A Bayesian Network-Based Knowledge Engineering Framework for IT Service Management

Wang, Wei; Wang, Hao; Yang, Bo; Liu, Liang; Liu, Peini; Zeng, Guosun

doi:10.1109/tsc.2011.34

Cited by 8 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These sources plus online content are the main sources of information and knowledge for network administration. The presented and other mining cases in NSM are applied on data from all these sourceslog data [64], [65], [71], [85], [89], [91], [94], measurements [15], [64], [76], traffic data [7], [8], [45] and Online data [66], [86]. When using log and traffic data, miners have leveraged various well known tools (tcpdump, tcppurify, wireshark, syslog, netflow etc) to extract NS-data.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Four Decades of Data Mining in Network and Systems Management

Kalegele

Sasai

Takahashi

et al. 2015

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

How has the interdisciplinary data mining field been practiced in Network and Systems Management (NSM)? In Science and Technology, there is a wide use of data mining in areas like bioinformatics, genetics, Web and more recently astroinformatics. However, the application in NSM has been limited and inconsiderable. In this article, we provide an account of how data mining has been applied in managing networks and systems for the past four decades, presumably since its birth. We look into the field's applications in the key NSM activities -discovery, monitoring, analysis, reporting and domain knowledge acquisition. In the end, we discuss our perspective on the issues that are considered critical for the effective application of data mining in the modern systems which are characterized by heterogeneity and high dynamism.

show abstract

Section: Discussionmentioning

confidence: 99%

“…al. [89] proposed a framework for the acquisition of service support knowledge. In the framework, Bayesian networks are built as knowledge from the Configuration Management DataBase (CMDB) and system logs.…”

Section: Domain Knowledgementioning

confidence: 99%

Four Decades of Data Mining in Network and Systems Management

Kalegele

Sasai

Takahashi

et al. 2015

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

show abstract

“…1 consists of knowledge acquisition. This consists of explicit knowledge in a document format [2], [3], [22], [25] knowledge storage is the knowledge collected by a worker group whose language represents the knowledge to be stored. Knowledge can be classified in a database system [3], [23], [25], [26] knowledge utilization is the use of knowledge through a web browser related to cloud computing.…”

Section: Methodsmentioning

confidence: 99%

“…Knowledge can be classified in a database system [3], [23], [25], [26] knowledge utilization is the use of knowledge through a web browser related to cloud computing. Only members can use the knowledge base system within the knowledge server [2], [3], [22], [23], [25].…”

Section: Methodsmentioning

confidence: 99%

“…Yang [3] explained that knowledge engineering is the process of finding knowledge through the use of a systematic structure which can be used for solving problems or making decisions. Knowledge engineering is the acquisition, structuring and refinement of knowledge, including knowledge analysis, knowledge formalization and knowledge reasoning [22]. Knowledge engineering consists of tools, techniques and processes that correspond to the knowledge system [23].…”

Section: Knowledge Engineeringmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for a Knowledge Management System in a Cloud Computing Environment Using a Knowledge Engineering Approach

Anupan¹,

Nilsook²,

Wannapiroon³

2015

IJKE

View full text Add to dashboard Cite

Abstract-This study aims to design a framework for a knowledge management system in a cloud computing environment using a knowledge engineering approach. The research comprised 2 steps: 1) analyzing and synthesizing the relevant literature with regard to knowledge management systems in a cloud computing environment using a knowledge engineering approach and 2) designing a framework for a knowledge management system in a cloud computing environment using a knowledge engineering approach. Data were analyzed using content analysis. A knowledge management system consists of four components as follows: 1) knowledge retrieval 2) knowledge storage 3) knowledge sharing and 4) knowledge publishing. The knowledge engineering approach consists of three components as follows: 1) knowledge acquisition 2) knowledge storage and 3) knowledge utilization.Index Terms-Knowledge management system, cloud computing environment, knowledge engineering.

show abstract