Search results for: knowledge graph and recommender system
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 23127

Search results for: knowledge graph and recommender system

22947 Applying Spanning Tree Graph Theory for Automatic Database Normalization

Authors: Chetneti Srisa-an

Abstract:

In Knowledge and Data Engineering field, relational database is the best repository to store data in a real world. It has been using around the world more than eight decades. Normalization is the most important process for the analysis and design of relational databases. It aims at creating a set of relational tables with minimum data redundancy that preserve consistency and facilitate correct insertion, deletion, and modification. Normalization is a major task in the design of relational databases. Despite its importance, very few algorithms have been developed to be used in the design of commercial automatic normalization tools. It is also rare technique to do it automatically rather manually. Moreover, for a large and complex database as of now, it make even harder to do it manually. This paper presents a new complete automated relational database normalization method. It produces the directed graph and spanning tree, first. It then proceeds with generating the 2NF, 3NF and also BCNF normal forms. The benefit of this new algorithm is that it can cope with a large set of complex function dependencies.

Keywords: relational database, functional dependency, automatic normalization, primary key, spanning tree

Procedia PDF Downloads 328
22946 Optimizing Availability of Marine Knowledge Repository with Cloud-Based Framework

Authors: Ahmad S. Mohd Noor, Emma A. Sirajudin, Nur F. Mat Zain

Abstract:

Reliability is an important property for knowledge repository system. National Marine Bioinformatics System or NABTICS is a marine knowledge repository portal aimed to provide a baseline for marine biodiversity and a tool for researchers and developers. It is intended to be a large and growing online database and also a metadata system for inputs of research analysis. The trends of present large distributed systems such as Cloud computing are the delivery of computing as a service rather than a product. The goal of this research is to make NABTICS a system of greater availability by integrating it with Cloud based Neighbor Replication and Failure Recovery (NRFR). This can be achieved by implementation of NABTICS into distributed environment. As a result, the user can experience minimum downtime while using the system should the server is having a failure. Consequently the online database application is said to be highly available.

Keywords: cloud, availability, distributed system, marine repository, database replication

Procedia PDF Downloads 444
22945 The Influences of Marketplace Knowledge, General Product Class Knowledge, and Knowledge in Meat Product with Traceability on Trust in Meat Traceability

Authors: Kawpong Polyorat

Abstract:

Since the outbreak of mad cow disease and bird flu, consumers have become more concerned with meat quality and safety. As a result, meat traceability is adopted as one approach to handle consumers’ concern in this issue. Nevertheless, in Thailand, meat traceability is rarely used as a marketing tool to persuade consumers. As a consequence, the present study attempts to understand consumer trust in the meat traceability system by conducting a study in this country to examine the impact of three types of consumer knowledge on this trust. The study results reveal that out of three types of consumer knowledge, marketplace knowledge was the sole predictor of consumer trust in meat traceability and it has a positive influence. General product class knowledge and knowledge in meat products with traceability, however, did not significantly influence consumer trust. The research results provide several implications and directions for future study.

Keywords: consumer knowledge, marketing, product knowledge, traceability

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22944 The Contemporary Issues of Quality Management: Relationship between Total Quality Management and Knowledge Management

Authors: Mehrnoosh Askarizadeh

Abstract:

To meet the challenges of the new global environment, companies have started paying great attention towards quality management as an integral part of their strategic business plans. The purpose of this article is to investigate the relationship between total quality management (TQM) and knowledge management (KM). Successful total quality management implementation throughout the organizations requires major changes in the main four aspects of knowledge management, namely: Creating, storage, sharing and application. Skill, knowledge and productivity are important factors in organization’s success and have important role. Therefore, TQM management system pays special attention to it. However, knowledge as the source is essential for organization’s survival. Our study points out how the quality management and knowledge management have been incorporated into each other for the development of the quality culture within the organization.

Keywords: knowledge management (KM), total quality management (TQM), organizational performance (OP), deming cycle

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22943 Nonlinear Evolution on Graphs

Authors: Benniche Omar

Abstract:

We are concerned with abstract fully nonlinear differential equations having the form y’(t)=Ay(t)+f(t,y(t)) where A is an m—dissipative operator (possibly multi—valued) defined on a subset D(A) of a Banach space X with values in X and f is a given function defined on I×X with values in X. We consider a graph K in I×X. We recall that K is said to be viable with respect to the above abstract differential equation if for each initial data in K there exists at least one trajectory starting from that initial data and remaining in K at least for a short time. The viability problem has been studied by many authors by using various techniques and frames. If K is closed, it is shown that a tangency condition, which is mainly linked to the dynamic, is crucial for viability. In the case when X is infinite dimensional, compactness and convexity assumptions are needed. In this paper, we are concerned with the notion of near viability for a given graph K with respect to y’(t)=Ay(t)+f(t,y(t)). Roughly speaking, the graph K is said to be near viable with respect to y’(t)=Ay(t)+f(t,y(t)), if for each initial data in K there exists at least one trajectory remaining arbitrary close to K at least for short time. It is interesting to note that the near viability is equivalent to an appropriate tangency condition under mild assumptions on the dynamic. Adding natural convexity and compactness assumptions on the dynamic, we may recover the (exact) viability. Here we investigate near viability for a graph K in I×X with respect to y’(t)=Ay(t)+f(t,y(t)) where A and f are as above. We emphasis that the t—dependence on the perturbation f leads us to introduce a new tangency concept. In the base of a tangency conditions expressed in terms of that tangency concept, we formulate criteria for K to be near viable with respect to y’(t)=Ay(t)+f(t,y(t)). As application, an abstract null—controllability theorem is given.

Keywords: abstract differential equation, graph, tangency condition, viability

Procedia PDF Downloads 117
22942 Integrated Approach of Knowledge Economy and Society in the Perspective of Higher Education Institutions

Authors: S. K. Ashiquer Rahman

Abstract:

Innovation, sustainability, and higher education are vital issues of the knowledge economy and society. In fact, the concentration on these issues, educators and researchers convinced the learners to prepare productive citizens for the knowledge economy and society, and many initiatives have been launched worldwide. The concept of a knowledge economy requires simultaneous and balanced progress in three dimensions (Innovation, Education and Sustainability) which are totally interdependent and correlated. The paper discusses the importance of an integrated approach to the knowledge economy and society from the perspective of higher education institutions. It remarks on the advent of a knowledge-based economy and society and the need for the combination of Innovation, sustainability, and education. This paper introduces nine (9) important issues or challenges of higher education institutions that have emphasized, cross-linked each other, and combined in a new education system that can form a new generation for the completive world as well as able to manage the knowledge-based economy and societal system. Moreover, the education system must be the foundation for building the necessary knowledge-based economy and society, which must manage the innovation process through a more sustainable world. In this viewpoint, Innovation, sustainability and higher education are becoming more and more central in our economy and society, and it is directly associated with the possibility of global wealth distribution to the economy and society. The objective of this research is to demonstrate the knowledge-based economy and social paradigm in order to create the opportunity for higher education institutions' development. The paper uses the collective action methodologies to examine “the mechanisms and strategies” used by higher education institutions’ authority to accommodate an integrated pattern as per connecting behaviors of knowledge economy and society. The paper accomplishes that the combination of Innovation, sustainability and education is a very helpful approach to building a knowledge-based economy and society for practicing the higher education institution’s challenges.

Keywords: education, innovation, knowledge economy, sustainability

Procedia PDF Downloads 74
22941 Knowledge Discovery from Production Databases for Hierarchical Process Control

Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

Abstract:

The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: hierarchical process control, knowledge discovery from databases, neural network, process control

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22940 Knowledge Transfer from Experts to Novice: An Empirical Study on Online Communities

Authors: Firmansyah David

Abstract:

This paper aims to investigate factors that drive individuals to transfer their knowledge in the context of online communities. By revisiting tacit-to-explicit knowledge creation, this research attempts to contribute empirically using three online forums (1) Software Engineering; (2) Aerospace Simulator; (3) Health Insurance System. A qualitative approach was deployed to map and recognize the pattern of users ‘Knowledge Transfer (KT), particularly from expert to novice. The findings suggest a common form on how experts give their effort to formulate ‘explicit’ knowledge and how novices ‘understand’ such knowledge. This research underlines that skill; intuition, judgment; value and belief are the prominent factors, both for experts and novice. Further, this research has recognized the groups of expert and novice by their ability to transfer and to ‘adopt’ new knowledge. Future research infers to triangulate the method in which the quantitative study is needed to measure the level of adoption of (new) knowledge by individuals.

Keywords: explicit, expert, knowledge, online community

Procedia PDF Downloads 246
22939 Modeling of Bioelectric Activity of Nerve Cells Using Bond Graph Method

Authors: M. Ghasemi, F. Eskandari, B. Hamzehei, A. R. Arshi

Abstract:

Bioelectric activity of nervous cells might be changed causing by various factors. This alteration can lead to unforeseen circumstances in other organs of the body. Therefore, the purpose of this study was to model a single neuron and its behavior under an initial stimulation. This study was developed based on cable theory by means of the Bond Graph method. The numerical values of the parameters were derived from empirical studies of cellular electrophysiology experiments. Initial excitation was applied through square current functions, and the resulted action potential was estimated along the neuron. The results revealed that the model was developed in this research adapted with the results of experimental studies and demonstrated the electrical behavior of nervous cells properly.

Keywords: bond graph, stimulation, nervous cells, modeling

Procedia PDF Downloads 400
22938 Impact of Similarity Ratings on Human Judgement

Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

Abstract:

Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval

Procedia PDF Downloads 91
22937 Introduction of Knowledge Management in a Public Sector Organization in India

Authors: Siddharth Vashisth, Varun Mathur

Abstract:

This review provides an overview of the impact that implementation of various Knowledge Management (KM) strategies has had on the growth of a department in a Public Sector Company in India. In a regulated utility controlled by the government, the growth of an organization such as Hindustan Petroleum Corporation Limited (HPCL) had depended largely on the efficiencies of the systems and its people. However, subsequent to the de-regularization & to the entry of the private competition, the need for a ‘systematic templating’ of knowledge was recognized. This necessitated the introduction of Knowledge Management Centre (KMC). Projects & Pipelines Department (P&P) of HPCL introduced KMC that contributed significantly towards KM by adopting various strategies such as standardization, leveraging information system, competency enhancement, and improvements & innovations. These strategies gave both tangible as well as intangible benefits towards KM. Knowledge, technology & people are the three pillars that need to be catered for effective knowledge management in any organization. In HPCL, the initiative of KMC has served as an intermediary between these three major pillars as each activity of the strategy was centered on them and contributed significantly to their growth and up-gradation, ensuring overall growth of KM in the department.

Keywords: knowledge, knowledge management, public sector organization, standardization, technology, people, skill, information system, innovation, competency, impact

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22936 Constructing Orthogonal De Bruijn and Kautz Sequences and Applications

Authors: Yaw-Ling Lin

Abstract:

A de Bruijn graph of order k is a graph whose vertices representing all length-k sequences with edges joining pairs of vertices whose sequences have maximum possible overlap (length k−1). Every Hamiltonian cycle of this graph defines a distinct, minimum length de Bruijn sequence containing all k-mers exactly once. A Kautz sequence is the minimal generating sequence so as the sequence of minimal length that produces all possible length-k sequences with the restriction that every two consecutive alphabets in the sequences must be different. A collection of de Bruijn/Kautz sequences are orthogonal if any two sequences are of maximally differ in sequence composition; that is, the maximum length of their common substring is k. In this paper, we discuss how such a collection of (maximal) orthogonal de Bruijn/Kautz sequences can be made and use the algorithm to build up a web application service for the synthesized DNA and other related biomolecular sequences.

Keywords: biomolecular sequence synthesis, de Bruijn sequences, Eulerian cycle, Hamiltonian cycle, Kautz sequences, orthogonal sequences

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22935 Graph Neural Network-Based Classification for Disease Prediction in Health Care Heterogeneous Data Structures of Electronic Health Record

Authors: Raghavi C. Janaswamy

Abstract:

In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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22934 Aspect-Level Sentiment Analysis with Multi-Channel and Graph Convolutional Networks

Authors: Jiajun Wang, Xiaoge Li

Abstract:

The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

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22933 Optimizing Design Works in Construction Consultant Company: A Knowledge-Based Application

Authors: Phan Nghiem Vu, Le Tuan Vu, Ta Quang Tai

Abstract:

The optimal construction design used during the execution of a construction project is a key factor in determining high productivity and customer satisfaction, however, this management process sometimes is carried out without care and the systematic method that it deserves, bringing negative consequences. This study proposes a knowledge management (KM) approach that will enable the intelligent use of experienced and acknowledged engineers to improve the management of construction design works for a project. Then a knowledge-based application to support this decision-making process is proposed and described. To define and design the system for the application, semi-structured interviews were conducted within five construction consulting organizations with the purpose of studying the way that the method’ optimizing process is implemented in practice and the knowledge supported with it. A system of an optimizing construction design works (OCDW) based on knowledge was developed then validated with construction experts. The OCDW was liked as a valuable tool for construction design works’ optimization, by supporting organizations to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The benefits are described as provided by the performance support system, reducing costs and time, improving product design quality, satisfying customer requirements, expanding the brand organization.

Keywords: optimizing construction design work, construction consultant organization, knowledge management, knowledge-based application

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22932 Design of a Tool for Generating Test Cases from BPMN

Authors: Prat Yotyawilai, Taratip Suwannasart

Abstract:

Business Process Model and Notation (BPMN) is more important in the business process and creating functional models, and is a standard for OMG, which becomes popular in various organizations and in education. Researches related to software testing based on models are prominent. Although most researches use the UML model in software testing, not many researches use the BPMN Model in creating test cases. Therefore, this research proposes a design of a tool for generating test cases from the BPMN. The model is analyzed and the details of the various components are extracted before creating a flow graph. Both details of components and the flow graph are used in generating test cases.

Keywords: software testing, test case, BPMN, flow graph

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22931 Petri Net Modeling and Simulation of a Call-Taxi System

Authors: T. Godwin

Abstract:

A call-taxi system is a type of taxi service where a taxi could be requested through a phone call or mobile app. A schematic functioning of a call-taxi system is modeled using Petri net, which provides the necessary conditions for a taxi to be assigned by a dispatcher to pick a customer as well as the conditions for the taxi to be released by the customer. A Petri net is a graphical modeling tool used to understand sequences, concurrences, and confluences of activities in the working of discrete event systems. It uses tokens on a directed bipartite multi-graph to simulate the activities of a system. The Petri net model is translated into a simulation model and a call-taxi system is simulated. The simulation model helps in evaluating the operation of a call-taxi system based on the fleet size as well as the operating policies for call-taxi assignment and empty call-taxi repositioning. The developed Petri net based simulation model can be used to decide the fleet size as well as the call-taxi assignment policies for a call-taxi system.

Keywords: call-taxi, discrete event system, petri net, simulation modeling

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22930 Autism Spectrum Disorder Classification Algorithm Using Multimodal Data Based on Graph Convolutional Network

Authors: Yuntao Liu, Lei Wang, Haoran Xia

Abstract:

Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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22929 A Soft System Approach to Explore Ill-Defined Issues in Distance Education System - A Case of Saudi Arabia

Authors: Sulafah Basahel

Abstract:

Nowadays, Higher Education Institutions (HEIs) around the world are attempting to utilize Information and Communication Technologies (ICTs) to enhance learning process and strategies of knowledge delivery for students through Distance Education (DE) system. Stakeholders in DE system face a complex situation of different ill-defined and related issues that influence decision making process. In this study system thinking as a body of knowledge is used to explore the emergent properties that produced from these connections between issues and could have either positive or negative outcomes for the DE development. Checkland Soft System Methodology (SSM) - Mode 2 is employed in a cultural context of Saudi Arabia for more knowledge acquisition purposes among multiple stakeholders in DE rather than solving problems to achieve an overall development of DE system. This paper will discuss some political, cultural issues and connections between them that impact on effectiveness of stakeholders’ activities and relations. This study will significantly contribute to both system thinking and education fields by leading decision makers in DE to reconsider future plans, strategies and right actions for more successful educational practices.

Keywords: distance education, higher education institutions, ill-defined issues, soft system methodology-Mode 2

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22928 Robust Stabilization against Unknown Consensus Network

Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha

Abstract:

This paper considers a robust stabilization problem of a single agent in a multi-agent consensus system composed of identical agents, when the network topology of the system is completely unknown. It is shown that the transfer function of an agent in a consensus system can be described as a multiplicative perturbation of the isolated agent transfer function in frequency domain. Applying known robust stabilization results, we present sufficient conditions for a robust stabilization of an agent against unknown network topology.

Keywords: single agent control, multi-agent system, transfer function, graph angle

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22927 Gender Effects in EEG-Based Functional Brain Networks

Authors: Mahdi Jalili

Abstract:

Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

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22926 Towards Developing a Strategic Framework for Sustainable Knowledge Economy

Authors: Hamid Alalwany, Nabeel A. Koshak, Mohammad K. Ibrahim

Abstract:

Both knowledge economy and sustainable development are considered key dimensions in the policy action lines of many developed and developing countries. In this context, universities and other higher education institutes have a vital role in developing and sustaining wellbeing communities. In this paper, the authors’ aim is to address the links between the concepts of innovation and entrepreneurial capacity and knowledge economy, and to utilize the approach of intellectual capital development in building a sustainable knowledge economy. The paper will contribute to two discourses: (1) Developing a common understanding of the intersection aspects between the three concepts: Knowledge economy, Innovation and entrepreneurial system, and sustainable development; (2) Paving the road towards developing an integrated multidimensional framework for sustainable knowledge economy.

Keywords: innovation and entrepreneurial capacity, intellectual capital development, sustainable development, sustainable knowledge economy.

Procedia PDF Downloads 500
22925 Encapsulation of Volatile Citronella Essential oil by Coacervation: Efficiency and Release Kinetic Study

Authors: Rafeqah Raslan, Mastura AbdManaf, Junaidah Jai, Istikamah Subuki, Ana Najwa Mustapa

Abstract:

The volatile citronella essential oil was encapsulated by simple coacervation and complex coacervation using gum Arabic and gelatin as wall material. Glutaraldehyde was used in the methodology as crosslinking agent. The citronella standard calibration graph was developed with R2 equal to 0.9523 for the accurate determination of encapsulation efficiency and release study. The release kinetic was analyzed based on Fick’s law of diffusion for polymeric system and linear graph of log fraction release over log time was constructed to determine the release rate constant, k and diffusion coefficient, n. Both coacervation methods in the present study produce encapsulation efficiency around 94%. The capsules morphology analysis supported the release kinetic mechanisms of produced capsules for both coacervation process.

Keywords: simple coacervation, complex coacervation, encapsulation efficiency, release kinetic study

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22924 Sharing Tacit Knowledge: The Essence of Knowledge Management

Authors: Ayesha Khatun

Abstract:

In 21st century where markets are unstable, technologies rapidly proliferate, competitors multiply, products and services become obsolete almost overnight and customers demand low cost high value product, leveraging and harnessing knowledge is not just a potential source of competitive advantage rather a necessity in technology based and information intensive industries. Knowledge management focuses on leveraging the available knowledge and sharing the same among the individuals in the organization so that the employees can make best use of it towards achieving the organizational goals. Knowledge is not a discrete object. It is embedded in people and so difficult to transfer outside the immediate context that it becomes a major competitive advantage. However, internal transfer of knowledge among the employees is essential to maximize the use of knowledge available in the organization in an unstructured manner. But as knowledge is the source of competitive advantage for the organization it is also the source of competitive advantage for the individuals. People think that knowledge is power and sharing the same may lead to lose the competitive position. Moreover, the very nature of tacit knowledge poses many difficulties in sharing the same. But sharing tacit knowledge is the vital part of knowledge management process because it is the tacit knowledge which is inimitable. Knowledge management has been made synonymous with the use of software and technology leading to the management of explicit knowledge only ignoring personal interaction and forming of informal networks which are considered as the most successful means of sharing tacit knowledge. Factors responsible for effective sharing of tacit knowledge are grouped into –individual, organizational and technological factors. Different factors under each category have been identified. Creating a positive organizational culture, encouraging personal interaction, practicing reward system are some of the strategies that can help to overcome many of the barriers to effective sharing of tacit knowledge. Methodology applied here is completely secondary. Extensive review of relevant literature has been undertaken for the purpose.

Keywords: knowledge, tacit knowledge, knowledge management, sustainable competitive advantage, organization, knowledge sharing

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22923 Digital Learning Repositories for Vocational Teaching and Knowledge Sharing

Authors: Prachyanun Nilsook, Panita Wannapiroon

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The purpose of this research is to study a Digital Learning Repository System (DLRS) on vocational teachers and teaching in Thailand. The innobpcd.net is a DLRS being utilized by the Office of Vocational Education Commission and operationalized by the Bureau of Personnel Competency Development for vocational education teachers. The aim of the system is to support and enhance the process of vocational teaching and to improve staff development by providing teachers with a variety of network connections and information. The system provides centralized hosting and access to content, and the ability to share digital objects or files, to set permissions and controls for access to content that can be used vocational education teachers for their teaching and for their own development. The elements of DLRS include; Digital learning system, Media Library, Knowledge-based system and Mobile Application. The system aims to link vocational teachers to the most effective emerging technologies available for learning, so they are better resourced to support their vocational students. The initial results from this evaluation indicate that there is a range of services provided by the system being used by vocational teachers and this paper indicates which facilities have the greatest usage and impact on vocational teaching in Thailand.

Keywords: digital learning repositories, vocational education, knowledge sharing, learning objects

Procedia PDF Downloads 441
22922 Mining Educational Data to Support Students’ Major Selection

Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well.

Keywords: data mining technique, the decision support system, knowledge and decision rules, education

Procedia PDF Downloads 399
22921 Deciding Graph Non-Hamiltonicity via a Closure Algorithm

Authors: E. R. Swart, S. J. Gismondi, N. R. Swart, C. E. Bell

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We present an heuristic algorithm that decides graph non-Hamiltonicity. All graphs are directed, each undirected edge regarded as a pair of counter directed arcs. Each of the n! Hamilton cycles in a complete graph on n+1 vertices is mapped to an n-permutation matrix P where p(u,i)=1 if and only if the ith arc in a cycle enters vertex u, starting and ending at vertex n+1. We first create exclusion set E by noting all arcs (u, v) not in G, sufficient to code precisely all cycles excluded from G i.e. cycles not in G use at least one arc not in G. Members are pairs of components of P, {p(u,i),p(v,i+1)}, i=1, n-1. A doubly stochastic-like relaxed LP formulation of the Hamilton cycle decision problem is constructed. Each {p(u,i),p(v,i+1)} in E is coded as variable q(u,i,v,i+1)=0 i.e. shrinks the feasible region. We then implement the Weak Closure Algorithm (WCA) that tests necessary conditions of a matching, together with Boolean closure to decide 0/1 variable assignments. Each {p(u,i),p(v,j)} not in E is tested for membership in E, and if possible, added to E (q(u,i,v,j)=0) to iteratively maximize |E|. If the WCA constructs E to be maximal, the set of all {p(u,i),p(v,j)}, then G is decided non-Hamiltonian. Only non-Hamiltonian G share this maximal property. Ten non-Hamiltonian graphs (10 through 104 vertices) and 2000 randomized 31 vertex non-Hamiltonian graphs are tested and correctly decided non-Hamiltonian. For Hamiltonian G, the complement of E covers a matching, perhaps useful in searching for cycles. We also present an example where the WCA fails.

Keywords: Hamilton cycle decision problem, computational complexity theory, graph theory, theoretical computer science

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22920 Some New Bounds for a Real Power of the Normalized Laplacian Eigenvalues

Authors: Ayşe Dilek Maden

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For a given a simple connected graph, we present some new bounds via a new approach for a special topological index given by the sum of the real number power of the non-zero normalized Laplacian eigenvalues. To use this approach presents an advantage not only to derive old and new bounds on this topic but also gives an idea how some previous results in similar area can be developed.

Keywords: degree Kirchhoff index, normalized Laplacian eigenvalue, spanning tree, simple connected graph

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22919 Agricultural Knowledge Management System Design, Use, and Consequence for Knowledge Sharing and Integration

Authors: Dejen Alemu, Murray E. Jennex, Temtim Assefa

Abstract:

This paper is investigated to understand the design, the use, and the consequence of Knowledge Management System (KMS) for knowledge systems sharing and integration. A KMS for knowledge systems sharing and integration is designed to meet the challenges raised by knowledge management researchers and practitioners: the technical, the human, and social factors. Agricultural KMS involves various members coming from different Communities of Practice (CoPs) who possess their own knowledge of multiple practices which need to be combined in the system development. However, the current development of the technology ignored the indigenous knowledge of the local communities, which is the key success factor for agriculture. This research employed the multi-methodological approach to KMS research in action research perspective which consists of four strategies: theory building, experimentation, observation, and system development. Using the KMS development practice of Ethiopian agricultural transformation agency as a case study, this research employed an interpretive analysis using primary qualitative data acquired through in-depth semi-structured interviews and participant observations. The Orlikowski's structuration model of technology has been used to understand the design, the use, and the consequence of the KMS. As a result, the research identified three basic components for the architecture of the shared KMS, namely, the people, the resources, and the implementation subsystems. The KMS were developed using web 2.0 tools to promote knowledge sharing and integration among diverse groups of users in a distributed environment. The use of a shared KMS allows users to access diverse knowledge from a number of users in different groups of participants, enhances the exchange of different forms of knowledge and experience, and creates high interaction and collaboration among participants. The consequences of a shared KMS on the social system includes, the elimination of hierarchical structure, enhance participation, collaboration, and negotiation among users from different CoPs having common interest, knowledge and skill development, integration of diverse knowledge resources, and the requirement of policy and guideline. The research contributes methodologically for the application of system development action research for understanding a conceptual framework for KMS development and use. The research have also theoretical contribution in extending structuration model of technology for the incorporation of variety of knowledge and practical implications to provide management understanding in developing strategies for the potential of web 2.0 tools for sharing and integration of indigenous knowledge.

Keywords: communities of practice, indigenous knowledge, participation, structuration model of technology, Web 2.0 tools

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22918 Method of Cluster Based Cross-Domain Knowledge Acquisition for Biologically Inspired Design

Authors: Shen Jian, Hu Jie, Ma Jin, Peng Ying Hong, Fang Yi, Liu Wen Hai

Abstract:

Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design.

Keywords: knowledge clustering, knowledge acquisition, knowledge based engineering, knowledge cell, biologically inspired design

Procedia PDF Downloads 404