Knowledge Presentation and Reasoning with Loglinear Models

Our approach for knowledge presentation is based on the idea of expert system shell. At first we will build a graph shell of both possible dependencies and possible actions. Then, reasoning by means of Loglinear models, we will activate some nodes and some directed links. In this way a Bayesian network and networks presenting loglinear models are generated.

Consuming linked data in supply chains:enabling data visibility via linked pedigrees

The performance of a supply chain depends critically on the coordinating actions and decisions undertaken by the trading partners. The sharing of product and process information plays a central role in the coordination and is a key driver for the success of the supply chain. In this paper we propose the concept of "Linked pedigrees" - linked datasets, that enable the sharing of traceability information of products as they move along the supply chain. We present a distributed and decentralised, linked data driven architecture that consumes real time supply chain linked data to generate linked pedigrees. We then present a communication protocol to enable the exchange of linked pedigrees among trading partners. We exemplify the utility of linked pedigrees by illustrating examples from the perishable goods logistics supply chain.

Ownership identity, strategy and performance:business group affiliates versus independent firms in India

We consider whether the impact of entrepreneurial orientation on business performance is moderated by the company affiliation with business groups. Within business groups, we explore the trade-off between inter-firm insurance that enables risk-taking, and inefficient resource allocation. Risk-taking in group affiliated firms leads to higher performance, compared to independent firms, but the impact of proactivity is attenuated. Utilizing Indian data, we show that risk-taking may undermine rather than improve business performance, but this effect is not present in business groups. Proactivity enhances performance, but less so in business groups. Firms can also enhance performance by technological knowledge acquisition, but these effects are not significantly different for various ownership categories.

Food SMEs’ innovation profile – the Hungarian case

The small and medium sized enterprises (SMEs) in the Hungarian agri-food sector play determining role. The innovation capacity (efforts, activities and results) however of the individual SMEs is very limited. Food production (including SMEs) has to fulfil food safety requirements in a rapidly increasing extent, which implies a continuous innovation and development process from all market players. In Hungary the agri-food sector had to face a suddenly increased competition especially after the EU enlargement. Based on survey data this paper examines the efforts, activities and results in knowledge acquisition, utilisation, coordination and transfer in the Central Hungarian food SMEs. We have found (using ordered logistic regression) that R&D expenditures, achieved innovations, export/import orientation as well as the networking activity of the SMEs play significant role in market development.

Innovation across the food chain – the Hungarian case

The small and medium sized enterprises (SMEs) in the Hungarian agri-food sector play determining role. The innovation capacity (efforts, activities and results) however of the individual SMEs is very limited. Food production (including SMEs) has to fulfil food safety requirements in a rapidly increasing extent, which implies a continuous innovation and development process from all market players. In Hungary the agri-food chain had to face a suddenly increased competition especially after the EU enlargement. Based on survey data this paper examines the efforts, activities and results in knowledge acquisition, utilisation, coordination and transfer in the Central Hungarian food SMEs. We have found (using ordered logistic regression) that R&D expenditures, achieved innovations, export/import orientation as well as the networking activity of the SMEs play significant role in market development.

Introduction to data analysis

With the latest development in computer science, multivariate data analysis methods became increasingly popular among economists. Pattern recognition in complex economic data and empirical model construction can be more straightforward with proper application of modern softwares. However, despite the appealing simplicity of some popular software packages, the interpretation of data analysis results requires strong theoretical knowledge. This book aims at combining the development of both theoretical and applicationrelated data analysis knowledge. The text is designed for advanced level studies and assumes acquaintance with elementary statistical terms. After a brief introduction to selected mathematical concepts, the highlighting of selected model features is followed by a practice-oriented introduction to the interpretation of SPSS1 outputs for the described data analysis methods. Learning of data analysis is usually time-consuming and requires efforts, but with tenacity the learning process can bring about a significant improvement of individual data analysis skills.

Fourth generation evaluation revisited: Testing a revised pause model

This research provides data which investigates the feasibility of using fourth generation evaluation during the process of instruction. A semester length course entitled "Multicultural Communications", (PUR 5406/4934) was designed and used in this study, in response to the need for the communications profession to produce well-trained culturally sensitive practitioners for the work force and the market place. A revised pause model consisting of three one-on-one indepth interviews conducted outside of the class, three reflections periods during the class and a self-reflective essay prepared one week before the end of the course was analyzed. Narrative and graphic summaries of participant responses produced significant results. The revised pause model was found to be an effective evaluation method for use in multicultural education under certain conditions as perceived by the participants in the study. participant self-perceived behavior change and knowledge acquisition was identified through use of the revised pause model. Study results suggest that by using the revised pause model of evaluation, instructors teaching multicultural education in schools of journalism and mass communication is yet another way of enhancing their ability to become both the researcher and the research subject. In addition, the introduction of a qualitative model has been found to be a more useful way of generating participant involvement and introspection. Finally, the instructional design of the course used in the study provides communication educators with a practical way of preparing their students be effective communicators in a multicultural world.

Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.

Knowledge creation in dynamic learning environments

Researchers have extensively discussed using knowledge management to achieve sustainable competitive advantages; however, the successful implementation of knowledge management programs in organizations remains challenging. Problems with knowledge management arise primarily from issues related to inter-subjective creation of meaning by diverse individuals in a dynamic learning environment. ^ The first part of this dissertation examined the concepts of shared interpretive resources referring to background assumptions, shared language, and symbolic resources upon which individuals draw in their interactions in the community. The discussion adopted an interpretive research approach to underscore how community members develop shared interpretive resources over time. The second part examined how learners' behaviors influence knowledge acquisition in the community, emphasizing the associations between learners' learning approaches and learning contexts. An empirical survey of learners provided significant evidence to demonstrate the influences of learners' learning approaches. The third part examined an instructor's strategy—namely, advance organizer—to enhance learners' knowledge assimilation process. Advance organizer is an instructor strategy that refers to a set of inclusive concepts that introduce and sum up new material, and refers to a method of bridging and linking old information with something new. In this part, I underscore the concepts of advance organizer, and the implementations of advance organizer in one learning environment. A study was conducted in one higher educational environment to show the implementation of advance organizer. Additionally, an advance organizer instrument was developed and tested, and results from learners' feedback were analyzed. The significant empirical evidence showed the association between learners' learning outcomes and the implementation of advance organizer strategy. ^

Knowledge, attitudes, and practices affecting health behaviors in the U.S. Army Special Operations Command population of Rangers

Malaria is a threat to United States military personnel operating in endemic areas, from which there have been hundreds of cases reported over the past decade. Each of these cases might have been avoided with proper adherence to malaria chemoprophylaxis medications. Military operations may detract from the strict 100% adherence required of these preventive medications. However, the reasons for non-adherence in military populations are not well understood. This behavior was investigated using a cross sectional study design on a convenience sample of U.S. Army Ranger volunteers (n=150) located at three military instillations. Theoretical support was based on components of the Health Belief Model, the Theory of Reasoned Action/Theory of Planned Behavior, and the Social Cognitive Theory. ^ Data on knowledge, attitudes, and practices, as well as multiple environmental domains was collected using an original yet unvalidated questionnaire. The data was analyzed using bivariate Pearson correlations, binary logistic regression, and moderated logistic regressions employing a 0.05 criterion of statistical significance. Power analyses predicted 96-98% power for this analysis. ^ Multiple significant medium strength Pearson correlation coefficients were identified relative to the two dependent variables Take medications as directed and Intend to take the medications as directed the next time. Binary logistic regression analyses identified multiple variables that may predict behavioral intentions to adhere to these preventive medications, as a proxy for behavioral change. Moderated logistic regression analyses identified Command Support for adherence to these medications as a potential significant moderator that interacts with independent variables within three domains of the survey questionnaire. ^ The findings indicate that there may be potential significant beneficial effects, which may improve this behavior in this population of Rangers through 1) promoting affirmative interpersonal communications that emphasize adherence to these medications, 2) including malaria chemoprophylaxis medications in the mission planning process, and 3) military command support, in the form of including the importance of proper adherence to these medications in the unit safety briefings.^

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

Classificação de técnicas e auxílio à navegação de busca com base em sua carga cognitiva, precisão e eficiência

Navigation, in both virtual and real environments, is the process of a deliberated movement to a specific place that is usually away from the origin point, and that cannot be perceived from it. Navigation aid techniques (TANs) have as their main objective help finding a path through a virtual environment to a desired location and, are widely used because they ease the navigation on these unknown environments. Tools like maps, GPS (Global Positioning System) or even oral instructions are real world examples of TAN usage. Most of the works which propose new TANs for virtual environments aim to analyze their impact in efficiency gain on navigation tasks from a known place to an unknown place. However, such papers tend to ignore the effect caused by a TAN usage over the route knowledge acquisition process, which is important on virtual to real training transfer, for example. Based on a user study, it was possible to confirm that TANs with different strategies affects the performance of search tasks differently and that the efficiency of the help provided by a TAN is not inversely related to the cognitive load of the technique’s aids. A technique classification formula was created. This formula utilizes three factors instead of only efficiency. The experiment’s data were applied to the formula and we obtained a better refinement of help level provided by TANs.

Classificazione e selezione di tecniche di visualizzazione per Big Data Analytics

La tesi presenta uno studio della libreria grafica per web D3, sviluppata in javascript, e ne presenta una catalogazione dei grafici implementati e reperibili sul web. Lo scopo è quello di valutare la libreria e studiarne i pregi e difetti per capire se sia opportuno utilizzarla nell'ambito di un progetto Europeo. Per fare questo vengono studiati i metodi di classificazione dei grafici presenti in letteratura e viene esposto e descritto lo stato dell'arte del data visualization. Viene poi descritto il metodo di classificazione proposto dal team di progettazione e catalogata la galleria di grafici presente sul sito della libreria D3. Infine viene presentato e studiato in maniera formale un algoritmo per selezionare un grafico in base alle esigenze dell'utente.