Comparative analysis of reporting mechanisms based on XML technology

Comparative analysis of reporting mechanisms based on XML technology is presented in the paper. The analysis was carried out as the part of the process of selecting and implementing of reporting mechanisms for a cadastre information system. The reports were designed for two versions of the system, i.e. for the internet system based on PHP technology and the fat client system in two-layer client-server architecture. The reports for the internet system were prepared using XSLT for HTML output and using XML-FO for PDF output and compared with reports implemented using Free PDF library. Each solution was tested by means of the Web Application Stress Tool in order to determine what limits in scalability and efficiency could be observed. As far as the desktop system is concerned three versions of reporting mechanisms based on Crystal Reports, Microsoft Reporting Services and XML technology were accomplished and compared with the mean execution time as the main criterion

Evolutionary Fuzzy System Ensemble Approach to Model Real Estate Market based on Data Stream Exploration

An approach to predict from a data stream of real estate sales transactions based on ensembles of genetic fuzzy systems was presented. The proposed method relies on incremental expanding an ensemble by models built over successive chunks of a data stream. The output of aged component models produced for current data is updated according to a trend function reflecting the changes of premises prices since the moment of individual model generation or the beginning of the data stream. The impact of different trend functions on the accuracy of single and ensemble fuzzy models was investigated in the paper. Intensive experiments were conducted to evaluate the proposed method using real-world data taken from a dynamically changing real estate market. The statistical analysis of experimental output was made employing the nonparametric methodology designed especially for multiple comparisons including Friedman tests followed by Nemenyi's, Holm's, Shaffer's, and Bergmann-Hommel's post-hoc procedures. The results proved the usefulness of ensemble approach incorporating the correction of individual component model output

Hybrid and Ensemble Methods in Machine Learning

Hybrid and Ensemble Methods in Machine Learnin

New trends in computational collective intelligence

This book consists of 20 chapters in which the authors deal with different theoretical and practical aspects of new trends in Collective Computational Intelligence techniques. Computational Collective Intelligence methods and algorithms are one the current trending research topics from areas related to Artificial Intelligence, Soft Computing or Data Mining among others. Computational Collective Intelligence is a rapidly growing field that is most often understood as an AI sub-field dealing with soft computing methods which enable making group decisions and processing knowledge among autonomous units acting in distributed environments. Web-based Systems, Social Networks, and Multi-Agent Systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions. The chapters included in this volume cover a selection of topics and new trends in several domains related to Collective Computational Intelligence: Language and Knowledge Processing, Data Mining Methods and Applications, Computer Vision, and Intelligent Computational Methods. This book will be useful for graduate and PhD students in computer science as well as for mature academics, researchers and practitioners interested in the methods and applications of collective computational intelligence in order to create new intelligent systems

A Quick Method for Querying Top-k Rules from Class Association Rule Set

Finding class association rules (CARs) is one of the most important research topics in data mining and knowledge discovery, with numerous applications in many fields. However, existing techniques usually generate an extremely large number of results, which makes analysis difficult. In many applications, experts are interested in only the most relevant results. Therefore, we propose a method for querying top-k CARs based on their supports. From the set of mined CARs that satisfy the minimum support and the minimum confidence thresholds, we use a QuickSort-based method to query top-k rules. The whole rule set is partitioned into two groups. If the number of rules in the first group is k, then the first group is the set of result rules. If the number of rules in the first group is greater than k, the second group is partitioned to find the remaining top-k rules. Experimental results show that the proposed method is more efficient than existing techniques in terms of mining time

4th International Conference on Computational Collective Intel- ligence Technologies and Applications (ICCCI 2012)

The book consists of 35 extended chapters which have been selected and invited from the submissions to the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2012) held on November 28-30, 2012 in Ho Chi Minh City, Vietnam. The book is organized into six parts, which are semantic web and ontologies, social networks and e-learning, agent and multiagent systems, data mining methods and applications, soft computing, and optimization and control, respectively. All chapters in the book discuss theoretical and practical issues connected with computational collective intelligence and related technologies. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants in courses on Soft Computing, Multiagent Systems, and Data Mining. This book can be also useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases of intelligent collectives. Many such challenges are suggested by particular approaches and models presented in individual chapters of this book. The editors hope that readers of this volume can find many inspiring ideas and influential practical examples and use them in their future work

Equilibrium Analysis for Within-Network Dynamics: From Linear to Nonlinear Aggregation

In this paper, it is shown how, in contrast to often held beliefs, certain classes of nonlinear functions used for aggregation in network models enable analysis of the emerging within-network dynamics like linear functions do. In addition, two specific classes of nonlinear functions for aggregation in networks (weighted euclidean functions and weighted geometric functions) are introduced. Focusing on them in particular, it is illustrated in detail how methods for equilibrium analysis (based on a symbolic linear equation solver), can be applied to predict the state values in equilibria for such nonlinear cases as well