The health of modern organizations depends on many complex business and operational processes that are impossible to monitor and manage manually. A disruption to these systems can cost millions of dollars to enterprises and jeopardize customer satisfaction and loyalty. Until recently, few analytical tools could scan through humungus data, detect subtle changes, and proactively notify users about issues that might adversely affect business outcomes. They continuously detect anomalies, trends, and correlations and present individuals with a handful of most relevant insights. However, only monitoring tools are not adequate to analyse and detect the root causes interm of a performance issue. This paper proposes an approach that is based on 3 core pillars that are asis analysis of business systems, static analysis, and dynamic analysis.
Modern bapplications carry a heavy burden: they are required to deliver high performance and are featured rich. They must provide personalization to users, assemble information from disparate and data sources, and process data intelligently depending on user requests. These applications are fragmented into multiple operations executed for the same request, the results of which are assembled into a structured response to the user. For example, the request to view an account balance may require checking for a valid session, querying a backend database, and finally putting together an HTML response leveraging the results from the database query. As some of these operations are time-consuming, such dynamic applications have high inherent costs in performance and scalability. This paper will also discuss the different technique for building high performance business applications.
Article Details
Unique Paper ID: 150657
Publication Volume & Issue: Volume 7, Issue 8
Page(s): 196 - 203
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