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The aim of this paper is to examine possibilities for the initial data analyses of the failure data from industrial production process. To perform the initial data analysis of the data from production process we have used graphical statistical method and also data mining methods like drill-down analysis and cluster analysis. Before applying mentioned techniques and methods it was necessary to know...
Both academia and organizations show great interest in streaming big data analytics - the process of extracting knowledge structures from continuous, high volume and high velocity continuous flow of data in a myriad of formats from a variety of real-time data sources. The challenge for organizations lies in being able to transform this deluge of data into instantaneous intelligence that can enable...
This study attempts to establish methods for characterizing the complexity of ordinal data through the information and entropy parameters. In this respect, there were examined the methods for measuring the complexity of data with similar statistical characteristics and the parameters that can make the difference between them were established. For this purpose, the analysis was applied to three data...
In this demonstration we present MYOLAP, a Java-based tool that allows OLAP analyses to be personalized and enhanced by expressing “soft” query constraints in the form of user preferences. MYOLAP is based on a novel preference algebra and a preference evaluation algorithm specifically devised for the OLAP domain. Preferences are formulated either visually or through an extension of the MDX language,...
Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures. They can be seen as the generalization of matrices. One typical...
In this paper, we present a strategy to reduce the processing time needed for selection operations with many attributes in standard database systems. These problems mostly occur in data mining, data analysis, information retrieval, and applications with high combinatorial complexity. In these systems, standard indexes do not gain a satisfying performance. Currently, this problem is tackled using more...
Mass of information intelligence services are a hot research field of current information, As the massive network of information, particularly audio and video information has a huge amount of data, non-structural, high dimension, semantic feature of diversity, putting forward a new challenge in the mass of information in data integration, deep mining and intelligence analysis. Traditional information...
Concept lattice is a new mathematical tool for data analysis and knowledge processing. Attribute reduction is very important in the theory of concept lattice because it can make the discovery of implicit knowledge in data easier and the representation simpler. In this paper the reduction of the concept lattice was investigated. First, we present a close-degree of concept to measure the close-degree...
In enterprise environments, the task of assigning access control rights to subjects for resources is not trivial. Because of their complexity, distribution and size, access control policies can contain anomalies such as inconsistencies, which can result in security vulnerabilities. A set of access control policies is inconsistent when, for specific situations different incompatible policies can apply...
In this paper, we present a scalable evolutionary algorithm for clustering large and dynamic data sets, called Scalable Evolutionary Clustering with Self Adaptive Genetic Operators (Scalable ECSAGO). The proposed evolutionary clustering algorithm can adapt its genetic operators rate while the evolution leads to the optimal centers of the clusters. The sizes of the clusters are estimated using a hybrid...
The proportion of unstructured data in the total number of information is much larger that the proportion of structured data. But the research on the processing and analyzing mode is not wider and deeper than the structured data. Based on illustrating the importance of the unstructured data research, this article expounds the key techniques in its processing and analyzing mode, such as entity recognition,...
Regional economic data are mainly from statistical data. The traditional statistical data are documents of WORD or Excel. The data structure with non-hierarchical brings a lot of inconvenience to the analysis and research. In this paper, the regional economic data analysis and mining platform based on On-Line Analytical Mining (OLAM) has been studied, which provides a more convenient and efficient...
Efficient processing of similarity joins is important for a large class of data analysis and data-mining applications. This primitive finds all pairs of records within a predefined distance threshold of each other. However, most of the existing approaches have been based on spatial join techniques designed primarily for data in a vector space. Treating data collections as metric objects brings a great...
The category of systems that support the decision process is defined as the "business intelligence" term. On the whole, this regards all kinds of computer tools that add "Intelligence" to the business process. The traditional or operational application integration, along with a range of programs for data analysis or from the category of expert systems provides an effective basis...
In this paper questions of data acquisition for intelligent data analysis are considered. The authors describe ontology-based approach for data modeling and management. The ontology sets the domain data structure which can be used in the analysis process. "Imunoskryn" information system for centralized collecting and storing medical data in immunology is briefly described.
Network representation is a convenient and intuitive abstraction for analyzing the massive interacting data. Some topological characteristics of the network have been found in the past decade, and community structure is the typical one of them. Community detection has become a hot topic in complex network analysis. In the paper, a hybrid algorithm is presented for solving such problem. At first, we...
Summary form only given. OLAP cubes are multidimensional data structures, used for fast data analysis in business intelligence. Their design typically follows the analysis needs. Just like with relational databases, some designs carry anomalies with them. Our presentation starts with an overview of the life cycle of the data in a database environment and a short review of both theory and practice...
Exploratory data analysis (EDA: Tukey, 1977) has been introduced and extensively used for more than 30 years yet boxplot and scatterplot are still the major EDA tools for visualizing continuous data in the 21st century. On the other hand, multiple correspondence analysis (MCA) type of methods and mosaic plots are most popular in practice for visualizing multivariate binary and nominal data. But all...
Formal Concept Analysis has very important meaning in mathematical methods for data mining and knowledge processing. In the face of the variety of the data structure, traditional methods can hardly meet decider's need. This paper summarizes a few of data structure for the concept analysis and present the data scale methods for each other based on their own characters. It not only extends the application...
This paper presents our results with the investigation of decentralized data dependency analysis among concurrently executing processes in a service-oriented environment. Distributed Process Execution Agents (PEXAs) are responsible for controlling the execution of processes that are composed of Web services. PEXAs are also associated with specific distributed sites for the purpose of capturing data...
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