Continuous Architectural Knowledge Integration: Making Heterogeneous Architectural Knowledge Available in Large-Scale Organizations

2017 ◽  
Author(s):  
Juergen Musil ◽  
Fajar J. Ekaputra ◽  
Marta Sabou ◽  
Tudor Ionescu ◽  
Daniel Schall ◽  
...  
Keyword(s):  
Large Scale ◽  
Knowledge Integration ◽  
Architectural Knowledge

scholarly journals Knowledge Integration Networks for Action Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12862-12869
Author(s):  
Shiwen Zhang ◽  
Sheng Guo ◽  
Limin Wang ◽  
Weilin Huang ◽  
Matthew Scott
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Knowledge Integration ◽  
Scene Recognition ◽  
Teacher Networks ◽  
Medium Level ◽  
Meaningful Context ◽  
Context Knowledge ◽  
High Level ◽  
Context Features

In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition. KINet is capable of aggregating meaningful context features which are of great importance to identifying an action, such as human information and scene context. We design a three-branch architecture consisting of a main branch for action recognition, and two auxiliary branches for human parsing and scene recognition which allow the model to encode the knowledge of human and scene for action recognition. We explore two pre-trained models as teacher networks to distill the knowledge of human and scene for training the auxiliary tasks of KINet. Furthermore, we propose a two-level knowledge encoding mechanism which contains a Cross Branch Integration (CBI) module for encoding the auxiliary knowledge into medium-level convolutional features, and an Action Knowledge Graph (AKG) for effectively fusing high-level context information. This results in an end-to-end trainable framework where the three tasks can be trained collaboratively, allowing the model to compute strong context knowledge efficiently. The proposed KINet achieves the state-of-the-art performance on a large-scale action recognition benchmark Kinetics-400, with a top-1 accuracy of 77.8%. We further demonstrate that our KINet has strong capability by transferring the Kinetics-trained model to UCF-101, where it obtains 97.8% top-1 accuracy.

How does coopetition affect radical innovation? The roles of internal knowledge structure and external knowledge integration

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huanhuan Chen ◽  
Yanhong Yao ◽  
Ao Zan ◽  
Elias G. Carayannis
Keyword(s):  
Knowledge Integration ◽  
Knowledge Structure ◽  
Radical Innovation ◽  
Content Type ◽  
External Knowledge ◽  
Mediating Role ◽  
Architectural Knowledge ◽  
The Relationship ◽  
Positive Effect

Purpose Building on the resource- and knowledge-based views, this paper aims to explore how coopetition affects radical innovation and the roles of knowledge structure and external knowledge integration in the relationship between coopetition and radical innovation. Design/methodology/approach This study proposes a research model to examine the mediating role of external knowledge integration on the coopetition-radical innovation link, where the mediation is moderated by the firm’s knowledge structure (including component knowledge and architectural knowledge). The authors use regression and bootstrapping to test the proposed model with survey data from 241 Chinese technology firms. Findings This study finds that coopetition positively affects radical innovation and the effect is fully mediated by external knowledge integration. Additionally, component knowledge negatively moderates the coopetition-external knowledge integration link and architectural knowledge positively moderates this relationship. Further, the mediating effect of external knowledge integration is also moderated by component knowledge and architectural knowledge. Practical implications Firms should engage in coopetition to promote radical innovation. Further, it is necessary for firms to appropriately manage coopetition according to their internal knowledge structure. Originality/value This study explains why scholars have different ideas about the relationship between coopetition and radical innovation by exploring the mediating role of external knowledge integration and the moderating effect of knowledge structure. Firms possess increased possibilities for knowledge leakage and partner opportunism with high levels of component knowledge, which will reduce the positive effect coopetition on external knowledge integration; thus, they are less likely to realize radical innovation. Instead, firms possess increased opportunities for resource sharing with high levels of architectural knowledge, thus improving the positive effect coopetition on external knowledge integration and they are more likely to achieve radical innovation.

scholarly journals A Dialogic Perspective on Managing Knowledge Differences: Problem-Solving while Building a Nuclear Power Plant Safety System

2021 ◽  
pp. 017084062110618
Author(s):  
Chia-Yu Kou ◽  
Sarah Harvey
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Large Scale ◽  
Knowledge Integration ◽  
Safety System ◽  
Problem Situation ◽  
Plant Safety ◽  
Nuclear Power Plant Safety ◽  
Large Scale Project

To manage knowledge differences, existing research has documented two sets of practices: traversing and transcending knowledge boundaries. What research has yet to explore, however, is the dynamics through which traversing or transcending practices emerge in response to a particular problem situation. Using a qualitative, inductive study of the problem episodes encountered by groups of experts working on a large-scale project to build the safety system for a nuclear power plant, we observed that the emergence of traversing or transcending depended on how experts interpreted problems and initiated dialogues around specific problems. Our work provides insight into the condition through which knowledge integration trajectories may emerge.

scholarly journals A Systematic Review of Ontology-Based River Streamflow and Flood Data Management Challenges

2021 ◽  
Vol 19 (1) ◽  
pp. 40-50
Author(s):  
Muhammad H. Mughal ◽  
Zubair A. Shaikh ◽  
Zahid H. Khand ◽  
Asif Rajput ◽  
Faheem Akhtar
Keyword(s):  
Systematic Review ◽  
Water Flow ◽  
Water Distribution ◽  
Large Scale ◽  
Knowledge Integration ◽  
Limiting Factors ◽  
Contextual Knowledge ◽  
Domain Specific ◽  
Management Perspective ◽  
Spatio Temporal

The management of the riverine water has always remained an open challenge. The variation of water flow creates hurdles to determine the exact time and the quantity of water flow caused by the spatio-temporal complex streamflow and flood risk reduction domain. From a management perspective, irregular flow patterns generate various challenges and the development of irrigation water distribution schemes without contextual knowledge integration adversely affect the relevant community. The river streamflow and flood mitigation domains are interdisciplinary that require coordination from the various stakeholders. Coordination limiting factors includes native data acquisition methodology of each stakeholder for their specific needs, the complexity of the domain involving a heterogeneous group of managers, spatio-temporal context, region-specific terminologies, data sharing, and reusability support. Earlier proposed research and developed ontologies by the esteemed researchers focused to address these challenges in a domain-specific context. In this research, we review the challenges of a large scale spatio-temporal system for streamflow of watersheds and flood disaster management based on the ontological semantic models. This research also examines the proposed ontological models for streamflow and/or flood domain, and how they address such challenges. Furthermore, a systematic review of the last two decades’ research articles is conducted and the findings are presented to assess the mappings of the challenges to proposed solutions through ontological modeling for streamflow and flood domain.

scholarly journals A Domain Specific Language for Lessening the Effort Needed to Instantiate Applications Using GRENJ Framework

2010 ◽  
Author(s):  
Vinícius Durelli ◽  
Rafael Durelli ◽  
Simone De Sousa Borges ◽  
Rosana Braga
Keyword(s):  
Large Scale ◽  
Source Code ◽  
Domain Specific Language ◽  
Specific Language ◽  
Domain Specific ◽  
Architectural Knowledge

GRENJ is a white-box framework implemented in Java. White-box frameworks are reusable designs composed of a set of concrete and abstract classes so that the collaboration among these classes provides support for large-scale reuse of design and source code. However, instantiating applications by using this sort of framework is quite complex and demands detailed architectural knowledge. In order to lessen the amount of source code, effort, and expertise required to instantiate applications by using GRENJ framework, we have developed a domain specific language that manages all application instantiation issues systematically. This domain specific language facilitates the application instantiation process by acting as a facade over GRENJ framework as well as providing the user with a more concise, human-readable syntax than Java. In this paper, we contrast the major differences and benefits resulting from instantiating applications solely using GRENJ framework and indirectly reusing its source code by applying our domain specific language.

scholarly journals RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

2016 ◽  
Author(s):  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Binding Sites ◽  
Domain Knowledge ◽  
Large Scale ◽  
Knowledge Integration ◽  
State Of The Art ◽  
Binding Motifs ◽  
Abstraction Level ◽  
Cross Domain

AbstractBackgroundRNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation.ResultsIn viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications.ConclusionThe iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep

scholarly journals An architecture governance approach for Agile development by tailoring the Spotify model

AI & Society ◽  
2021 ◽  
Author(s):  
Abdallah Salameh ◽  
Julian M. Bass
Keyword(s):  
Software Architecture ◽  
Large Scale ◽  
Agile Development ◽  
The Novel ◽  
Software Product ◽  
Open Coding ◽  
Architectural Decision ◽  
Architectural Knowledge ◽  
Embedded Case Study

AbstractThe role of software architecture in large-scale Agile development is important because several teams need to work together to release a single software product while helping to maximise teams’ autonomy. Governing and aligning Agile architecture across autonomous squads (i.e., teams), when using the Spotify model, is a challenge because the Spotify model lacks practices for addressing Agile architecture governance. To explore how software architecture can be governed and aligned by scaling the Spotify model, we conducted a longitudinal embedded case study in a multinational FinTech organisation. Then, we developed and evaluated an approach for architectural governance by conducting an embedded case study. The collected data was analysed using Thematic Analysis and informed by selected Grounded Theory techniques such as memoing, open coding, constant comparison, and sorting. Our approach for architectural governance comprises an organisational structure change and an architecture change management process. The benefits reported by the practitioners include devolving architectural decision-making to the operational level (i.e., Architecture Owners), enhancing architectural knowledge sharing among squads, minimising wasted effort in architectural refactoring, and other benefits. The practitioners in our case study realised an improved squad autonomy by the ability to govern and align architectural decisions. We provide two key contributions in this paper. First, we present the characteristics of our proposed architectural governance approach, its evaluation, benefits, and challenges. Second, we present how the novel Heterogeneous Tailoring model was enhanced to accommodate our architectural governance approach.

The Methodology and Environment for Modeling and Implementation in Concurrent Engineering

1993 ◽  
Author(s):  
Jianzhong Cha ◽  
Wei Guo
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Knowledge Integration ◽  
Computer Integrated Manufacturing ◽  
Design System ◽  
Concurrent Design ◽  
Design Processes ◽  
Processing Theory ◽  
Prescriptive Model ◽  
Engineering Environment

Abstract The Concurrent Design, characterized with the integration of a large scale information and knowledge environment in a Computer Integrated Manufacturing System (CIMS), will involve multidiscipline and multidomain of knowledge. This will lead to the difficulty to implement the concurrent design with the nature of complexity, integrality and systematicity in design process, which caused by the above mentioned knowledge integration. This paper, based on the fundamental theory of design processes and adopted the knowledge processing theory and techniques offered by Intelligence Engineering, has investigated: the descriptive models which represent the general framework of concurrent design processes; the cognitive models that highlights the reasoning aspect performed by group of human experts from multidisciplines in concurrent design process; the prescriptive model which is prepared for being used in an computerized automated concurrent design system; the computable model represented with the object-oriented method, which can be executed in the computer world for automated concurrent design. Also this paper developed an engineering environment of analyzing, modeling and implementing with an architecture of Integrated Intelligent Unit, borrowed from the theory of Intelligence Engineering. In a separate paper, the authors apply the above methodology to a concrete concurrent design on a mechanical system to show the feasibility and advantages of the proposed method.

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