Data-Intensive Systems, Knowledge Management, and Software Engineering

2021 ◽  
pp. 1-40
Author(s):  
Bruce R. Maxim ◽  
Matthias Galster ◽  
Ivan Mistrik ◽  
Bedir Tekinerdogan
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Data Intensive

PrEstoCloud

2021 ◽  
Vol 34 (1) ◽  
pp. 66-85
Author(s):  
Yiannis Verginadis ◽  
Dimitris Apostolou ◽  
Salman Taherizadeh ◽  
Ioannis Ledakis ◽  
Gregoris Mentzas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Software Engineering ◽  
Response Time ◽  
Fog Computing ◽  
Data Sources ◽  
Data Intensive ◽  
Service Response Time ◽  
Enabling Services ◽  
Data Intensive Applications ◽  
Multi Cloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

The Business Transformation Framework and Enterprise Architecture Framework for Managers in Business Innovation

2019 ◽  
pp. 20-49
Author(s):  
Antoine Trad
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Enterprise Architecture ◽  
Global Business ◽  
Business Transformation ◽  
Knowledge Based ◽  
Architecture Framework ◽  
Enterprise Architecture Framework ◽  
Global Software Engineering ◽  
Technical Recommendations

The KMGSE offers a real-life case for detecting and processing an enterprise knowledge management model for global business transformation, knowledge management systems, global software engineering, global business engineering and enterprise architecture recurrent problems solving. This global software engineering (GSE) subsystem is a driven development model that offers a set of possible solutions in the form of architecture, method, patterns, managerial and technical recommendations, coupled with an applicable framework. The proposed executive and technical recommendations are to be applied by the business environment's knowledge officers, architects, analysts and engineers to enable solutions to knowledge-based, global software engineering paradigms' development and maintenance.

Qualitative Analysis of Semantically Enabled Knowledge Management Systems in Agile Software Engineering

2012 ◽  
pp. 144-164
Author(s):  
Jörg Rech ◽  
Christian Bogner
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Qualitative Analysis ◽  
Management Systems ◽  
Knowledge Management Systems ◽  
Two Phase ◽  
Complex Processes ◽  
Management Processes ◽  
Agile Software Engineering ◽  
Agile Software

In many agile software engineering organizations there is not enough time to follow knowledge management processes, to retrieve knowledge in complex processes, or to systematically elicit knowledge. This chapter gives an overview about the human-centered design of semantically-enabled knowledge management systems based on Wikis used in agile software engineering environments. The methodology – developed in the RISE (Reuse in Software Engineering) project – enables and supports the design of human-centered knowledge sharing platforms, such as Wikis. Furthermore, the paper specifies requirements one should keep in mind when building human-centered systems to support knowledge management. A two-phase qualitative analysis showed that the knowledge management system acts as a flexible and customizable view on the information needed during working-time which strongly relieves software engineers from time-consuming retrieval activities. Furthermore, the observations gave some hints about how the software system supports the collection of vital working experiences and how it could be subsequently formed and refined.

Combining Software Engineering Elicitation Technique with the Knowledge Management Lifecycle

2012 ◽  
Vol 3 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Diana-Marcela Vásquez-Bravo ◽  
Maria-Isabel Sánchez-Segura ◽  
Fuensanta Medina-Domínguez ◽  
Antonio Amescua
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Knowledge Creation ◽  
Knowledge Elicitation ◽  
Elicitation Process ◽  
The Relationship ◽  
Epistemological Dimension

Knowledge elicitation process allows acquiring and transferring the knowledge. This process presents difficulties to select the appropriate elicitation technique. This paper presents a classification of the elicitation techniques used in software engineering and the relationship between the elicitation techniques and some elements of knowledge management as assets knowledge, epistemological dimension of knowledge and the knowledge creation phases. This classification provides a guideline to select a technique or a set of techniques for knowledge elicitation based on phases of Nonaka’s model.

Metrics for Concept-Oriented Knowledge Bases

1998 ◽  
Vol 08 (02) ◽  
pp. 161-188 ◽  
Author(s):  
Timothy C. Lethbridge
Keyword(s):  
Knowledge Management ◽  
Software Engineering ◽  
Knowledge Base ◽  
Management System ◽  
Software Metrics ◽  
Knowledge Engineering ◽  
Knowledge Bases ◽  
Knowledge Management System ◽  
The Other ◽  
Develop Software

Metrics are widely researched and used in software engineering; however there is little analogous work in the field of knowledge engineering. In other words, there are no widely-known metrics that the developers of knowledge bases can use to monitor and improve their work. In this paper we adapt the GQM (Goals-Questions-Metrics) methodology that is used to select and develop software metrics. We use the methodology to develop a series of metrics that measure the size and complexity of concept-oriented knowledge bases. Two of the metrics measure raw size; seven measure various aspects of complexity on scales of 0 to 1, and are shown to be largely independent of each other. The remaining three are compound metrics that combine aspects of the other nine in an attempt to measure the overall 'difficulty' or 'complexity' of a knowledge base. The metrics have been implemented and tested in the context of a knowledge management system called CODE4.

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