Using contextual information in recommender systems is a subject of continuous improvement of rating prediction accuracy. Among others, information on temporal rating dynamics contain valuable data that establish foundation for discovering changes in both individual and group user's preferences. Such changes can be caused by multiple factors such as changes of individual user interests, changes in item popularity or other hidden patterns or events. In this paper an improved user-based collaborative filtering algorithm is presented that utilizes changes of group user's preferences over time. We also investigate temporal dynamics of changes in user's preferences within different item categories and propose time weight function that improves prediction accuracy of recommender systems.

Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting prediction of ratings for individual users. In this work is presented improved user-based collaborative filtering algorithm using temporal contextual information. With difference to other approaches, we propose using weight function based on changes in the group user's preferences over time that increases prediction accuracy of collaborative filtering prediction algorithm.

This paper presents one of the approaches to improve quality of communication technology service using different techniques available today in the contest of Six Sigma and five steps of its cycle. In the scenario given in this paper telecommunication operator in its wide service portfolio is gathering a lot of information that is available through the reporting, but facing issue of defining areas of the improvement and selecting right projects and priorities for implementation. Operators today applied most of ITIL processes, but it is necessary to perform Continual Service Improvement in order to create and maintain value for customers through better service delivery. Application of Six Sigma as the complementary discipline to ITIL becomes well chosen tool to define problem, measure and analyze data, improve the process and control it after implementation. In the given example, data from many support activities are collected through longer period of time and analyzed using different statistics tool. The root cause has been identified and concrete project is recommended and implemented for improvement.

This paper presents the results of the analysis of the network intrusion detection systems using data mining techniques and anomaly detection. Anomaly detection technique is present for a while in the area of data mining. Previous papers that implement data mining techniques to detect anomaly attacks actually use well-known techniques such as classification or clustering. Anomaly detection technique combines all these techniques. They are also facing problem on the fact that many of the attacks do not have some kind of signature on network and transport layer, so it is not easy to train models for these type of attacks. Network dataset that was used in this paper is DARPA 1998 dataset created in MIT Lincoln Laboratory and is used worldwide for the network testing purposes.

Internet and various services offered by it has become a daily routine. The Quality of Web Service (QWS) has become a significant factor in distinguishing the success of service providers. The main purpose of this paper is to analyze quality prediction using the IKS hybrid model with a new approach of data classification. We present the IKS hybrid model. The model combines selection of features, clustering and classification techniques. Three techniques are used (Information Gain (IG), K-means and Support Vector Machine (SVM)) over QWS dataset with collected 5,000 Web services. Our experiments and test results show that the proposed hybrid approach has achieved promising results in predicting the quality of web services and it represents a good basis for further development and research.

Most research on network traffic prediction has been done on small datasets based on statistical methodologies. This research analyzes an internet traffic dataset spanning multiple months using the data mining process. Each data mining phase was carefully fitted to the network analysis domain and systematized in context of data mining. The second part of the paper evaluates various seasonal time series prediction models (univariate), including ANN, ARIMA, Holt Winters etc., as a data mining phase on the given dataset. The experiments have shown that in most cases ANNs are superior to other algorithms for this purpose.

Software security is becoming highly important for universal acceptance of applications for many kinds of transactions. Automated code analyzers can be utilized to detect security vulnerabilities during the development phase. This paper is aimed to provide a survey on Static code analysis and how it can be used to detect security vulnerabilities. The most recent findings and publications are summarized and presented in this paper. This paper provides an overview of the gains, flows and algorithms of static code analyzers. It can be considered a stepping stone for further research in this domain.

Predicting the success of students is a topic which has been studied for a long time in different scientific fields. Evaluation of importance of the features used in the prediction and their subsequent selection is an immensely important step in the process of classification and data mining. This paper presents a study on the importance of student demographic features in the process of predicting. The study and performed analyses used the demographic data collected from the Information System for Higher Education (ISVU). For determining the importance of demographic features in the study the following methods have been used: Information Gain (IG), Gain Ratio (GR), Sequential Backward Selection (SBS), Sequential Forward Selection (SFS). The results show the features rank, their importance weight in the prediction and comparison of the results and the use of different methods. Two classification algorithms for evaluating the impact of ranking features to the quality of prediction are used: Naive Bayes i Support Vector Machine (SVM). Final results provide guidelines for the development of a new prediction model.

Climate data analysis is a progressive research area that focuses on analysis of change of climate conditions, investigation of climate phenomena and evaluation of interconnections of climate conditions. Data mining techniques introduce the effective and efficient way to analyze large amount of data in climatology. In this paper is presented the algorithm for climate data analysis using the clustering data mining techniques. The developed solution represents evaluation of climate data from the different points of view in order to provide a complete view of the data. Climate research experts can use these results to draw their own conclusions and perform detailed climate change analysis. Climate data is represented graphically as the map of measured climate parameters, the map of climate clusters identified in specified moment of time and the map of evolution steps identified between the consecutive time slices. Key-Words: Data mining, clustering, climate, hierarchical clustering, meteorology, evolving clusters

This paper presents results of using clustering to improve results of collaborative filtering. Clusters of users are created using friendship links within a social network using Markov Chain Algorithm (MCL). Clusters are then used to make prediction of user choices using item based collaborative filtering with cosine similarity. Using the results from analyzing different cluster sizes, new algorithm was proposed that saves time and memory resources.

Since agile development was invented in the mid-1990s, it changed the way of doing software development. The emphasis is placed on a short development cycles based on a feedback from customer. But, agile development was missing important component - development platform for supporting fast development cycles. That missing linking component can be recognized in a cloud computing, which eliminates major distribution requests which can downgrade agile development. The goal of this paper is to describe the connection of agile methods for software development with cloud computing platform. By describing that connection, benefits and improvements in whole software development process are being pointed out. As a practical part of this paper, the application for warehouse management is developed applying agile method called Dynamic Systems Development Method (DSDM) on Google App Engine platform as a service. This development is compared with the development of the application for warehouse management by agile methods but in a traditional way.

Electronic government initiatives in Bosnia and Herzegovina are still in its infancy and facing many issues and challenges. Therefore, the main goal of this study is to gain a better understanding of these issues and challenges by examining the adoption and diffusion of ‘e-government services’ from the citizen’s perspective at the local municipal level. Sixty nine usable responses were obtained from one hundred surveyed citizens with permanent residency in the Centar Municipality Sarajevo. The participants were asked about their perceptions of different aspects of egovernment services provided by their municipality. The results are encouraging. The citizens of Centar Municipality Sarajevo perceived their municipal e-government system as useful, easy to use, and having a high level of information quality. Consequently, they were willing to use e-government, particularly for accessing laws and by-law acts, filing state taxes, ordering birth, death and marriage certificates, renewing drivers’ licenses, registration and shopping. However, they were not in favour of using internet for online voting. Keywords: Electronic Government, Local Government, Case Study, Municipality Centar-Sarajevo