The impact of knowledge management processes on organizational resilience: data mining as an instrument of measurement.

The aim of the research conducted for this thesis is to test the feasibility of using data mining (DM) to assess the relationship between and the impact of knowledge management (KM) on organizational resilience (OR). The emphasis currently placed on the value of intangible assets by private sector organizations and the recent increase in the use of data mining technologies are the key drivers in this evaluation of the use of data mining tools as an alternative to classical statistics when measuring intangibles. Data was collected using a questionnaire that was sent to the senior executives of a number of mid-sized companies located in the mid-west of the USA. Using Microsoft's SQL Server's Analytical Services (MSSAS) and the data provided by the respondents, five predictive models are built to test the suitability of the MSSAS' DM tool for assessing the relationships between and the impact of KM on OR. Of the five models constructed as part of this research, four classification models (two Naïve Bayes models, one neural network model, and one decision tree model) and one clustering model were found to be suitable tools for capturing the intricate relationships that exist between KM and OR. These models made it possible to evaluate the strengths of the relationships between KM and OR and to identify which KM processes contribute, and to what extent, to OR. In addition, the models enabled the collation of predicted OR scores, based on the responses given in the questionnaire. Finally, this research identifies some of the key challenges associated with using DM as a measurement instrument for assessing the relationship between and the impact of KM on OR. This research makes a number of significant contributions to the existing body of knowledge. It contributes to the understanding of the impact of KM on OR, to the understanding of the methods used to measure such impact and to the processes involved in measuring such impact using DM. From a practitioner perspective, this research contributes to the understanding of OR and provides a framework for achieving OR within an organizational context

A two-stage framework for designing visual analytics systems to augment organizational analytical processes

A perennially interesting research topic in the field of visual analytics is how to effectively develop systems that support organizational knowledge worker’s decision-making and reasoning processes. The primary objective of a visual analytic system is to facilitate analytical reasoning and discovery of insights through interactive visual interfaces. It also enables the transfer of capability and expertise from where it resides to where it is needed–across individuals, and organizations as necessary. The problem is, however, most domain analytical practices generally vary from organizations to organizations. This leads to the diversified design of visual analytics systems in incorporating domain analytical processes, making it difficult to generalize the success from one domain to another. Exacerbating this problem is the dearth of general models of analytical workflows available to enable such timely and effective designs. To alleviate these problems, this dissertation presents a two-stage framework for informing the design of a visual analytics system. This two-stage design framework builds upon and extends current practices pertaining to analytical workflow and focuses, in particular, on investigating its effect on the design of visual analytics systems for organizational environments. It aims to empower organizations with more systematic and purposeful information analyses through modeling the domain users’ reasoning processes. The first stage in this framework is an Observation and Designing stage, in which a visual analytic system is designed and implemented to abstract and encapsulate general organizational analytical processes, through extensive collaboration with domain users. The second stage is the User-centric Refinement stage, which aims at interactively enriching and refining the already encapsulated domain analysis process based on understanding user’s intentions through analyzing their task behavior. To implement this framework in the process of designing a visual analytics system, this dissertation proposes four general design recommendations that, when followed, empower such systems to bring the users closer to the center of their analytical processes. This dissertation makes three primary contributions: first, it presents a general characterization of the analytical workflow in organizational environments. This characterization fills in the blank of the current lack of such an analytical model and further represents a set of domain analytical tasks that are commonly applicable to various organizations. Secondly, this dissertation describes a two-stage framework for facilitating the domain users’ workflows through integrating their analytical models into interactive visual analytics systems. Finally, this dissertation presents recommendations and suggestions on enriching and refining domain analysis through capturing and analyzing knowledge workers’ analysis processes. To exemplify the generalizability of these design recommendations, this dissertation presents three visual analytics systems that are developed following the proposed recommendations, including Taste for Xerox Corporation, OpsVis for Microsoft, and IRSV for the U.S. Department of Transportation. All of these systems are deployed to domain knowledge workers and are adopted for their analytical practices. Extensive empirical evaluations are further conducted to demonstrate efficacy of these systems in facilitating domain analytical processes

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

The doctoral research abstract. Vol:9 2016 / Institute of Graduate Studies, UiTM

FOREWORD: Seventy three doctoral graduands will be receiving their scroll today signifying their achievements in completing their PhD journey. The novelty of their research is shared with you through The Doctoral Abstracts on this auspicious occasion, UiTM 84th Convocation. We are indeed proud that another 73 scholarly contributions to the world of knowledge and innovation have taken place through their doctoral research ranging from Science and Technology, Business and Administration, and Social Science and Humanities. As we rejoice and celebrate your achievement, we would like to acknowledge dearly departed Dr Halimi Zakaria’s scholarly contribution entitled “Impact of Antecedent Factors on Collaborative Technologies Usage among Academic Researchers in Malaysian Research Universities”. He has left behind his discovery to be used by other researchers in their quest of pursuing research in the same area, a discovery that his family can be proud of. Graduands, earning your PhD is not the end of discovering new ideas, invention or innovation but rather the start of discovering something new. Enjoy every moment of its discovery and embrace that life is full of mystery and treasure that is waiting for you to unfold. As you unfold life’s mystery, remember you have a friend to count on, and that friend is UiTM. Congratulations for completing this academic journey. Keep UiTM close to your heart and be our ambassador wherever you go. / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR

Air Force Institute of Technology Research Report 2019

This Research Report presents the FY19 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

An approach to the semantic intelligence cloud

Cloud computing is a disruptive technology that aims to provide a utility approach to computing, where users can obtain their required computing resources without investment in infrastructure, computing platforms or services. Cloud computing resources can be obtained from a number internal or external sources. The heterogeneity of cloud service provision makes comparison of services difficult, with further complexity being introduced by a number of provision approaches such as reserved purchase, on-demand provisioning and spot markets.The aim of the research was to develop a semantic framework for cloud computing services which incorporated Cloud Service Agreements, requirements, pricing and Benefits Management.The proposed approach sees the development of an integrated framework where Cloud Service Agreements describe the relationship between cloud service providers and cloud service users. Requirements are developed from agreements and can use the concepts, relationships and assertions provided as requirements. Pricing in turn is established from requirements. Benefits Management is pervasive across the semantic framework developed.The methods used were to provide a comprehensive review of literature to establish a good theoretical basis for the research undertaken. Then problem solving ontology was developed that defined concepts and relationships for the proposed semantic framework. A number of case studies were used to populate the developed ontology with assertions. Reasoning was used to test the framework was correct.The results produced were a proposed framework of concepts, relationships and assertions for a cloud service descriptions, which are presented as ontology in textual and graphical form. Several parts of the ontology were published on public ontology platforms and, in journal and conference papers.The original contribution to knowledge is seen in the results produced. The proposed framework provides the foundations for development of a unified semantic framework for cloud computing service description and has been used by other researchers developing semantic cloud service description.In the area of Cloud Service Agreements a full coverage of the documents described by major standards organisations have been encoded into the framework. Requirements have been modelled as a unique multilevel semantic representation. Pricing of cloud services has been developed using semantic description that can be mapped to requirements. The existing Benefits Management approach has been reimplemented using semantic description.In conclusion a proposed framework has been developed that allows the semantic description of cloud computing services. This approach provides greater expression than simplistic frameworks that use mathematical formulas or models with simple relationships between concepts. The proposed framework is limited to a narrow area of service description and requires expansion to be viable in a commercial setting.Further work sees the development of software toolsets based on the semantic description developed to realise a viable product for mapping high level cloud service requirements to low level cloud resources

A Knowledge Multidimensional Representation Model for Automatic Text Analysis and Generation: Applications for Cultural Heritage

Knowledge is information that has been contextualized in a certain domain, where it can be used and applied. Natural Language provides a most direct way to transfer knowledge at different levels of conceptual density. The opportunity provided by the evolution of the technologies of Natural Language Processing is thus of making more fluid and universal the process of knowledge transfer. Indeed, unfolding domain knowledge is one way to bring to larger audiences contents that would be otherwise restricted to specialists. This has been done so far in a totally manual way through the skills of divulgators and popular science writers. Technology provides now a way to make this transfer both less expensive and more widespread. Extracting knowledge and then generating from it suitably communicable text in natural language are the two related subtasks that need be fulfilled in order to attain the general goal. To this aim, two fields from information technology have achieved the needed maturity and can therefore be effectively combined. In fact, on the one hand Information Extraction and Retrieval (IER) can extract knowledge from texts and map it into a neutral, abstract form, hence liberating it from the stylistic constraints into which it was originated. From there, Natural Language Generation can take charge, by regenerating automatically, or semi-automatically, the extracted knowledge into texts targeting new communities. This doctoral thesis provides a contribution to making substantial this combination through the definition and implementation of a novel multidimensional model for the representation of conceptual knowledge and of a workflow that can produce strongly customized textual descriptions. By exploiting techniques for the generation of paraphrases and by profiling target users, applications and domains, a target-driven approach is proposed to automatically generate multiple texts from the same information core. An extended case study is described to demonstrate the effectiveness of the proposed model and approach in the Cultural Heritage application domain, so as to compare and position this contribution within the current state of the art and to outline future directions