Background. Agile Software Development (ASD) promises agility and flexibility in dealing with uncertainty by prioritizing interaction between people supported by informal communication and knowledge sharing. The lack of practices to manage the knowledge as a resource might jeopardize the application of knowledge in the production of goods and service. The utilization of Knowledge Management (KM) strategies can significantly support achieving and sustaining competitive advantage and brings several benefits to software development. However, how to manage knowledge in ASD is still not well understood or investigated.  Objectives. The main objective of this thesis is to contribute to the software engineering field by providing a different perspective on directions that KM can take to improve knowledge-based resource (KBR) management in ASD. The detailed objectives are: (i) Understand the current ASD environment regarding KM; (ii) Identify KBRs in ASD and its implications for KM; and (iii) Provide an initial set of variables to evaluate knowledge criticality of knowledge items in ASD.  Method. We used a mixed-methods approach to address the objective of this thesis. The methods selected to conduct the studies include systematic literature review, grounded theory, and improvement case study. The data collection comprised a literature review, semi-structured interviews, and practitioners’ feedback through static validation.  Results. From our SLR we observed that that KM strategies in ASD promote mainly knowledge transfer through practices that stimulate social interaction to share tacit knowledge in the project layer, increasing the risk of losing knowledge by keeping the knowledge localized inside a few individual’s minds. When it comes to coordination, practitioners utilize KBRs in their routines, through social collaboration within teams’ environment/settings. However, this process is nonsystematic, which brings inefficiency to KBR utilization resulting in knowledge loss. It can generate negative implications to the course of the software development, including meaningless searches in databases, frustration because of recurrent problems, and unawareness of knowledge sources. To support decision making related to knowledge retention, we have developed an initial version of the method to evaluate the criticality (KCEM) of a knowledge item, which is divided into two categories, relevance, and scarcity.  Conclusion. The current results of this thesis are of particular interest. However, we recognize that the work is unfinished. As a complement to this thesis, we have planned our long-term objective, which is to contribute to creating scalable KM solutions for companies adopting ASD.We divide this long-term objective into three studies: Carry out a complementary study to apply KCEM in different companies; explore efficient ways of storing codified knowledge in combination with the KCEM, and investigate how to define metrics to evaluate the outcomes of KM practices. / S.E.R.T.E.R.T. Research Profile

ABSTRACT Electronic Commerce (EC) has completely changed business environment and model. Furthermore, traditional business needs new transformation strategy to match customer demand. Many companies try to implement Knowledge Management (KM) or Customer Relationship Management (CRM) system to promote customer satisfaction and earn more money. The purposes of this research are as follows: 1. Strategic analysis of customer knowledge management for steel industry 2. Taking steel industry as an example to understand the customer knowledge This research focuses on three dimensions of CRM¡BKM (knowledge management, customer knowledge resource, and customer knowledge measurement) to design the questionnaire for the steel companies. Taiwanese company has limited knowledge about CRM and KM, especially for traditional business such as steel company. This research aims to collect the idea, strategy and model of CRM and KM in the global market. Moreover, this will help traditional business use new transformation strategy to catch on network consumer and create higher added value. This will help steel industry to draw a blue roadmap in the future. Keywords: KM, CRM, customer knowledge, customer knowledge resource, customer knowledge measurement

Thesis (M.A.)--Ohio University, June, 2008. / Title from PDF t.p. Includes bibliographical references.

Les systèmes de recherche d’information utilisent généralement une multitude de fonctionnalités pour classer les documents. Néanmoins, un élément reste essentiel pour le classement, qui est les modèles standards de recherche d’information.Cette thèse aborde une limitation fondamentale des modèles de recherche d’information, à savoir le problème de la disparité des termes <Term Mismatch Problem>. Le problème de la disparité des termes est un problème de longue date dans la recherche d'informations. Cependant, le problème de la récurrence de la disparité des termes n'a pas bien été défini dans la recherche d'information, son importance, et à quel point cela affecterai les résultats de la recherche. Cette thèse tente de répondre aux problèmes présentés ci-dessus.Nos travaux de recherche sont rendus possibles par la définition formelle de la probabilité de la disparité des termes. Dans cette thèse, la disparité des termes est définie comme étant la probabilité d'un terme ne figurant pas dans un document pertinent pour la requête. De ce fait, cette thèse propose des approches pour réduire la probabilité de la disparité des termes. De plus, nous confortons nos proposions par une analyse quantitative de la probabilité de la disparité des termes qui décrit de quelle manière les approches proposées permettent de réduire la probabilité de la disparité des termes tout en conservant les performances du système.Au première niveau, à savoir le document, nous proposons une approche de modification des documents en fonction de la requête de l'utilisateur. Il s'agit de traiter les termes de la requête qui n'apparaissent pas dans le document. Le modèle de document modifié est ensuite utilisé dans un modèle standard de recherche afin d'obtenir un modèle permettant de traiter explicitement la disparité des termes.Au second niveau, à savoir la requête, nous avons proposé deux majeures contributions.Premièrement, nous proposons une approche d'expansion de requête sémantique basée sur une ressource collaborative. Nous concentrons plutôt sur la structure de ressources collaboratives afin d'obtenir des termes d'expansion intéressants qui contribuent à réduire la probabilité de la disparité des termes, et par conséquent, d'améliorer la qualité de la recherche.Deuxièmement, nous proposons un modèle d'expansion de requête basé sur les modèles de langue neuronaux. Les modèles de langue neuronaux sont proposés pour apprendre les représentations vectorielles des termes dans un espace latent, appelées <Distributed Neural Embeddings>. Ces représentations vectorielles s'appuient sur les relations entre les termes permettant ainsi d'obtenir des résultats impressionnants en comparaison avec l'état de l'art dans les taches de similarité de termes. Cependant, nous proposons d'utiliser ces représentations vectorielles comme une ressource qui définit les relations entre les termes.Nous adaptons la définition de la probabilité de la disparité des termes pour chaque contribution ci-dessus. Nous décrivons comment nous utilisons des corpus standard avec des requêtes et des jugements de pertinence pour estimer la probabilité de la disparité des termes. Premièrement, nous estimons la probabilité de la disparité des termes à l'aide les documents et les requêtes originaux. Ainsi, nous présentons les différents cas de la disparité des termes clairement identifiée dans les systèmes de recherche pour les différents types de termes d'indexation. Ensuite, nous indiquons comment nos contributions réduisent la probabilité de la disparité des termes estimée et améliorent le rappel du système.Des directions de recherche prometteuses sont identifiées dans le domaine de la disparité des termes qui pourrait présenter éventuellement un impact significatif sur l'amélioration des scénarios de la recherche. / Even though modern retrieval systems typically use a multitude of features to rank documents, the backbone for search ranking is usually the standard retrieval models.This thesis addresses a limitation of the standard retrieval models, the term mismatch problem. The term mismatch problem is a long standing problem in information retrieval. However, it was not well understood how often term mismatch happens in retrieval, how important it is for retrieval, or how it affects retrieval performance. This thesis answers the above questions.This research is enabled by the formal definition of term mismatch. In this thesis, term mismatch is defined as the probability that a term does not appear in a document given that this document is relevant. We propose several approaches for reducing term mismatch probability through modifying documents or queries. Our proposals are then followed by a quantitative analysis of term mismatch probability that shows how much the proposed approaches reduce term mismatch probability with maintaining the system performance. An essential component for achieving term mismatch probability reduction is the knowledge resource that defines terms and their relationships.First, we propose a document modification approach according to a user query. The main idea of our document modification approach is to deal with mismatched query terms. While prior research on document enrichment provides a static approach for document modification, we are concerned to only modify the document in case of mismatch. The modified document is then used in a standard retrieval model in order to obtain a mismatch aware retrieval model.Second, we propose a semantic query expansion approach based on a collaborative knowledge resource. We focus on the collaborative resource structure to obtain interesting expansion terms that contribute to reduce term mismatch probability, and as a result, improve the effectiveness of search.Third, we propose a query expansion approach based on neural language models. Neural language models are proposed to learn term vector representations, called distributed neural embeddings. Distributed neural embeddings capture relationships between terms, and they obtained impressive results comparing with state of the art approaches in term similarity tasks. However, in information retrieval, distributed neural embeddings are newly started to be exploited. We propose to use distributed neural embeddings as a knowledge resource in a query expansion scenario.Fourth, we apply the term mismatch probability definition for each contribution of the above contributions. We show how we use standard retrieval corpora with queries and relevance judgments to estimate the term mismatch probability. We estimate the term mismatch probability using original documents and queries, and we figure out how mismatch problem is clearly found in search systems for different types of indexing terms. Then, we point out how much our contributions reduce the estimated mismatch probability, and improve the system recall. As a result, we present how the modified document and query representations contribute to build a mismatch aware retrieval model that mitigate term mismatch problem theoretically and practically.This dissertation shows the effectiveness of our proposals to improve retrieval performance. Our experiments are conducted on corpora from two different domains: medical domain and cultural heritage domain. Moreover, we use two different types of indexing terms for representing documents and queries: words and concepts, and we exploit several types of relationships between indexing terms: hierarchical relationships, relationships based on a collaborative resource structure, relationships defined on distributed neural embeddings.Promising research directions are identified where the term mismatch research may make a significance impact on improving the search scenarios.