Author:
M. Omair Shafiq
Affiliation:
Carleton University, Canada
Keyword(s):
Semantics, Streaming Clustering, Integrated Analytics, Application Execution and Management.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Cloud Computing
;
Coupling and Integrating Heterogeneous Data Sources
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Modeling of Distributed Systems
;
Ontologies and the Semantic Web
;
Semantic Web Technologies
;
Sensor Networks
;
Services Science
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Software Agents and Internet Computing
;
Web Information Systems and Technologies
Abstract:
Large-scale software applications produce enormous amount of execution data in the form of logs which makes it challenging for managing execution of such applications. There have been several semantically enhanced analytical solutions proposed for enhanced monitoring and management of software applications. In this paper, author proposes a customized semantic model for representing application execution, and a scalable stream clustering based processing solution. The stream clustering based approach acts as key to combine all the other analytical solutions using the proposed customized semantic model for logs. The proposed approach works in an integrated manner that clusters log data that is produced, as a result of events occurring during execution, at a large-scale and in a continuous streaming manner for managing execution of software applications. The proposed solution utilizes semantics for better expressiveness of log events, other related data and analytical approaches, throug
h stream clustering based integrated approach, to process logs that helps in enhancing the process of monitoring and management of software applications. This paper presents the customized semantic logging model for scalable stream clustering, algorithm design and discussion on scalable stream clustering based solution and its integration with other analytical solutions. The paper also presents experimentation, evaluation and demonstrates applicability of the proposed solution.
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