used to better address the open problems by providing 'relevant area focus' from the formed structure and can also be extended to other problems like detection of lesions and polyps

Metadata behind the Interoperability of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability

Интеллектуальная ГИС в системах мониторинга

The development of Intelligent Geographic Information Systems (IGIS) for monitoring systems in the broad sense including urban and regional applications is considered. The paper is focused on the development of the IGIS method based on a Service Oriented Architecture (SOA) concept.Рассматривается развитие интеллектуальных геоинформационных систем для различных систем мониторинга. Статья представляет метод построения интеллектуальных геоинформационных систем, основанный на SOA концепции

Aplicação de ontologia e sistema especialista para diagnóstico de falhas em transformadores de potência

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Neste trabalho foi desenvolvido o protótipo de um Sistema Especialista (SE) que tem como objetivo principal auxiliar os especialistas em transformadores de potência, de uma forma ágil e confiável, na emissão de diagnósticos de falhas incipientes (defeitos) nos transformadores principais dos geradores da Usina Hidrelétrica da Itaipu Binacional. Este protótipo foi desenvolvido utilizando o arcabouço Jess, e a base de conhecimento sobre transformadores de potência foi classificada e modelada na forma de ontologia utilizando o Protégé-2000. Nesta proposta, a integração do Jess e o Protégé possibilitou a transferência dos dados obtidos nos ensaios de cromatografía de gases dissolvidos no óleo mineral isolante e outras informações armazenadas na base de conhecimento, utilizadas pelo SE para a geração dos diagnósticos correspondentes. Os testes realizados com os protótipos demonstraram a eficiência da metodologia, validando a proposta

О распознавании ситуации на основе технологии искусственного интеллекта

This paper considers the situation awareness technique based on artificial intelligence technology. Certain meta–class of situation awareness problems comprising three major classes like environment and two moving objects. In the presented study the situation awareness (SA) is considered as some vector whose parameters contain information about the interrelationship and state of at least three separate classes at a given time instant. Over the SA set the function is being formed that allows to identify SA based on measurements of three classes’ parameters (properties) taken in the space and time. Mechanism of defining the inherence function and SA repository (set) forming is realized through the inference algorithm Rete and immunocomputing method.В настоящей статье рассматривается методика распознавания ситуации, основанная на технологии искусственного интеллекта. В представленном исследовании ситуация рассматривается как некоторый вектор, параметры которого содержат информацию о взаимосвязи и состоянии отдельных классов в определенный момент времени. На множестве ситуаций определяется функция, позволяющая идентифицировать ситуацию на основе измерений свойств (параметров) классов, взятых в пространстве и времени. Определение функции принадлежности и формирование репозитория (множества) выполняется с использованием алгоритма вывода Rete и метода иммунокомпьютинга

BIM : new rules of measurement ontology for construction cost estimation

For generations, the process of cost estimation has been manual, time-consuming and errorprone. Emerging Building Information Modelling (BIM) can exploit standard measurement methods to automate cost estimation process and improve inaccuracies. Structuring standard measurement methods in an ontologically and machine readable format for a BIM software can greatly facilitate the process of improving inaccuracies in cost estimation. This study explores the development of an ontology based on New Rules of Measurement (NRM) for cost estimation during the tendering stages. The methodology adopted is Methontology, one of the most widely used ontology engineering methodologies. To ensure the ontology is fit for purpose, cost estimation experts are employed to check the semantics, descriptive logicbased reasoners are used to syntactically check the ontology and a leading 4D BIM modelling software is used on a case study building to test/validate the proposed ontology

GQ-BPAOntoSOA: A goal- and object- based semantic framework for deriving software services from an organisation’s goals and riva business process architecture

Understanding a business organisation is a primary activity that is required for deriving service-oriented systems that assist in carrying out the business activities of an organisation. Business IT alignment is one of the hot topics that concerns with aligning business needs and system needs in order to keep a business organisation competitive in a market. One example in this area is the BPAOntoSOA framework that aligned business process architecture and the service-oriented model of computing. The BPAOntoSOA framework is a semantically enriched framework for deriving service oriented architecture candidate software services from a Riva-based business process architecture. The BPAOntoSOA framework was recently proposed in order to align the candidate software services to the business processes presented in a Riva business process architecture. The activities of the BPAOntoSOA framework are structured into two-semantic-based layers that are formed in a top-down manner. The top layer, the BPAOnt ontology instantiation layer, concerned with conceptualising the Riva business process architecture and the associated business process models. The bottom layer, which is the software service identification layer, concerned with the semantic identification of the service-oriented architecture candidate software services and their associated capabilities. In this layer, RPA clusters were used to describe the derived candidate software service. Ontologies were used in order to support addressing the semantic representation. However, the BPAOntoSOA framework has two limitations. First, the derived candidate software services are identified without considering the business goals. Second, the desired quality of service requirements that constrain the functionality of the software services are absent. This research is concerned with resolving these two limitations within the BPAOntoSOA framework. In this research, the original BPAOntoSOA framework has been extended into the GQ-BPAOntoSOA framework. A new semantic-based layer has been added into the two original layers. The new layer is concerned with conceptualising the goal- and quality- oriented models in order to address their absence in the original BPAOntoSOA framework. The new layer is called the GQOnt ontology instantiation layer. This extension has highlighted the need for aligning the models within the original BPAOnt intonation layer with the ones in the new layer. This is because the BPAOnt was the base for the identification of the candidate software services and capabilities. Therefore, a novel alignment approach has been proposed in order to address this need. Also, the original service identification approach is refined in order to adapt with the integration of goals and quality requirements.The GQ-BPAOntoSOA framework, which is a goal-based and quality-linked extended BPAOntoSOA framework, has been evaluated using the Cancer Care Registration process. This is the same case study used in the evaluation of the BPAOntoSOA framework. And this is required in order to investigate the implication of integrating goals and quality requirements into the pre-existing BPAOntoSOA framework-driven candidate software services. This has shown that: (1) the GQOnt ontology does not only contribute to the extension of the BPAOntoSOA framework, yet it also contributes to providing a semantic representation of a business strategy view for an organisation. The GQOnt ontology acts as an independent repository of knowledge in order to have an early agreement between stakeholders with regard to business goals and quality requirements. The semantic representation could be reused for different purposes with respect to the needs. (2) the alignment approach has bridged the gap between goal-oriented models and Riva-based business process architectures. (3) the Riva business process architecture modelling method and business process models have been enriched with the integration of goals and quality requirements in order to provide a rich representation of business process architecture and process models that reflect an important information for the given organisation. (4) The service identification approach used in the original BPAOntoSOA framework has been enriched with goals and quality requirements. This has affected the identification of candidate software services (clusters) and their capabilities. Also, the derived candidate software services have conformed to service-oriented architecture principles. Accordingly, This research has bridged the gap between the BPAOntoSOA framework and the business goals and quality requirements. This is anticipated to lead to highly consistent, correct and complete software service specifications

Knowledge-based Methods for Integrating Carbon Footprint Prediction Techniques into New Product Designs and Engineering Changes.

This dissertation presents research focusing on the development of knowledge-based techniques of assessing the carbon footprint during new product creation. This research aims to transform the current time-consuming, off-line and reactive approach into an integrated proactive approach that relies on using fast estimates of sustainability generated from past computations on similar products. The developed methods address multiple challenges by leveraging the latest advancements in open standards and software capabilities from machine learning and data mining to support integration and early decision-making using generic knowledge of the product development field. Life-Cycle Assessment (LCA)-based carbon footprint calculation typically starts by analyzing the product functions. However, the lack of a semantically correct formal representation of product functions is a barrier to their effective capture and reuse. We first identified the advanced semantics that must be captured to ensure appropriate usability for reasoning with product functions. We captured them into a Function Semantics Representation that relies on the Semantic Web Rule Language, a proposed Semantic Web standard, to overcome limitations posed due to the commonly used Web Ontology Language. Several products are developed as Engineering Changes (ECs) of previous products but there is not enough data to predict the carbon footprint available before their implementation. In order to use past EC knowledge to predict for this purpose, we proposed an approach to compute similarity between ECs that overcame the challenge of the hierarchical nature of product knowledge by integrating an approach inspired from research in psychology with semantics specific to product development. We embedded this into a parallelized Ant-Colony based clustering algorithm for faster retrieval of a group of similar ECs. We are not aware of approaches to predict the carbon footprint of an EC or a proposed design right after the proposal. In order to reuse carbon footprint information from past designs and engineering changes, key parameters were determined to represent lifecycle attributes. The carbon footprint is predicted through a surrogate LCA technique developed using case-based reasoning and boosted-learning.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78846/1/scyang_1.pd

Mobile sensor networks for environmental monitoring

Vulnerability to natural disasters and the human pressure on natural resources have increased the need for environmental monitoring. Proper decisions, based on real-time information gathered from the environment, are critical to protecting human lives and natural resources. To this end, mobile sensor networks, such as wireless sensor networks, are promising sensing systems for flexible and autonomous gathering of such information. Mobile sensor networks consist of geographically deployed sensors very close to a phenomenon of interest. The sensors are autonomous, self-configured, small, lightweight and low powered, and they become mobile when they are attached to mobile objects such as robots, people or bikes. Research on mobile sensor networks has focused primarily on using sensor mobility to reduce the main sensor network limitations in terms of network topology, connectivity and energy conservation. However, how sensor mobility could improve environmental monitoring still remains largely unexplored. Addressing this requires the consideration of two main mobility aspects: sampling and mobility constraints. Sampling is about where mobile sensors should be moved, while mobility constraints are about how such movements should be handled, considering the context in which the monitoring is carried out. This thesis explores approaches for sensor mobility within a wireless sensor network for use in environmental monitoring. To achieve this goal, four sub-objectives were defined: Explore the use of metadata to describe the dynamic status of sensor networks. Develop a mobility constraint model to infer mobile sensor behaviour. Develop a method to adapt spatial sampling using mobile, constrained sensors. Extend the developed adaptive sampling method to monitoring highly dynamic environmental phenomena. Chapter 2 explores the use of metadata to describe the dynamic status of sensor networks. A context model was proposed to describe the general situation in which a sensor network is monitoring. The model consists of four types of contexts: sensor, network, sensing and organisation, where each of the contexts describes the sensor network from a different perspective. Metadata, which are descriptors of observed data, sensor configurations and functionalities, are used as parameters to describe what is happening in the different contexts. The results reveal that metadata are suitable for describing sensor network status within different contexts and reporting the status back to other components, systems or users. Chapter 3 develops a model which describes mobility constraints for inferring mobile sensor behaviour. The proposed mobility constraint model consists of three components: first, the context typology proposed in Chapter 2 to describe mobility constraints within the different contexts; second, a context graph, modelled as a Bayesian network, to encode dependencies of mobility constraints within the same or different contexts, as well as among mobility constraints and sensor behaviour; and third, contextual rules to encode how dependent mobility constraints are expected to constrain sensor behaviour. Metadata values for the monitored phenomenon and sensor properties are used to feed the context graph. They are propagated through the graph structure, and the contextual rules are used to infer the most suitable behaviour. The model was used to simulate the behaviour of a mobile sensor network to obtain a suitable spatial coverage in low and high fire risk scenarios. It was shown that the mobility constraint model successfully inferred behaviour, such as sleeping sensors, moving sensors and deploying more sensors to enhance spatial coverage. Chapter 4 develops a spatial sampling strategy for use with mobile, constrained sensors according to the expected value of information (EVoI) and mobility constraints. EVoI allows decisions to be made about the location to observe. It minimises the expected costs of wrong predictions about a phenomenon using a spatially aggregated EVoI criterion. Mobility constraints allow decisions to be made about which sensor to move. A cost-distance criterion is used to minimise unwanted effects of sensor mobility on the sensor network itself, such as energy depletion. The method was assessed by comparing it with a random selection of sample locations combined with sensor selection based on a minimum Euclidian distance criterion. The results demonstrate that EVoI enables selection of the most informative locations, while mobility constraints provide the needed context for sensor selection. Chapter 5 extends the method developed in Chapter 4 for the case of highly dynamic phenomena. It develops a method for deciding when and where to sample a dynamic phenomenon using mobile sensors. The optimisation criterion is to maximise the EVoI from a new sensor deployment at each time step. The method was demonstrated in a scenario in which a simulated fire in a chemical factory released polluted smoke into the open air. The plume varied in space and time because of variations in atmospheric conditions and could be only partially predicted by a deterministic dispersion model. In-situ observations acquired by mobile sensors were considered to improve predictions. A comparison with random sensor movements and the previous sensor deployment without performing sensor movements shows that the optimised sensor mobility successfully reduced risk caused by poor model predictions. Chapter 6 synthesises the main findings and presents my reflections on the implications of such findings. Mobile sensors for environmental monitoring are relevant to improving monitoring by selecting sampling locations that deliver the information that most improves the quality of decisions for protecting human lives and natural resources. Mobility constraints are relevant to managing sensor mobility within sampling strategies. The traditional consideration of mobility constraints within the field of computer sciences mainly leads to sensor self-protection rather than to the protection of human beings and natural resources. By contrast, when used for environmental monitoring, mobile sensors should above all improve monitoring performance, even thought this might produce negative effects on coverage, connectivity or energy consumption. Thus, mobility constraints are useful for reducing such negative effects without constraining the sampling strategy. Although sensor networks are now a mature technology, they are not yet widely used by surveyors and environmental scientists. The operational use of sensor networks in geo-information and environmental sciences therefore needs to be further stimulated. Although this thesis focuses on wireless sensor network, other types of informal sensor networks could be also relevant for environmental monitoring, such as smart phones, volunteer citizens and sensor web. Finally, the following recommendations are given for further research: extend the sampling strategy for dynamic phenomena to take account of mobility constraints; develop sampling strategies that take a decentralised approach; focus on mobility constraints related to connectivity and data transmission; elicit expert knowledge to reveal preferences for sensor mobility under mobility constraints within different types of environmental applications; and validate the proposed strategies in operational implementations. </p