The Experts below are selected from a list of 196824 Experts worldwide ranked by ideXlab platform

Nikolay Mehandjiev - One of the best experts on this subject based on the ideXlab platform.

  • Semantic content-based recommendation of Software Services using context
    ACM Transactions on the Web, 2013
    Co-Authors: Liwei Liu, Freddy Lecue, Nikolay Mehandjiev
    Abstract:

    The current proliferation of Software Services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional Services, yet their direct application to Software Services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune Software Services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic Services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

  • ICWS - A Hybrid Approach to Recommending Semantic Software Services
    2011 IEEE International Conference on Web Services, 2011
    Co-Authors: Liwei Liu, Freddy Lecue, Nikolay Mehandjiev
    Abstract:

    The current proliferation of Software Services means users should be supported when selecting one service out of the many which meet consumer's needs. Recommender Systems provide such support for selecting products, yet their direct application to Software Services is not straightforward. In this paper, we derive three requirements for Software service recommender systems and then propose a hybrid recommendation approach to address these requirements and provide effective recommendations in conditions of scarce user feedback. The approach combines semantic Content-based reasoning and context-dependent Collaborative Filtering. The paper ends with the experiments based on a realistic set of semantic Services against existing approaches, demonstrating how our approach can produce effective recommendation using semantic reasoning over service specifications.

Liwei Liu - One of the best experts on this subject based on the ideXlab platform.

  • Semantic content-based recommendation of Software Services using context
    ACM Transactions on the Web, 2013
    Co-Authors: Liwei Liu, Freddy Lecue, Nikolay Mehandjiev
    Abstract:

    The current proliferation of Software Services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional Services, yet their direct application to Software Services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune Software Services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic Services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

  • ICWS - A Hybrid Approach to Recommending Semantic Software Services
    2011 IEEE International Conference on Web Services, 2011
    Co-Authors: Liwei Liu, Freddy Lecue, Nikolay Mehandjiev
    Abstract:

    The current proliferation of Software Services means users should be supported when selecting one service out of the many which meet consumer's needs. Recommender Systems provide such support for selecting products, yet their direct application to Software Services is not straightforward. In this paper, we derive three requirements for Software service recommender systems and then propose a hybrid recommendation approach to address these requirements and provide effective recommendations in conditions of scarce user feedback. The approach combines semantic Content-based reasoning and context-dependent Collaborative Filtering. The paper ends with the experiments based on a realistic set of semantic Services against existing approaches, demonstrating how our approach can produce effective recommendation using semantic reasoning over service specifications.

Freddy Lecue - One of the best experts on this subject based on the ideXlab platform.

  • Semantic content-based recommendation of Software Services using context
    ACM Transactions on the Web, 2013
    Co-Authors: Liwei Liu, Freddy Lecue, Nikolay Mehandjiev
    Abstract:

    The current proliferation of Software Services means users should be supported when selecting one service out of the many which meet their needs. Recommender Systems provide such support for selecting products and conventional Services, yet their direct application to Software Services is not straightforward, because of the current scarcity of available user feedback, and the need to fine-tune Software Services to the context of intended use. In this article, we address these issues by proposing a semantic content-based recommendation approach that analyzes the context of intended service use to provide effective recommendations in conditions of scarce user feedback. The article ends with two experiments based on a realistic set of semantic Services. The first experiment demonstrates how the proposed semantic content-based approach can produce effective recommendations using semantic reasoning over service specifications by comparing it with three other approaches. The second experiment demonstrates the effectiveness of the proposed context analysis mechanism by comparing the performance of both context-aware and plain versions of our semantic content-based approach, benchmarked against user-performed selection informed by context.

  • ICWS - A Hybrid Approach to Recommending Semantic Software Services
    2011 IEEE International Conference on Web Services, 2011
    Co-Authors: Liwei Liu, Freddy Lecue, Nikolay Mehandjiev
    Abstract:

    The current proliferation of Software Services means users should be supported when selecting one service out of the many which meet consumer's needs. Recommender Systems provide such support for selecting products, yet their direct application to Software Services is not straightforward. In this paper, we derive three requirements for Software service recommender systems and then propose a hybrid recommendation approach to address these requirements and provide effective recommendations in conditions of scarce user feedback. The approach combines semantic Content-based reasoning and context-dependent Collaborative Filtering. The paper ends with the experiments based on a realistic set of semantic Services against existing approaches, demonstrating how our approach can produce effective recommendation using semantic reasoning over service specifications.

David Upton - One of the best experts on this subject based on the ideXlab platform.

  • team familiarity role experience and performance evidence from indian Software Services
    IEEE Engineering Management Review, 2012
    Co-Authors: Bradley R Staats, Robert S Huckman, David Upton
    Abstract:

    Much of the literature on team learning views experience as a unidimensional concept captured by the cumulative production volume of, or the number of projects completed by, a team. Implicit in this approach is the assumption that teams are stable in their membership and internal organization. In practice, however, such stability is rare, as the composition and structure of teams often changes over time or between projects. In this paper, we use detailed data from an Indian Software Services firm to examine how such changes may affect the accumulation of experience within, and the performance of, teams. We find that the level of team familiarity (i.e., the average number of times that each member has worked with every other member of the team) has a significant positive effect on performance, but we observe that conventional measures of the experience of individual team members (e.g., years at the firm) are not consistently related to performance. We do find, however, that the role experience of individuals in a team (i.e., years in a given role within a team) is associated with better team performance. Our results offer an approach for capturing the experience held by fluid teams and highlight the need to study context-specific measures of experience, including role experience. In addition, our findings provide insight into how the interactions of team members may contribute to the development of broader firm capabilities.

  • lean principles learning and knowledge work evidence from a Software Services provider
    Journal of Operations Management, 2011
    Co-Authors: Bradley R Staats, David James Brunner, David Upton
    Abstract:

    In this paper, we examine the applicability of lean production to knowledge work by investigating the implementation of a lean production system at an Indian Software Services firm. We first discuss specific aspects of knowledge work—task uncertainty, process invisibility, and architectural ambiguity—that call into question the relevance of lean production in this setting. Then, combining a detailed case study and empirical analysis, we find that lean Software projects perform better than non-lean Software projects at the company for most performance outcomes. We document the influence of the lean initiative on internal processes and examine how the techniques affect learning by improving both problem identification and problem resolution. Finally, we extend the lean production framework by highlighting the need to (1) identify problems early in the process and (2) keep problems and solutions together in time, space, and person.

  • team familiarity role experience and performance evidence from indian Software Services
    Management Science, 2009
    Co-Authors: Robert S Huckman, Bradley R Staats, David Upton
    Abstract:

    Much of the literature on team learning views experience as a unidimensional concept captured by the cumulative production volume of, or the number of projects completed by, a team. Implicit in this approach is the assumption that teams are stable in their membership and internal organization. In practice, however, such stability is rare, because the composition and structure of teams often change over time (e.g., between projects). In this paper, we use detailed data from an Indian Software Services firm to examine how such changes may affect the accumulation of experience within, and the performance of, teams. We find that the level of team familiarity (i.e., the average number of times that each member has worked with every other member of the team) has a significant positive effect on performance, but we observe that conventional measures of the experience of individual team members (e.g., years at the firm) are not consistently related to performance. We do find, however, that the role experience of individuals in a team (i.e., years in a given role within a team) is associated with better team performance. Our results offer an approach for capturing the experience held by fluid teams and highlight the need to study context-specific measures of experience, including role experience. In addition, our findings provide insight into how the interactions of team members may contribute to the development of broader firm capabilities.

Abdelkader Gouaich - One of the best experts on this subject based on the ideXlab platform.