Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

An enterprise modeling and integration framework based on knowledge discovery and data mining

This paper deals with the conceptual design and development of an enterprise modeling and integration framework using knowledge discovery and data mining. First, the paper briefly presents the background and current state-of-the-art of knowledge discovery in databases and data mining systems and projects. Next, enterprise knowledge engineering is dealt with. The paper suggests a novel approach of utilizing existing enterprise reference architectures, integration and modeling frameworks by the introduction of new enterprise views such as mining and knowledge views. An extension and a generic exploration of the information view that already exists within some enterprise models are also proposed. The Zachman Framework for Enterprise Architecture is also outlined versus the existing architectures and the proposed enterprise framework. The main contribution of this paper is the identification and definition of a common knowledge enterprise model which represents an original combination between the previous projects on enterprise architectures and the Object Management Group (OMG) models and standards. The identified common knowledge enterprise model has therefore been designed using the OMG's Model-Driven Architecture (MDA) and Common Warehouse MetaModel (CWM), and it also follows the RM-ODP (ISO/OSI). It has been partially implemented in Java(TM), Enterprise JavaBeans (EJB) and Corba/IDL. Finally, the advantages and limitations of the proposed enterprise model are outlined

A Proposal for Deploying Hybrid Knowledge Bases: the ADOxx-to-GraphDB Interoperability Case

Graph Database Management Systems brought data model abstractions closer to how humans are used to handle knowledge - i.e., driven by inferences across complex relationship networks rather than by encapsulating tuples under rigid schemata. Another discipline that commonly employs graph-like structures is diagrammatic Conceptual Modeling, where intuitive, graphical means of explicating knowledge are systematically studied and formalized. Considering the common ground of graph databases, the paper proposes an integration of OWL ontologies with diagrammatic representations as enabled by the ADOxx metamodeling platform. The proposal is based on the RDF-semantics variant of OWL and leads to a particular type of hybrid knowledge bases hosted, for proof-of-concept purposes, by the GraphDB system due to its inferencing capabilities. The approach aims for complementarity and integration, providing agile diagrammatic means of creating semantic networks that are amenable to ontology-based reasoning

A conceptual model of knowledge work productivity for software development process: Quality issues

Knowledge is considered as the main competitive asset of the organization.Work on the knowledge work productivity has barely begun, but the most important contribution that management needs to construct in the 21st century is not only to increase the productivity of knowledge work and knowledge workers in the new century.The quality of knowledge work productivity are becomes pivotal in the context of software development today.Software development is a knowledge-intensive activity and its success depends heavily on the developers’ knowledge and experience. A conceptual model will be proposed on a way describing organization to improve quality of knowledge work productivity. The methodology begins with a reviewing a theoretical foundation and expert review that provides the scientific basis for knowledge work productivity specifically for software development. A questionnaire will be constructing in order to investigate the relationship between factors of knowledge work and quality of productivity on knowledge work. The respondents are software developers from Small Manufacturing Enterprise(SME). The data will be analyzed using Structural Equation Modeling (SEM) to identify the significant direct relationship effect among the factors. The proposed model will be helpful for the software developers to understand the determinant factors for knowledge works productivity

Conceptual structures for modeling in CIM

The International Standards Organization (ISO) will release in 1993 the first version of the STEP standard, which is dedicated to the exchange of product model data, and is seen as the basis of the next generation of enterprise information modeling tools. Almost in the same time frame ANSI will release the Information Resource Dictionary System(IRDS) Conceptual Schema standard, which recommends the conceptual graphs (CGs) or other representation languages based on logic to be used for enterprise information modeling and integration. In this paper we develop the foundations for the utilization of conceptual structures (CS) in combination with EXPRESS and STEP Application Protocols in the field of Computer Integrated Manufacturing (CIM). The most important result described here is a mapping of EXPRESS into CGs. Around it we develop the architecture of a system able to analyze and translate some of the semantics of information models. Our overall strategy consists of representing the semantics of the language, including the informal meanings represented in the EXPRESS manual in plain English, in a systematic way in CS, and then use this block of knowledge, that can be processed by a machine, for the increasingly automatic analysis, translation and integration of enterprise information models. The work here described is one of the components of a prototype of a model management system under development at IBM, Kingston NY, coordinated by the CIM Architecture group

Knowledge Management in Higher Education: Effectiveness, Success factors, and Organisational Performance

In today business environment, Higher education institutions are facing a common challenge in the wake of rapid changes due to substantial drops in public funding for public colleges and universities, a larger number of calls for transparency, rapid expansion of the global business. To survive, organizations of higher education must improve their performance continually. Researchers reported that knowledge and effectively managing knowledge can help HEIs improve their performance by solving many of these problems and acquire and sustain competitive advantage. It is beneficial to explore the factors that impact the effective implementation of knowledge management within higher education institutions. These factors are organizational culture, and leadership styles. Additionally, it is essential to investigate the leadership style that best supports effective implementation of knowledge management. This study sought to examine the relationship between organizational culture (mission, adaptability, involvement, consistency), leadership styles (transformational and transactional), knowledge management effectiveness, and organizational performance. The study also analyzed the mediating role of organizational culture on the relationship between leadership styles and knowledge management effectiveness. Based on existing literature, eight hypotheses and a conceptual model were developed regarding the relationships of the five constructs: organizational culture, transformational and transformational leadership, knowledge management effectiveness and organizational performance. All constructs are measured by multi-items scales. For this study, organizational performance and knowledge management effectiveness were taken as dependents variables. Leadership styles of transformational and transactional and organizational culture were taken as independent variables. Organizational culture (mission, consistency, adaptability, and involvement) served as mediator variable. A questionnaire was used to collect data; this questionnaire was administered to 251 faculty and administrative leaders employed at 20 universities and colleges across the United States of America. Only 136 were entirely completed and deemed useful for the study. Structural equation modeling and Confirmatory Factor Analysis within SEM were adopted for data analysis. Results were presented using frequency distribution tables and graphs. Key findings suggested that organizational culture and transformational leadership impacted knowledge management effectiveness. But transactional leadership did not. Consequently, knowledge management effectiveness impacted organizational performance. While organizational culture mediated the effects of transformational leadership on knowledge management effectiveness, no mediating effect of organizational culture was found on the effect of transactional leadership on knowledge management effectiveness. Organizational culture has the largest positive impact on knowledge management effectiveness. These results may inform the successful implementation of KM practices, which in term improve the performance of higher educational institutions across the United States of America

Assessing the impact of physicians' social capital on decision making quality mediated by knowledge sharing in a virtual community of practice: an empirical quantitative analysis

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Purpose - Healthcare (HC) is a globally expensive investment, suffering from service quality, due to medical errors caused by physicians’ poor decisions making (DM). Current published literature: (1) encourages clinical DM research to reduce diagnostic errors and (2) stresses on the dearth of means for practitioners’ knowledge shared DM; this research focuses on knowledge sharing for improving medical DM quality through physicians’ social capital (SC) in a virtual community of practice (VCoP). Physicians join a virtual community (VC) to share clinical practice knowledge to aid medical DM. This study aims to assess the effect of physicians’ SC on medical DM and assess the mediating role of knowledge sharing quality, between physicians’ SC and medical DM quality since research lacks to investigate the impact of knowledge management (KM) tools in a HC context. VCoP is a KM tool and medical DM quality is a HC topic of this study. Design/methodology/approach – This positivist, quantitative research utilizes non-experimental survey to empirically assess its conceptual framework. After attaining an ethical approval, from Brunel Business School Research Ethics Committee, online survey was pre-tested and pilot tested for clarity and validity. 10 non-physician Ph.D. academics voluntarily participated during the survey’s pre-test phase. The survey was amendment for its pilot study phase; conducted in “plastic surgery yahoo group” VC. 31 physician VC members voluntarily participated. Again, the survey was amended and distributed for main data collection from 204 voluntary SurveyMonkey’s VC’s physician members. Findings – Data was analysed using SPSS 20 and LISREL 8.80 by means of confirmatory factor analysis and Structural Equation Modeling. Empirical findings supported this study’s four main hypotheses as well as supported this study’s initially proposed conceptual framework. Originality/value – This study customized the Honeycomb framework to establish a definition of professional physicians; HC VCs followed by identifying 51 VCs from social networking platforms like LinkedIn, Facebook, etc. This study also fulfilled its aim and hence proposed a structurally fit conceptual framework. Keywords –Virtual Community of Practice; Healthcare Knowledge Management; Confirmatory Factor Analysis; Structural Equation Modelin