Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Abstract When designing a workflow schema, the workflow designer must often explicitly deal with exceptional situations, such as abnormal process termination or suspension of task execution. This paper shows how the designer can be supported by tools allowing him to capture exceptional behavior within a workflow schema, by reusing an available set of pre-configured exceptions skeletons. Exceptions are expressed by means of triggers, to be executed on the top of an active database environment. In particular, the paper deals with the handling of typical workflow exceptional situations which are modeled as generic exception skeletons to be included in a new workflow schema by simply specializing or instantiating them. Such skeletons, called patterns , are stored in a catalog; the paper describes the catalog structure and its management tools constituting an integrated environment for pattern-based exception design and reuse. <s> BIB001 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Abstract To realize effective knowledge sharing in teamwork, this paper proposes a knowledge flow model for peer-to-peer knowledge sharing and management in cooperative teams. The model consists of the concepts, rules and methods about the knowledge flow, the knowledge flow process model, and the knowledge flow engine. A reference model for coordinating the knowledge flow process with the workflow process is suggested to provide an integrated approach to model teamwork process. We also discuss the peer-to-peer knowledge-sharing paradigm in large-scale teams and propose the approach for constructing a knowledge flow network from the corresponding workflow. The proposed model provides a new way to model and manage teamwork processes. <s> BIB002 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> The manufacturing industry is usually operated on the basis of business processes, and most workers are familiar with their own processes. The process-based approach can provide an efficient way to capture and navigate knowledge. In this study, we focus on knowledge that may be missed during knowledge transfer among workers. For this, we propose a method for analysing knowledge flow, which is passed among business processes. ::: ::: ::: ::: As a means of developing a process-based approach to analysing knowledge flow, a new categorization of knowledge is presented in three dimensions: scope, type, and usage level. Then we propose a process-based knowledge management framework for analysing knowledge flow, which employs a two-phase analysis: process analysis and knowledge-flow analysis. We use process decomposition and process dependency analysis techniques for process analysis. A knowledge-flow model, represented by a knowledge-flow diagram, is proposed as a tool for representing knowledge. We formulate several semantics for knowledge-flow modelling and suggest procedures for knowledge-flow analysis. The proposed approach is verified and illustrated through a case study of the manufacturing process. Copyright © 2003 John Wiley & Sons, Ltd. <s> BIB003 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Organizations and communities are held together by knowledge flow networks whether people are aware of them or not. To plan such a network is to describe a formal and optimal flow of knowledge as the basis for effective teamwork. The difficulty is that the result of such planning depends greatly on the planners' experience. This paper proposes a pattern-based approach to knowledge flow design for more effective and efficient planning. The approach starts from basic concepts, uses a knowledge spiral to model knowledge flow patterns and operations, and lays down principles for knowledge flow network composition and evolution. Tools for planning, simulation and management of resource-mediated knowledge flow have been developed and experimentally applied to the work of research teams. The planning tool can help users to define, modify and verify a knowledge flow network and to integrate its components. The simulation tool enables users to study knowledge flow in a visualized network and to develop strategies for adapting networks to changing conditions. The basic idea is to adapt and control logistical processes for knowledge flow within teams. <s> BIB004 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Knowledge sharing is a collective process where the people involved collaborate with others in order to learn from them. This effort creates communities where each member cooperates by sharing knowledge about a common domain. An example of these kinds of communities is software maintenance groups, where their members must collaborate with others, and share their knowledge and experience in order to complete their assignments. This paper presents a study carried out in two software maintenance groups to understand how the knowledge flows through these groups, that is, how their members share their knowledge when they perform their activities. The approach used to model the flows of knowledge and to identify the problems that affect that flow are described, as well as the main problems detected, and how we are trying to solve them with an agent-based knowledge management system. <s> BIB005 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Knowledge flow is the knowledge creation, distribution, and reuse among participants. In knowledge intensive organizations, business process control and knowledge assess management are closely related to each other. Workflow management is an important technology for business process control. Yet knowledge management mechanisms can not be represented by current workflow process definition meta models. An innovative extended workflow process definition meta model is first proposed for integrating the above two aspects. Based on that, modeling and control of knowledge flows are studied. A knowledge flow modeling approach is proposed by using five kinds of knowledge flow components to represent knowledge distribution and reuse, cooperation and communication among participants. To deal with dynamic elements in knowledge flows, an adaptive knowledge flow control approach with corresponding algorithms is proposed based on resource constraints, changes of knowledge requirements, and time constraints. This paper presents a beneficial approach for the effective integration of workflow and knowledge management technologies. <s> BIB006 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Scanning the internal and external environment of an organisation to trace current and emerging topics of discourse, is an important task for knowledge management. Results of this scanning process can be used to anticipate on new developments and for detecting possible knowledge bottlenecks. This paper addresses the unobtrusive identification of conceptual structures exchanged and possibly shared in weblogs as a case to explore the opportunities for automated support for this scanning process. The research is also motivated by the observation that many domains defy formal conceptualisation in the sense that professionals, in particular knowledge workers, differ on the meaning of the concepts and terminology and the relations between them. In such domains, we use knowledge management itself as an example, detailed formalisation is pointless and the best we can aim for is finding out whether professionals agree and share, or disagree. Informally, we define a knowledge flow to be the communication of some knowledge (represented as a natural language post on a weblog in our case) to a receiver (a reader of the post in question) when the receiver acknowledges the flow by referring back to the sender in a separate post. The research challenges we pursue are to determine what knowledge is being exchanged and to what degree the participants in the communication share conceptualisations. We have implemented the identification of knowledge flows in a tool called BlogTrace. BlogTrace uses semantic web technology to represent weblogs and linkage structures, term extraction to determine concepts in natural language posts, and a statistically founded algorithm which computes a relevance metric for the concepts extracted and their relation to other concepts for any particular weblog author. Based on several assumptions, software was written that had to answer knowledge management relevant questions concerning the frequency, intensity and topics of knowledge flows in a community of bloggers, which act like members of an actual organisation. The software was tested on a set of questions and the results show that this software provides to a large extent the answers. <s> BIB007 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Effective knowledge support in knowledge-intensive environments can place great demands on information filtering (IF) strategies. An IF system that relies on traditional information retrieval technology and user models (e.g., user profiles) is regarded as an effective approach for supporting long-term information needs. To provide a more effective knowledge support, we propose a task-stage knowledge support model that incorporates the advantages of the traditional IF model with the characteristics of each task-stage. A correlation analysis method is proposed to determine a worker's task-stage (e.g., pre-focus, focus formulation, and post-focus task stages), and an ontology-based topic discovery method is proposed to examine the variety of a worker's information needs for specific topics. Consequently, the knowledge support is achieved by coupling user information needs with task-stage identification. <s> BIB008 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Recognizing and understanding knowledge flow between scientists is valuable for science. Discovering, managing, and utilizing such knowledge are advanced services of the e-science knowledge grid environment. <s> BIB009 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> A knowledge service consists of systematic knowledge and the mechanism of using knowledge to perform a task. The supply of knowledge services forms a knowledge service layer over the knowledge flow network formed by free knowledge sharing. To stimulate the supply of knowledge services, this paper proposes a virtual knowledge service market by establishing reward and reputation mechanisms. Simulations demonstrate that a team with the market mechanism performs better than those without it. The virtual knowledge service market provides an experimental platform for exploring the rules of knowledge service such as the impact of individual behavior on states of individual and team as well as the change of the states. <s> BIB010 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Purpose – The aim of this paper is to suggest a method to redesign business processes from the viewpoint of knowledge flows using a knowledge map.Design/methodology/approach – Knowledge flows and business processes cannot be separated because knowledge is inputted and outputted through business processes. Knowledge flows inherit the feature and appearance of corresponding business processes; therefore, one can identify problems within business processes by analyzing corresponding knowledge flows. The methodology is composed of the following sections: knowledge mapping, knowledge profiling, knowledge flow identification, knowledge flow optimization and TO‐BE process visualization.Findings – This paper provides a methodology for knowledge flow‐based business process redesign and ten guidelines for knowledge flow optimization. The case study demonstrates that the proposed ideas constitute knowledge‐intensified business processes.Research limitations/implications – A more formal validation method that is base... <s> BIB011 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> As the result of business process automation, more and more knowledge is codified and stored in knowledge repositories and scattered in employees’ computers across functionally and geographically separated business units. While several alternative mechanisms such as chat rooms, search engines, recommender systems exist to retrieve and access knowledge, satisfying a knowledge requirement often involves the coordination of multiple tasks and the use of several technologies. However, there is no existing technology for orchestrating various collaboration, communication and information retrieval components to satisfy the knowledge needs in corporations. In order to address this gap, we propose the approach of pattern-based knowledge workflow that can enable the automation of knowledge flows across an organization. In this paper, we present an overview of the pattern-based knowledge workflow approach and propose extensions to the conventional workflow paradigm. We demonstrate the feasibility of implementing knowledge workflows by means of a Business Process Execution Language (BPEL) specification for executing knowledge workflows in a business setting. We then discuss the engineering challenges and research issues that need to be addressed to further develop this approach. <s> BIB012 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Textual knowledge flow (TKF) provides an effective technique and theoretical support for the discovery and cooperation of knowledge innovation, intelligent browsing, and personalized recommendation in Web services and e-Science Knowledge Grid. For the discovery of TKF, firstly knowledge map (KM) is proposed to represent the textual knowledge; then a hash algorithm is used to code KMs in order to form an Island which contains enormous KMs belonging to a domain. Under the control of the Island, C-Location and R-Location are introduced to manage those KMs belonging to an Island. KM-Chord is proposed to manage the number of Islands, C-Locations and R-Locations in Web or a library. With the help of the management of KMs, similar relation and associated relation between KMs are found to build the semantic link network (SLN) between KMs. Based on the SLN and users' profile and input, similar or associated TKF with the user's different demands is activated. Experiments show that the proposed method can effectively discover TKF for Web services and e-Science Knowledge Grid. Copyright © 2008 John Wiley & Sons, Ltd. <s> BIB013 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> Knowledge is a critical resource that organizations use to gain and maintain competitive advantages. In the constantly changing business environment, organizations must exploit effective and efficient methods of preserving, sharing and reusing knowledge in order to help knowledge workers find task-relevant information. Hence, an important issue is how to discover and model the knowledge flow (KF) of workers from their historical work records. The objectives of a knowledge flow model are to understand knowledge workers' task-needs and the ways they reference documents, and then provide adaptive knowledge support. This work proposes hybrid recommendation methods based on the knowledge flow model, which integrates KF mining, sequential rule mining and collaborative filtering techniques to recommend codified knowledge. These KF-based recommendation methods involve two phases: a KF mining phase and a KF-based recommendation phase. The KF mining phase identifies each worker's knowledge flow by analyzing his/her knowledge referencing behavior (information needs), while the KF-based recommendation phase utilizes the proposed hybrid methods to proactively provide relevant codified knowledge for the worker. Therefore, the proposed methods use workers' preferences for codified knowledge as well as their knowledge referencing behavior to predict their topics of interest and recommend task-related knowledge. Using data collected from a research institute laboratory, experiments are conducted to evaluate the performance of the proposed hybrid methods and compare them with the traditional CF method. The results of experiments demonstrate that utilizing the document preferences and knowledge referencing behavior of workers can effectively improve the quality of recommendations and facilitate efficient knowledge sharing. <s> BIB014 </s> Overview of knowledge flow in a software development process <s> I. INTRODUCTION <s> A significant amount of work has been done to better understand the barriers to knowledge flow and develop models of KM maturity; this was intended to help in assessing the progress of KM initiatives in the firm. However, to date there has been no comprehensive research that considers both these issues at the same time, and thus it is still necessary to explore the evolution of barriers to knowledge flow when the level of KM maturity is changing. We reviewed the progress of KM initiatives in recent years, categorized the barriers to knowledge flow according to the cultural historical activity theory (CHAT), and used one of the existing models of KM maturity (the Knowledge Navigator Model-KNM), to address gaps in the current literature. As part of our exploratory study, a longitudinal survey, involving constant observation, development and use of questionnaires and qualitative interviews with managers of seven firms were conducted. We selected the firms so they covered a wide range of KM maturity levels; triangulation was adopted to enhance the quality of the process. The major findings indicated that: (1) barriers to knowledge flow were inherently different at different KM maturity levels; and (2) various changes in the barriers to knowledge flow were associated with the maturity of the KM. <s> BIB015
Knowledge is the foundation of commercial, educational, and theoretical fields. Knowledge management (KM) significantly influences the elevation of the competitive spirit of a team. It involves innovating and spreading, sharing, and using knowledge  . Knowledge has not always been significant but has become a critical factor and an inner force in developing the economy during challenging times. KM is now an area of considerable importance BIB002 BIB006 . The plurality of KM research regularly treats knowledge as an inert or stationary object, and part of that interest drives the dynamic feature of knowledge processing and KM, which is mainly known as knowledge flow (KF). KF has recently been identified in different patterns in KM research and studies  . It is usually characterized as actions, procedures, and events through which data, information, knowledge, and meta-knowledge are converted from one state to another. Disseminated group software improvement may be a kind from claiming programming improvement management standard that concentrates around asset offering Furthermore fill in collaboration between team members geographically conveyed less group parts throughout those improvement procedure II. KNOWLEDGE FLOW Many scholars have developed various definitions of KF. H. Zhuge defines KF as a procedure for passing knowledge to people or organization, knowledge processing tools (mechanisms) and refers to the various definite features of KF, such as direction, content, and carrier BIB002 . Xiaogang Zhang defines KF as a method of creating, conveying, and stratifying knowledge among different participants BIB006 . Innovation requires KF. In BIB002 BIB004 BIB010 , KF research concentration on how transfer using KFs facilitates the sharing and collection of knowledge in a group works (team). In the case of a workflow, work knowledge , information may be passed to participants. At the same time, process knowledge may pass through different functions or tasks. Therefore, KF takes into account the standard of knowledge collaboration between workers or procedures and the effect on the activity, efficiency of group work workflows. KF in a software development team can integrate the information or knowledge from one participant and transfer it to another participant, thus enabling a knowledge partnership to be easily established BIB002 . to improve the activity ,efficiency of teamwork, Zhuge BIB004 suggested pattern based methods that combines codification also personalization methodologies to draw and design an efficient knowledge flow network. To support the mechanization KF, a framework of knowledge workflow task was enhances by Sarnikar and Zhao  BIB012 by conjugating workflow and knowledge to determine the appropriate techniques. Rodriguez et al. BIB005 suggested that KFs in communities or societies of practice assist participants to share information, knowledge and experience particular fields to complete their studies. KF can ease knowledge sharing and be reused in research areas. Particularly, a citation network can be considered a KF that distributes knowledge to researchers. Some KF models have been suggested in the last few years BIB013 . A textual knowledge flow (TKF) model has been designed for a semantic link network. TKF can suggest suitable channels for users after evaluating the benefits and inputs of each channel. KFs can also symbolize the sequence of when implement the task workers, needs the knowledge and or knowledge reference. Patterns Lai and Liu BIB014 developed a time request kf model to define the order of the knowledge workers. In terms of activities, employee could earn the knowledge to gratify the informational requirement through the kf starting with archive document of the KFs from document access log. Indicating order in weblogs can also be considered a KF and has been represented the source (sender message), recipient (receiver model) because the weblog post of a blogger Might incorporate a hyperlink on An weblog post about an alternate blogger BIB007 . Kim et al. BIB003 BIB002 . One benefit of KF is that it can bypass or abolish unnecessary knowledge that passes through the team members BIB002 , as various team members may achieve various kinds of tasks and require different knowledge. Another benefit of KF is that people can avoid the necessity of consuming more time and can take advantage of the reduced effort required. In BIB002 , KF is determined as the procedure of knowledge "movement" from an exporter to a recipients and its following intake and usage. This process is undertaken to improve the capability of the organization in performing the required events. This process can be understood as a procedure for Information, knowledge passing to team or people or knowledge procedure mechanisms BIB015 . As stated by Zhuge. [2], the point of issue and the receiver of knowledge are each recognized as a "knowledge node." The flow has three major key characteristics, namely, direction (path), content, and carrier, Which separately relegate who sends (source) and who receives (recipient )the information or knowledge content and how to transfer the content. the knowledge node can be a team member or role or a knowledge portal or process BIB007 . Yoo, Suh, and Kim BIB011 established that KF in organizations could determine the issues in parallel business processes. An evident identification and optimization of KF can confirm the actual use of knowledge within an organization to enhance the dynamic between organizational knowledge and business processes. Identifying and mapping the KF are strategically important to organizations in three major domains BIB011 : 1. KF results in the transfer of the know-how created in a subunit to other locations within the organization. 2. KFs facilitate the harmony of workflows that join many geographically scattered subunits. 3. KFs help organizations to capitalize on business opportunities that require the cooperation of different subunits. In BIB002 , KF is a crossing procedure of knowledge invention, division, and reuse from a team member (named a knowledge source or sender) to the next person (named a knowledge recipient or receiver), thus enabling a description that uses process logic. It has knowledge source, knowledge receiver, passing direction, and knowledge substance. Knowledge substance is shared among members in a cooperation system. KF might be specified by four groups: KF = <S, R, T, D>. S = {sm: i=1, 2,…, -S-}, depicts knowledge sources, such as people or repositories. R = {ri: j=1, 2,…, -R -}, to describe the required knowledge of the execution task. T = {tn: n=1, 2,.,. -T-}, explanation of un-encoded tacit knowledge, such as beliefs and attitudes. D = {dj: j=1, 2… -D-}, contains of knowledge destinations (recipient or receivers). To clarify a knowledge node for the period of a task process, the KF from Node A to Node B is identified by KN AB , which consists of K AT and K RD . K AT describe the tacit knowledge of A, such as its beliefs and interests. K RD described the required knowledge that B works for tasks. KF BC and KF AC are similar to KF AB . In BIB002 , a KF is a procedure in which knowledge passes to people or knowledge processing mechanisms. The transfer of knowledge from a person, group, or storage medium to another person, group, or storage medium requires the ability of individuals to invent, pass, and apply knowledge in identifying and implementing new tasks, providing real-time logistic support, and creating new logistic ideas and abilities. The three major characteristics of flow, which are direction, content, and carrier, characterize a KF and define the source and the recipient, the shareable information content and information the media that might pass the content. According to Wiing (1995), a KF has four crucial functions: • Building of knowledge by knowledge transfer from external sources and invention of new knowledge through research and development • Storage of knowledge by keeping current knowledge in artifacts • Supply of knowledge by distribution and transfer • Application of knowledge by integration in organizational procedures, products, and facilities In BIB007 BIB003 BIB009 , knowledge flows to persons, and processes occur to accommodate knowledge sharing and reuse. The perception of KF has been applied in several fields, such as scientific research, communities of practice, teamwork, industry, and organizations. Scholarly articles indicate that the main reason for spreading knowledge among scientists is to motivate new concept BIB001 . A citation includes a KF between the citing article and the cited article. Similar to the citation procedure, a KF network empower knowledge with flow to diverse scientific activities to motivate interdisciplinary projects and scientific improvement. In BIB010 BIB004 , a KF model enriches the efficiency of participants by collecting and sharing knowledge among team members to enable peer-to-peer knowledge spreading. To enhance the efficacy of teamwork, a pattern-based approach is suggested that joins both the methodology of codification and personalization with outline to designs a powerful and effective KF network. Kim et al. BIB003 suggested a KF model linked to a process-orientated approach to capture, store, and transfer knowledge. KFs in communities of practice assist members to share their knowledge and experience in a target field to finish certain tasks BIB005 . Luo et al. BIB013 recommended the finding of a textual KF based on a semantic link network. A context-based KF is suggested to reflect the main features of a KF  . In an organization, knowledge employees normally have different knowledge, information needs over time when executing tasks. Therefore, KF is defined from the point of view of the information necessary for a worker to explain the development of referencing activities and the cumulative knowledge for a specific task BIB014 . A KF description is provided in BIB008 . KF is defined as the development of the knowledge demands of a worker when performing a task, containing information necessity, priority, and referencing attitude to categorize knowledge. KF is identified from the work logs of the historical task implementations of a worker. Two levels of KF have been identified: codified and topic. Knowledge at the codified level refers to KFs in documents based on their arrival time. In the majority of cases, the knowledge gained Starting with An report prompts information laborers to get the following related record (codified knowledge). Report progression holds numerous task-related documents. Documents with same subjects of benefit can be grouped into similar disciplines to form a topic-level abstraction of knowledge. Codified-level KF can be extracted to form a topic-level KF, which identify the transmission in many issues of codified knowledge.