Data mining for performance monitoring and optimisation

2011 ◽  
pp. 309-344 ◽  
Author(s):  
J. Ritchie ◽  
D. Flynn
Keyword(s):  
Data Mining ◽  
Performance Monitoring

The Performance Mining Method

Big Data ◽  
2016 ◽  
pp. 181-199
Author(s):  
Stella Pachidi ◽  
Marco Spruit
Keyword(s):  
Data Mining ◽  
Financial Management ◽  
Performance Monitoring ◽  
Software As A Service ◽  
Software Performance ◽  
Mining Method ◽  
Data Mining Techniques ◽  
Software Products ◽  
Software Product ◽  
Operation Knowledge

Software Performance is a critical aspect for all software products. In terms of Software Operation Knowledge, it concerns knowledge about the software product's performance when it is used by the end-users. In this paper the authors suggest data mining techniques that can be used to analyze software operation data in order to extract knowledge about the performance of a software product when it operates in the field. Focusing on Software-as-a-Service applications, the authors present the Performance Mining Method to guide the process of performance monitoring (in terms of device demands and responsiveness) and analysis (finding the causes of the identified performance anomalies). The method has been evaluated through a prototype which was implemented for an online financial management application in the Netherlands.

scholarly journals Data Mining to Assess Organizational Transparency across Technology Processes: An Approach from IT Governance and Knowledge Management

2021 ◽  
Vol 13 (18) ◽  
pp. 10130
Author(s):  
Pedro Solana-González ◽  
Adolfo Alberto Vanti ◽  
María Matilde García Lorenzo ◽  
Rafael E. Bello Pérez
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Internal Control ◽  
Information Quality ◽  
Performance Monitoring ◽  
It Governance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Technology Processes ◽  
Organizational Transparency

Information quality and organizational transparency are relevant issues for corporate governance and sustainability of companies, as they contribute to reducing information asymmetry, decreasing risks, and improving the conduct of decision-makers, ensuring an ethical standard of organizational control. This work uses the COBIT framework of IT governance, knowledge management, and machine learning techniques to evaluate organizational transparency considering the maturity levels of technology processes applied in 285 companies of southern Brazil. Data mining techniques have been methodologically applied to analyze the 37 processes in four different domains: Planning and organization, acquisition and implementation, delivery and support, and monitoring. Four learning techniques for knowledge discovery have been used to build a computational model that allowed us to evaluate the organizational transparency level. The results evidence the importance of IT performance monitoring and assessment, and internal control processes in enabling organizations to improve their levels of transparency. These processes depend directly on the establishment of IT strategic plans and quality management, as well as IT risk and project management, therefore an improvement in the maturity of these processes implies an increase in the levels of organizational transparency and their reputational, financial, and accountability impact.

scholarly journals Designing Single Slop Single Basin Solar Water Distillation Plant Performance Monitoring and Prediction System

2019 ◽  
Vol 9 (1) ◽  
pp. 2445-2451
Keyword(s):  
Data Mining ◽  
Performance Monitoring ◽  
Plant Performance ◽  
Prediction System ◽  
Sample Collection ◽  
Water Distillation ◽  
Solar Water ◽  
Solar Distillation ◽  
Mining Model ◽  
Distillation Plant

the issue of the water crisis is rising day by day, due to global warming and other environmental effects. That is not only the issue for India, but it is also for the entire world. However, the solar-based water distillation plants are not much efficient but we can use this method for producing pure and drinkable water. In this paper, we proposed to design a solar water distillation plant using the single slop method. In addition to that for monitoring and measuring the performance of the distillation plant a data mining based prediction system is implemented. The experiments are performed on the real-world implemented single slop solar water distillation plant-based observations. The observations are collected using the IoT (Internet of things) device for each five-minute time difference for each sample collection. The data samples are collected between 10:00 AM to 4:00 PM for 7 days. Additionally by using the collected samples the data mining model is trained and tested on the prepared syntactic dataset. The experimental results demonstrate accurate predictions for the solar distillation water plant. After this implementation and system model, the future directions of the research are also provided.

Data Warehousing and Data Mining Lessons for EC Companies

2008 ◽  
pp. 381-388
Author(s):  
Neerja Sethi ◽  
Vijay Sethi
Keyword(s):  
Data Mining ◽  
Performance Monitoring ◽  
Data Warehousing ◽  
Lessons Learned ◽  
Customer Relationship ◽  
Fine Tuning ◽  
Management Support ◽  
Web Performance ◽  
Unified View ◽  
Customer Profiles

Internet companies are now in the second stage of evolution in which the emphasis is on building brands (Campman, 2001) and retaining customers rather than just transactions. There is also an imperative for multidimensional Web performance monitoring (Earls, 2005) and a continual fine-tuning of sites for optimal navigation, increased stickiness and transactional efficiency. Such research as the relationship between customer profiles and navigational characteristics (Garatti, Sergio, Sergio, & Broccab, 2004) and techniques for seamlessly aggregating Web data with corporate data (Wood & Ow, 2005) also testify to the importance of holistic data analysis for knowledge discovery. The technologies that are becoming critical in this fight for customer retention are data warehousing, data mining and customer relationship management. This article presents two case studies, one on data warehousing and the other on data mining, to draw some very specific lessons about management support, organizational commitment and overall implementation of such projects. These lessons complement past recommendations that these technologies are more about organization change (Kale, 2004), about a single unified view of the business and, ultimately about building a shared data model of the enterprise. We start with a brief overview of data warehousing and data mining. The two cases are discussed next, using a similar analytical structure to facilitate comparison among them. In the conclusion, we describe the key lessons learned from the two cases and implication

Data Warehousing and Data Mining Lessons for EC Companies

2011 ◽  
pp. 172-176
Author(s):  
Neerja Sethi ◽  
Vijay Sethi
Keyword(s):  
Data Mining ◽  
Performance Monitoring ◽  
Data Warehousing ◽  
Lessons Learned ◽  
Customer Relationship ◽  
Fine Tuning ◽  
Management Support ◽  
Web Performance ◽  
Unified View ◽  
Customer Profiles

Internet companies are now in the second stage of evolution in which the emphasis is on building brands (Campman, 2001) and retaining customers rather than just transactions. There is also an imperative for multidimensional Web performance monitoring (Earls, 2005) and a continual fine-tuning of sites for optimal navigation, increased stickiness and transactional efficiency. Such research as the relationship between customer profiles and navigational characteristics (Garatti, Sergio, Sergio, & Broccab, 2004) and techniques for seamlessly aggregating Web data with corporate data (Wood & Ow, 2005) also testify to the importance of holistic data analysis for knowledge discovery. The technologies that are becoming critical in this fight for customer retention are data warehousing, data mining and customer relationship management. This article presents two case studies, one on data warehousing and the other on data mining, to draw some very specific lessons about management support, organizational commitment and overall implementation of such projects. These lessons complement past recommendations that these technologies are more about organization change (Kale, 2004), about a single unified view of the business and, ultimately about building a shared data model of the enterprise. We start with a brief overview of data warehousing and data mining. The two cases are discussed next, using a similar analytical structure to facilitate comparison among them. In the conclusion, we describe the key lessons learned from the two cases and implication

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