Michal Munk

Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor

Abstract Presumptions of each data analysis are data themselves, regardless of the analysis focus (visit rate analysis, optimization of portal, personalization of portal, etc.). Results of selected analysis highly depend on the quality of analyzed data. In case of portal usage analysis, these data can be obtained by monitoring web server log file. We are able to create data matrices and web map based on these data which will serve for searching for behaviour patterns of users. Data preparation from the log file represents the most time-consuming phase of whole analysis. We realized an experiment so that we can find out to which criteria are necessary to realize this time-consuming data preparation. We aimed at specifying the inevitable steps that are required for obtaining valid data from the log file. Specially, we focused on the reconstruction of activities of the web visitor. This advanced technique of data preprocessing belongs to time consuming one. In the article we tried to assess the impact of reconstruction of activities of a web visitor on the quantity and quality of the extracted rules which represent the web users’ behaviour patterns.

Data Pre-processing Evaluation for Text Mining: Transaction/Sequence Model

Abstract Data pre-processing presents the most time consuming phase in the whole process of knowledge discovery. The complexity of data pre-processing depends on the data sources used. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in e-documents. We used the transaction/sequence model for text representation and sequence rule analysis as a method of modelling. We compare four datasets of different quality obtained from texts and pre-processed in different ways: data with identified the paragraph sequences, data with identified the sentence sequences, data with identified the paragraph sequences without stop words and data with identified the sentence sequences without stop words. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules. The results confirm some initial assumptions, but they also show that the stop words removal has a substantial impact on the quantity and quality of extracted rules in case of paragraph sequence identification. Contrary, in case of sentence sequence identification, removing the stop words has not any significant impact on the quantity and quality of extracted rules.

Impact of Different Pre-Processing Tasks on Effective Identification of Users’ Behavioral Patterns in Web-based Educational System

Abstract Analyzing the unique types of data that come from educational systems can help find the most effective structure of the elearning courses, optimize the learning content, recommend the most suitable learning path based on students behavior, or provide more personalized environment. We focus only on the processes involved in the data preparation stage of web usage mining. Our objective is to specify the inevitable steps that are required for obtaining valid data from the stored logs of the webbased educational system. We compare three datasets of different quality obtained from logs of the web-based educational system and pre-processed in different ways: data with identified users’ sessions and data with the reconstructed path among course activities. We try to assess the impact of these advanced techniques of data pre-processing on the quantity and quality of the extracted rules that represent the learners’ behavioral patterns in a web-based educational system. The results confirm some initial assumptions, but they also show that the path reconstruction among visited activities in e-leaning course has not statistically significant effect on quality and quantity of the extracted rules.

Influence of Different Session Timeouts Thresholds on Results of Sequence Rule Analysis in Educational Data Mining

The purpose of using web usage mining methods in the area of learning management systems is to reveal the knowledge hidden in the log files of their web and database servers. By applying data mining methods to these data, interesting patterns concerning the users’ behaviour can be identified. They help us to find the most effective structure of the e-learning courses, optimize the learning content, recommend the most suitable learning path based on student’s behaviour, or provide more personalized environment. We prepare six datasets of different quality obtained from logs of the learning management system and pre-processed in different ways. We use three datasets with identified users’ sessions based on 15, 30 and 60 minute session timeout threshold and three another datasets with the same thresholds including reconstructed paths among course activities. We try to assess the impact of different session timeout thresholds with or without paths completion on the quantity and quality of the sequence rule analysis that contribute to the representation of the learners’ behavioural patterns in learning management system. The results show that the session timeout threshold has significant impact on quality and quantity of extracted sequence rules. On the contrary, it is shown that the completion of paths has neither significant impact on quantity nor quality of extracted rules.

Probability modeling of accesses to the web parts of portal

Abstract The analysis of behavior of portal visitors is one of the most important parts of web portal optimization. The results of the analysis are important for the further correction and improvement of web part organization. The aim of the paper is modeling of probabilities‘ accesses to the categories of web parts of portal. We deal with the access probabilities to the individual categories of faculty portal content depending on the day’s hour and the week’s day. The probabilities are estimated using multinomial logit model for employees and students separately. In logit models, in case of students and employees, the week’s days present statistically significant signs, representing dummy variables (MON, TUE,…) in the model. On the other hand, day’s hours representing with variables HOUR_DAY and their square HOUR_DAY_Q, are shown as statistically significant signs only in the case of students. These results correspond with the computing probabilities wherein the probabilities of access to web parts of the portal are more stable in the case of employees than of students during the day. The analysis provided us several interesting and surprising results. For instance, from the analysis, results follow that the part study is the most visited part by students in the evening and night hours. The analysis results confirmed general trends, for example the part announcements is the most visited part in morning’s hours, at the beginning of the week especially. All of the analysis results will help us to further optimize our web portal. This is especially point in level of portal adaptivity on the basis user and access hour on portal.

Probability modelling of accesses to the course activities in the web-based educational system

The aim of the paper is the probability modelling of accesses to the categories of activities of e-learning course in learning management system. We are concerned with the access probabilities to individual activities of e-learning course content depending on the part of the week (workweek and weekend). The probabilities are estimated through multinomial logit model. We pay attention to data preparation issues. We describe used model in more detail and deal with parameter estimations. Finally, we figure that the multinomial logit model finds its application mainly in the process of restructuring the existing e-learning courses. We discuss about its possible contribution to the improvement of the learning management as well as in the personalization of the course content and structure.

Usage analysis in e-learning system for healthcare

Education and possible further studies have become a rather demanding investment. At the time of imperfect technology, i.e. technology of the previous period, it was a very difficult and doubtable task. But, is an ordinary use of new technologies a formula for the solution? We shall try to adequately answer this question. In our contribution we shall introduce educational activities (e-courses) based on cooperation and experimentation within the combined form of education. All presented information was implemented and verified in the combined forms of education. The authors present a detailed analysis of the user log-on data, on which we can better understand the behavior of the student in an electronic learning environment.

Influence of Stop-Words Removal on Sequence Patterns Identification within Comparable Corpora

Short texts like advertisements are characterised by a number of slogans, phrases, words, symbols etc. To improve the quality of textual data, it is necessary to filter out noise textual data from important data. The aim of this work is to determine to what extent it is necessary to carry out the time consuming data pre-processing in the process of discovering sequential patterns in English and Slovak advertisement corpora. For this purpose, an experiment was conducted focusing on data pre-processing in these two comparable corpora. We try to find out to what extent removing the stop words has an influence on a quantity and quality of extracted rules. Stop words removal has no impact on the quantity and quality of extracted rules in English as well as in Slovak advertisement corpora. Only language has a significant impact on the quantity and quality of extracted rules.

IPv4/IPv6 Performance Analysis: Transport Layer Protocol Impact to Transmission Time

Simulation modeling of computer networks is an effective technique for evaluating the performance of network and data transfer with various internet protocols. In this paper we are focusing on the impact of used transport layer protocol transferred with new internet protocol (IPv6) against network performance, especially network router load and efficiency. Higher values of load or worse efficiency can be a problem in global IPv6 deployment. We propose methodology of traffic measurement based on different stochastic systems. We are able to generate different TCP, UDP and SCTP traffic according to uniform probability distribution for packet inter departure time and packet size. We use a generator that is able to reproduce experiment by using the same seed for random values and we focus on the correlation of packet size, inter departure time to router and communicating nodes load. All experiments were made in pure IPv4 environment and in native IPv6 environment too. We expected that the use of a new protocol lead to higher efficiency of data transfer. By using forward stepwise regression analysis we identify which variables imply the packet transfer time.

Modelling of Language Processing Dependence on Morphological Features

The order, association and variability of the advertising language is different in every language and culture, because it is based on different rules in the given culture. Therefore, the study is focused on comparative linguistic data analysis of advertisements written in Slovak and English randomly collected from online sources. The transaction/sequence model for text representation was used and an association rules analysis was applied as the research method. The results are significant mainly in terms of the differences in the incidence of parts of speech in English and Slovak written advertisements. Based on the morphological features of the examined languages, different models of language of advertising were being created.