scholarly journals Spotify Guilds: How to Succeed With Knowledge Sharing in Large-Scale Agile Organizations

IEEE Software ◽  
2019 ◽  
Vol 36 (2) ◽  
pp. 51-57 ◽  
Author(s):  
Darja Smite ◽  
Nils Brede Moe ◽  
Georgiana Levinta ◽  
Marcin Floryan
Keyword(s):  
Knowledge Sharing ◽  
Large Scale ◽  
Agile Organizations

An ontology-driven framework for enhancing reusability of distributed simulation modeling of industrial construction processes

2013 ◽  
Vol 40 (9) ◽  
pp. 917-926 ◽  
Author(s):  
Farzaneh Saba ◽  
Yasser Mohamed
Keyword(s):  
Knowledge Sharing ◽  
Simulation Modeling ◽  
Large Scale ◽  
Distributed Simulation ◽  
World View ◽  
Simulation Models ◽  
Construction Simulation ◽  
Construction Process ◽  
Industrial Construction ◽  
Ontological Modeling

This paper describes an ontology-driven framework for developing distributed simulation modeling of construction processes. The research described in the paper is motivated by the necessity of knowledge sharing between distributed simulation modeling collaborators, and reuse and portability challenges in construction simulation models. Our approach addresses these challenges through ontological modeling and linking of construction simulation modeling components including (i) ontology of construction process, (ii) ontology of simulation world view, and (iii) ontology of distributed simulation modeling application tool. Within the paper, ontology driven approach and mapping of ontologies for information transference between simulation components has been described. Another discussed application of ontologies is structuring of simulation modeling development through use of reusable elements. A large-scale distributed simulation model of industrial construction processes has been outlined to illustrate the application of the approach.

scholarly journals An Integrated Platform Supporting Semantic Similarity Score Calculation and Reproducibility

2021 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Kenneth Opap ◽  
Funmilayo Makinde ◽  
Victoria Nembaware ◽  
Francis Agamah ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Knowledge Sharing ◽  
Semantic Similarity ◽  
Automated Reasoning ◽  
Large Scale ◽  
Essential Role ◽  
Similarity Score ◽  
File Format ◽  
Flexible Tool ◽  
Integrated Platform

Abstract During the last decade, we witnessed an exponential rise of datasets from heterogeneous sources. Ontologies are playing an essential role in consistently describing domain concepts, data harmonization and integration to support large-scale integrative analysis and semantic interoperability in knowledge sharing. Several semantic similarity (SS) measures have been suggested to enable the integration of rich ontology structures into automated reasoning and inference. However, there is no tool that exhaustively implements these measures and existing tools are generally Gene Ontology specific, do not implement several models suggested in the WordNet context and are not equipped to properly deal with frequent ontology updates. We introduce a Python SS measure library (PySML), which tackles issues related to current SS tools, providing a portable and expandable tool to a broad computational audience. This empowers users to manipulate SS scores from several applications for any ontology version and file format. PySML is a flexible tool enabling the implementation of all existing semantic similarity models, resolving issues related to computation, reproducibility and re-usability of SS scores.

scholarly journals Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing

2018 ◽  
Author(s):  
Osman Ramadan ◽  
Paweł Budzianowski ◽  
Milica Gašić
Keyword(s):  
Knowledge Sharing ◽  
Large Scale

scholarly journals Using Objectives and Key Results (OKRs) and Slack: A Case Study of Coordination in Large-Scale Distributed Agile

2021 ◽  
Author(s):  
Viktoria Stray ◽  
Nils Brede Moe ◽  
Henrik Vedal ◽  
Marthe Berntzen
Keyword(s):  
Problem Solving ◽  
Knowledge Sharing ◽  
Goal Setting ◽  
Large Scale ◽  
Ad Hoc ◽  
Relational Coordination ◽  
Software Projects ◽  
Coordination Theory ◽  
Working From Home

Today, many large-scale software projects have members working from home, which has changed the way teams coordinate work. To better understand coordination in this setting, we conducted a case study through which we examined two teams in a large-scale agile project by observing meetings and conducting 17 interviews. Through the lens of Relational Coordination Theory (RCT), we analyzed the use of the goal-setting framework Objectives and Key Results (OKRs) and the collaboration tool Slack. Slack was used for frequent, timely, and problem-solving communication and, and its use decreased the number of planned meetings. However, discussions often started on Slack and continued in virtual ad-hoc meetings. The use of OKRs facilitated knowledge sharing, helped the teams align their goals, and provided inter-team insights. The main implication of our research is that projects using OKRs need to support project members, especially in formulating the key results that align and motivate the teams to work toward the same mission.

scholarly journals Innovative Practices for Knowledge Sharing in Large-Scale DevOps

IEEE Software ◽  
2020 ◽  
Vol 37 (3) ◽  
pp. 30-37
Author(s):  
Aymeric Hemon ◽  
Brian Fitzgerald ◽  
Barbara Lyonnet ◽  
Frantz Rowe
Keyword(s):  
Knowledge Sharing ◽  
Large Scale

scholarly journals Inventors, Patents, and Inventive Activities in the English Brewing Industry, 1634–1850

2013 ◽  
Vol 87 (1) ◽  
pp. 95-120 ◽  
Author(s):  
Alessandro Nuvolari ◽  
James Sumner
Keyword(s):  
Nineteenth Century ◽  
Knowledge Sharing ◽  
Technological Innovation ◽  
Open Innovation ◽  
Large Scale ◽  
Patent System ◽  
Brewing Industry ◽  
Apparent Paradox ◽  
Market For Technology ◽  
The Relationship

This article examines the relationship between patents, appropriability strategies, and market for technology in the English brewing industry before 1850. Previous research has pointed to the apparent paradox that large-scale brewing in this period showed both a self-aware culture of rapid technological innovation and a remarkably low propensity to patent. Our study records how brewery innovators pursued a wide variety of highly distinct appropriability strategies, including secrecy, selective revealing, open innovation and knowledge-sharing for reputational reasons, and patenting. All these strategies could co-exist, although some brewery insiders maintained a suspicion of the promoters of patent technologies, which faded only in the nineteenth century. Furthermore, we find evidence that sophisticated strategies of selective revealing could support trade in inventions even without the use of the patent system.

Context-Aware Expert Finding in Tag Based Knowledge Sharing Communities

2012 ◽  
Vol 3 (1) ◽  
pp. 48-63 ◽  
Author(s):  
Hengshu Zhu ◽  
Enhong Chen ◽  
Huanhuan Cao ◽  
Jilei Tian
Keyword(s):  
Knowledge Sharing ◽  
Information Needs ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Rapid Development ◽  
Development Trend ◽  
Semantic Relationship ◽  
Real World Data ◽  
Data Set

With the rapid development of online Knowledge Sharing Communities (KSCs), the problem of finding experts becomes increasingly important for knowledge propagation and putting crowd wisdom to work. A recent development trend of KSCs is to allow users to add text tags for annotating their posts, which are more accurate than traditional category information. However, how to leverage these user-generated tags for finding experts is still underdeveloped. To this end, this paper develops a novel approach for finding experts in tag based KSCs by leveraging tag context and the semantic relationship between tags. Specifically, the extracted prior knowledge and user profiles are first used for enriching the query tags to infer tag context, which represents the user’s latent information needs. Specifically, two different approaches for addressing the problem of tag sparseness in authority ranking are proposed. The first is a memory-based collaborative filtering approach, which leverages non-negative matrix factorization (NMF) to find similar users for alleviating tag sparseness. The second approach is based on Latent Dirichlet Allocation (LDA) topic model, which can further capture the latent semantic relationship between tags. A large-scale real-world data set is collected from a tag based Chinese commercial Q&A web site. Experimental results show that the proposed method outperforms several baseline methods with a significant margin.

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