Knowledge Discovery from Web Usage Data: A Survey of Web Usage Pre-processing Techniques

Prasad, Guru; Reddy, Neelima G.; Acharya, Dinesh U

doi:10.1007/978-3-642-12214-9_88

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…The preprocessing of raw data facilitates the stabilization of the mean and variance and aims to provide a structural, reliable, and integrated data source, and it is a very critical and complex step, which allows guaranteeing reasonable results, whether the analysis is concerned with exploratory data mining, classification, or construction of a good and robust prediction model [17][18][19][20][21].…”

Section: Data Preparationmentioning

confidence: 99%

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Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study

et al. 2021

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The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment.

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Section: Data Preparationmentioning

confidence: 99%

“…Through good data cleaning, it is possible to improve performance and quality [17,24,25], as well as to isolate characteristics of interest and eliminate elements that "bother" the theoretical models [26].…”

Section: Data Preparationmentioning

confidence: 99%

Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study

et al. 2021

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“…Preprocessing techniques based on some heuristics is proposed in [6]. [7] and [8] A sample HTTP request for the NASA Kennedy Space Centre WWW server [10] is depicted in figure 1. The web access log line in Figure 1 shows that a user from the IP address 199.72.81.55 successfully requested the page "apollo" on 01 st July 1995 at 00:00:01 a.m.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques

Shivaprasad¹,

Reddy²,

Acharya³

2015

IJCA

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“…Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization and network traffic flow analysis and so on (Krol et al, 2008). The important concepts of Web usage mining and its various practical applications are presented in (Prasad et al, 2010). The approach called\intelligent-miner" (i-Miner) was presented in (Suneetha and Krihnamoorthi, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Web Usage Miner Using Decisive Induction Rules

Poongothai¹,

Sathiyabama²

2012

Journal of Computer Science

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Problem statement: Web usage mining is the technique of extracting useful information from server logs (users history) and finding out what users are looking for on the Internet. This type of web mining allows for the collection of Web access data for Web pages. Scope: The web usage data provides the paths leading to accessed Web pages with prefrences and higher priorities. This information is often gathered automatically into access logs through the Web server. Approach: In this study we propose Induction based decision rule model for generating inferences and implicit hidden behavioral aspects in the web usage mining which investigates at the web server and client logs. The decision based rule induction mining combines a fast decision rule induction algorithm and a method for converting a decision tree to a simplified rule set. Results: The experimentation is conducted by weka tool and the performance of proposed Induction based decision rule algorithm is evaluated in terms of mined decisive rules, Execution time, root mean square error and mean absolute error. Proposed induction rule mining needs 400 ms of execution time for decisive rule generation, whereas previous work expectation maximization algorithm needs 600ms. Conclusion: Web usage mining is evaluated with decisive rules of user page navigation and preferences. Decisive rule provide the web site developers and owners to known the site presentation likeness and demands of the web users

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Knowledge Discovery from Web Usage Data: A Survey of Web Usage Pre-processing Techniques

Cited by 6 publications

References 6 publications

Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study

Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study

Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques

Efficient Web Usage Miner Using Decisive Induction Rules

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