Applying semiotic analysis to the design and modeling of distributed multimedia systems

Semiotics is the study of signs. Application of semiotics in information systems design is based on the notion that information systems are organizations within which agents deploy signs in the form of actions according to a set of norms. An analysis of the relationships among the agents, their actions and the norms would give a better specification of the system. Distributed multimedia systems (DMMS) could be viewed as a system consisted of many dynamic, self-controlled normative agents engaging in complex interaction and processing of multimedia information. This paper reports the work of applying the semiotic approach to the design and modeling of DMMS, with emphasis on using semantic analysis under the semiotic framework. A semantic model of DMMS describing various components and their ontological dependencies is presented, which then serves as a design model and implemented in a semantic database. Benefits of using the semantic database are discussed with reference to various design scenarios.

Modeling building semantics: providing feedback and sustainability

Even minor changes in user activity can bring about significant energy savings within built space. Many building performance assessment methods have been developed, however these often disregard the impact of user behavior (i.e. the social, cultural and organizational aspects of the building). Building users currently have limited means of determining how sustainable they are, in context of the specific building structure and/or when compared to other users performing similar activities, it is therefore easy for users to dismiss their energy use. To support sustainability, buildings must be able to monitor energy use, identify areas of potential change in the context of user activity and provide contextually relevant information to facilitate persuasion management. If the building is able to provide users with detailed information about how specific user activity that is wasteful, this should provide considerable motivation to implement positive change. This paper proposes using a dynamic and temporal semantic model, to populate information within a model of persuasion, to manage user change. By semantically mapping a building, and linking this to persuasion management we suggest that: i) building energy use can be monitored and analyzed over time; ii) persuasive management can be facilitated to move user activity towards sustainability.

Ontology modeling for generation of clinical pathways

Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP) as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM) is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM) is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the behavior will occur. To overcome this weakness, a semiotic approach to generation of clinical pathway is introduced. The CP generated from SAM together with norms will enrich the knowledge representation of the domain through ontology modeling, which allows the recognition of human responsibilities and obligations and more importantly, the ultimate power of decision making in exceptional circumstances.

Normative modeling for personalized clinical pathway using organizational semiotics methods

Clinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and reduce the length and cost of hospital stay. However, nowadays most of them are static and nonpersonalized. Our objective is to capture and represent clinical pathway using organizational semiotics method including Semantic Analysis which determines semantic units in clinical pathway, their relationship and their patterns of behavior, and Norm Analysis which extracts and specifies the norms that establish how and when these medical behaviors will occur. Finally, we propose a method to develop clinical pathway ontology based on the results of Semantic Analysis and Norm analysis. This approach will give a contribution to design personalized clinical pathway by defining a set of possible patterns of behavior and theClinical pathways are widely adopted by many large hospitals around the world in order to provide high-quality patient treatment and reduce the length and cost of hospital stay. However, nowadays most of them are static and nonpersonalized. Our objective is to capture and represent clinical pathway using organizational semiotics method including Semantic Analysis which determines semantic units in clinical pathway, their relationship and their patterns of behavior, and Norm Analysis which extracts and specifies the norms that establish how and when these medical behaviors will occur. Finally, we propose a method to develop clinical pathway ontology based on the results of Semantic Analysis and Norm analysis. This approach will give a contribution to design personalized clinical pathway by defining a set of possible patterns of behavior and the norms that govern the behavior based on patient’s condition.

Considering the Feasibility of Semantic Model Design in the Built-Environment

Building Information Modeling (BIM) is the process of structuring, capturing, creating, and managing a digital representation of physical and/or functional characteristics of a built space [1]. Current BIM has limited ability to represent dynamic semantics, social information, often failing to consider building activity, behavior and context; thus limiting integration with intelligent, built-environment management systems. Research, such as the development of Semantic Exchange Modules, and/or the linking of IFC with semantic web structures, demonstrates the need for building models to better support complex semantic functionality. To implement model semantics effectively, however, it is critical that model designers consider semantic information constructs. This paper discusses semantic models with relation to determining the most suitable information structure. We demonstrate how semantic rigidity can lead to significant long-term problems that can contribute to model failure. A sufficiently detailed feasibility study is advised to maximize the value from the semantic model. In addition we propose a set of questions, to be used during a model’s feasibility study, and guidelines to help assess the most suitable method for managing semantics in a built environment.

Hierarchical modeling of entangled polymers

We demonstrate that it is possible to link multi-chain molecular dynamics simulations with the tube model using a single chain slip-links model as a bridge. This hierarchical approach allows significant speed up of simulations, permitting us to span the time scales relevant for a comparison with the tube theory. Fitting the mean-square displacement of individual monomers in molecular dynamics simulations with the slip-spring model, we show that it is possible to predict the stress relaxation. Then, we analyze the stress relaxation from slip-spring simulations in the framework of the tube theory. In the absence of constraint release, we establish that the relaxation modulus can be decomposed as the sum of contributions from fast and longitudinal Rouse modes, and tube survival. Finally, we discuss some open questions regarding possible future directions that could be profitable in rendering the tube model quantitative, even for mildly entangled polymers

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

Automatically constructing the semantic web

The storage and processing capacity realised by computing has lead to an explosion of data retention. We now reach the point of information overload and must begin to use computers to process more complex information. In particular, the proposition of the Semantic Web has given structure to this problem, but has yet realised practically. The largest of its problems is that of ontology construction; without a suitable automatic method most will have to be encoded by hand. In this paper we discus the current methods for semi and fully automatic construction and their current shortcomings. In particular we pay attention the application of ontologies to products and the particle application of the ontologies.

Networks of contextualised ontologies for the Semantic Web

Currently many ontologies are available for addressing different domains. However, it is not always possible to deploy such ontologies to support collaborative working, so that their full potential can be exploited to implement intelligent cooperative applications capable of reasoning over a network of context-specific ontologies. The main problem arises from the fact that presently ontologies are created in an isolated way to address specific needs. However we foresee the need for a network of ontologies which will support the next generation of intelligent applications/devices, and, the vision of Ambient Intelligence. The main objective of this paper is to motivate the design of a networked ontology (Meta) model which formalises ways of connecting available ontologies so that they are easy to search, to characterise and to maintain. The aim is to make explicit the virtual and implicit network of ontologies serving the Semantic Web.

The impact of deformation strain on the formation of banded clouds in idealized modeling experiments

Experiments are performed using an idealized version of an operational forecast model to determine the impact on banded frontal clouds of the strength of deformational forcing, low-level baroclinicity, and model representation of convection. Line convection is initiated along the front, and slantwise bands extend from the top of the line-convection elements into the cold air. This banding is attributed primarily to M adjustment. The cross-frontal spreading of the cold pool generated by the line convection leads to further triggering of upright convection in the cold air that feeds into these slantwise bands. Secondary low-level bands form later in the simulations; these are attributed to the release of conditional symmetric instability. Enhanced deformation strain leads to earlier onset of convection and more coherent line convection. A stronger cold pool is generated, but its speed is reduced relative to that seen in experiments with weaker deformational strain, because of inhibition by the strain field. Enhanced low-level baroclinicity leads to the generation of more inertial instability by line convection (for a given capping height of convection), and consequently greater strength of the slantwise circulations formed by M adjustment. These conclusions are based on experiments without a convective-parametrization scheme. Experiments using the standard or a modified scheme for this model demonstrate known problems with the use of this scheme at the awkward 4 km grid length used in these simulations. Copyright © 2008 Royal Meteorological Society

Aerosol optical depths over North Africa: 2. Modeling and model validation

An operational dust forecasting model is developed by including the Met Office Hadley Centre climate model dust parameterization scheme, within a Met Office regional numerical weather prediction (NWP) model. The model includes parameterizations for dust uplift, dust transport, and dust deposition in six discrete size bins and provides diagnostics such as the aerosol optical depth. The results are compared against surface and satellite remote sensing measurements and against in situ measurements from the Facility for Atmospheric Airborne Measurements for a case study when a strong dust event was forecast. Comparisons are also performed against satellite and surface instrumentation for the entire month of August. The case study shows that this Saharan dust NWP model can provide very good guidance of dust events, as much as 42 h ahead. The analysis of monthly data suggests that the mean and variability in the dust model is also well represented.