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Photo sharing websites have become extremely popular with GPS-enabled digital cameras and camera phones. Seeing autumn leaves, cherry blossoms, etc. is a traditional seasonal activity. Tremendous photos about leaves and blossoms have been uploaded. The goal of our paper is to detect and rank the most scenic leaves and blossoms viewing places based on social and image features. Methods for social interestingness, color percentage, edge rate, sharpness, ranking score have been provided. The proposed methods have been validated and verified by experimental results for maple leaves in Kyoto.

Performance metrics can be predicted with appropriate performance models and evaluation algorithms. The goal of our work is to adapt the Mean-Value Analysis evaluation algorithm to model the behavior of the thread pool. The computation time and the computational complexity of the proposed algorithm have been provided. The limit of the response time and the throughput sequences computed by the novel algorithm has been determined. It has been shown that the proposed algorithm can be applied to performance prediction of web-based software systems in ASP.NET environment. The proposed algorithm has been validated and the correctness of performance prediction with the novel algorithm has been verified with performance measurements. Error analysis has been performed to verify the correctness of performance prediction.

With GPS-enabled digital cameras and camera phones photo sharing websites have become exceptionally popular recently. Traditional seasonal activities are to admire leaves and blossoms. Tremendous photos about them have been uploaded. The goal of our paper is to detect the most scenic leaves and blossoms viewing places from Flickr based on social and image features as well as to perform their time trend analysis based on statistical and control engineering methods. Firstly, methods for social interestingness, color percentage, edge rate, sharpness, ranking score have been proposed. Secondly, methods for time trend analysis based on chi square test of independence and subspace identification have been provided. The proposed methods have been validated and verified with experimental results for maple leaves in Kyoto.

The remarkable growth of social networking services on global positioning system (GPS)-enabled handheld devices has produced enormous amounts of georeferenced big data. Given a large spatial dataset, the challenge is to effectively discover dense regions from the dataset. Dense regions might be the most attractive area in a city or the most dangerous zone of a town. A solution to this problem can be useful in many applications, including marketing, tourism, and social research. Density-based clustering methods, such as DBSCAN, are often used for this purpose. Nevertheless, current spatial clustering methods emphasize density while neglecting human behavior derived from geographical features. In this paper, we propose EBSCAN, which is based on the novel idea of an entanglement-based approach. Our method considers not only spatial information but also human behavior derived from geographical features. Another problem is that competing methods such as DBSCAN have two input parameters. Thus, it is difficult to determine optimal values. EBSCAN requires only a single intuitive parameter, tooFar, to discover dense regions. Finally, we evaluate the effectiveness of the proposed method using both toy examples and real datasets. Our experimentally obtained results reveal the properties of EBSCAN and show that it is >10 times faster than the competitor.

The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources to describe and organize them. Tag clouds provide rough impression of relative importance of each tag within overall cloud in order to facilitate browsing among numerous tags and resources. The goal of our paper is to enrich visualization of tag clouds. A font distribution algorithm has been proposed to calculate a novel metric based on frequency and content, and to classify among classes from this metric based on power law distribution and percentages. The suggested algorithm has been validated and verified on the tag cloud of a real-world thesis portal., {"references":["S.A. Golder, B.A. Huberman, The Structure of Collaborative Tagging\nSystems, 2005.","D.H. Pink, Folksonomy, New York Times, December 11, 2005.","C.C. Aggarwal, Social Network Data Analytics, 1st Edition, Springer,\nISBN 978-1-4419-8461-6, 2011.","J. Sinclair, M. Cardew-Hall, The folksonomy tag cloud: when is it useful?,\nJournal of Information Science, Vol. 34(1), pp. 15–29, 2008.","S. Lohmann, J. Ziegler, L. Tetzlaff, Comparison of Tag Cloud Layouts:\nTask-Related Performance and Visual Exploration, INTERACT, Part I,\nLNCS 5726, pp. 392–404, 2009.","Y. Hassan-Montero, V. Herrero-Solana, Improving Tag-Clouds as\nVisual Information Retrieval Interfaces, International Conference on\nMultidisciplinary Information Sciences and Technologies, Merida, Spain,\n2006.","J. Salonen, Self-Organizing Map Based Tag Clouds, OPAALS\nConference, 2007.","A.P. Garc´ıa-Plaza, A. Zubiaga, V. Fresno, R. Mart´ınez, Social Media\nMining and Social Network Analysis: Emerging Research, Chapter 9,\nTag Cloud Reorganization: Finding Groups of Related Tags on Delicious,\npp. 140–155, 2013.","kentbye's blog, Tag Cloud Font Distribution Algorithm, http://www.\nechochamberproject.com/node/247, June 24, 2005.\n[10] K. Hoffman, In Search Of ... The Perfect Tag Cloud, http://files.blog-city.\ncom/files/J05/88284/b/insearchofperfecttagcloud.pdf.\n[11] A. Clauset, C.R. Shalizi, M.E.J. Newman, Power-law distributions in\nempirical data, SIAM Review, Vol. 51(4), pp. 661–703, 2009.\n[12] A´ . Boga´rdi-Me´szo¨ly, A. Ro¨vid, H. Ishikawa, S. Yokoyama, Z. Va´mossy,\nTag and Topic Recommendation Systems, Acta Polytechnica Hungarica,\nVol. 10, No. 6, pp. 171–191, 2013.\n[13] Thesis Portal, Faculty of Electrical Engineering and Informatics,\nBudapest University of Technology and Economics, http://diplomaterv.\nvik.bme.hu/en/.\n[14] A. Clauset, C.R. Shalizi, M.E.J. Newman, Implementation for Article\nof Power-Law Distributions in Empirical Data, http://tuvalu.santafe.edu/\n∼aaronc/powerlaws/"]}

The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources in order to describe and organize them. Tag clouds are visually depicted tags in order to facilitate browsing among numerous tags and resources. The goal of our paper is to improve tag clouds with vocabulary refinement and enhanced reference counts. Namely, three novel algorithms have been proposed to correct spelling and clerical errors, contract tags, and improve reference counts. The provided algorithms have been validated and verified on real-world tag clouds of a thesis portal.

Process chains are a common modeling paradigm for analysis and optimization of logistic processes, and are intensively used in many practical applications. The ProC/B toolset is a collection of software tools for modeling, analysis, validation and optimization of process chains. The ProC/B models can be translated into queueing networks or Petri nets, which can be solved by effective techniques and algorithms to evaluate performance metrics. The base queueing model with Mean-Value Analysis evaluation algorithm, and their adaptations for modeling thread pool and queue limit have been verified and validated for multi-tier software systems. The goal of our work is to adapt these models and algorithms for process chains to model parallel processes and queue limit.

This paper addresses the issues to establish performance models and evaluation methodologies applying the identified queue limit performance factors. The computational complexity of the novel algorithms is provided. It is demonstrated that the proposed models and algorithms can be used for performance prediction of web-based software systems in ASP.NET environment. The validity of the novel models and algorithms as well as the correctness of the performance prediction is proven with performance measurements. It is shown that the error of the suggested models and algorithms is less than the error of the original model and algorithm. These methods facilitate more efficient performance prediction of web-based software systems.

Web-based software systems access some resources while executing the requests of the clients, typically several requests arrive at the same time, thus, competitive situation is established for the resources. In case of modeling such situation queueing model-based approaches are widely recognized. In our work, novel models and algorithms have been proposed to model the queue limit performance factor. In addition, the computational time and complexity of the proposed algorithms have been provided. Moreover, it has been shown that the proposed models and algorithms can be applied to performance prediction of webbased software systems in ASP.NET environment. The goal of our work is to validate the proposed models and algorithms and to verify the correctness of performance prediction with performance measurements in ASP.NET environment. The results have shown that the proposed models and algorithms predict the performance metrics much more accurate than the original model and algorithm.

The performance of information systems is a very important and complicated consideration. With the help of a proper performance model and an appropriate evaluation algorithm, performance metrics can be predicted. The goal of our work is to enhance the Mean-Value Analysis evaluation algorithm based on the investigation of thread pools. With the help of a queueing model and the enhanced performance evaluation algorithm the performance metrics of multi-tier information systems can be predicted much more accurate. In our work, the convergence of the original and the enhanced algorithms is analyzed, as well. In addition, the enhanced algorithm is experimentally validated.

The performance of information systems is a very important and complicated consideration. With the help of a proper performance model and evaluation algorithm, performance metrics can be predicted. The goal of our work is to improve the mean-value analysis evaluation algorithm based on the investigation of thread pools. With the help of a queueing model and the improved algorithm the performance metrics of multi-tier information systems can be predicted much more accurate. In our work, effect analysis of the improved algorithm is performed, as well.

With the help of a proper performance model and evaluation algorithm, the performance metrics of information systems can be determined at the early stages of the development process. In our work, the response time and the throughput performance metrics of multi-tier Web applications have been predicted based on a queueing model. The goal of our work is to extend the mean-value analysis (MVA) algorithm according to the investigation of the thread pool. Web applications have been tested with concurrent user sessions in order to validate the proposed algorithm in different versions of ASP.NET environment. Moreover, error analysis has been performed to demonstrate the accuracy of the proposed algorithm.

Web-based information systems play an important role in computer science. The most common consideration is performance. With the help of a proper performance model and evaluation algorithm, the performance metrics can be determined at the early stages of the development process. In our work, the response time and the throughput of multi-tier Web applications have been predicted based on a queueing model. The goal of our work is to extend the mean-value analysis (MVA) algorithm according to the investigation of the thread pool. The MVA and the proposed extended MVA evaluation algorithms have been implemented with the help of MATLAB. The input parameters have been estimated based on one measurement, and the model has been evaluated to predict performance metrics. Moreover, a Web application has been tested with concurrent user sessions in order to validate the proposed algorithm in ASP.NET environment.

Distributed systems and web applications play an important role in computer science nowadays. The most common consideration is performance, because these systems must provide services with low response time, high availability, and certain throughput level. The performance of a web application is affected by several factors. The goal of our work is to analyze how some of them affect the response time. In this paper, the effects of two configurable settings of the ASP.NET application server are discussed: the limit of the global queue and the limit of the application queue. The response time of a test web application are measured, while changing these settings. The results are analyzed in a qualitative manner which is followed by using statistical methods: independence tests to investigate which factors influence principally the performance. Our experiments have shown that the global queue limit and the application queue limit are performance factors. Finally, optimal settings according to the performance-related requirements are determined as a function of client workload and the settings of the thread pool attributes.

The most common consideration of web-based information systems is performance. Performance metrics depend on many factors. The first goal of our work is to analyse how some of them affect the response time. Our results have shown that the thread pool properties and the queue size limits are performance factors. In addition, the distribution of the response time has been determined. With the help of properly designed performance models and evaluation algorithms, the performance metrics of a system can be determined at the early stages of the development process. The second goal of our work is to predict the response time, the throughput, and the tier utilization of ASP.NET web applications, based on queueing models handling one and multiple session classes. In addition, web applications have been tested with concurrent user sessions to validate the models in different versions of the ASP.NET environment.