Attribute oriented induction with star schema

This paper will propose a novel star schema attribute induction as a new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using non rule based concept hierarchy by implementing both of approaches. In novel star schema attribute induction some improvements have been implemented like elimination threshold number as maximum tuples control for generalization result, there is no ANY as the most general concept, replacement the role concept hierarchy with concept tree, simplification for the generalization strategy steps and elimination attribute oriented induction algorithm. Novel star schema attribute induction is more powerful than the current attribute oriented induction since can produce small number final generalization tuples and there is no ANY in the results.Comment: 23 Pages, IJDM

Warehousing of object oriented petroleum data for knowledge mapping

Australia produces a-third of world?s natural resources. Enormous amounts of energy and financial resources are expended in order to tap these natural reserves from the earth?s surface. Vast amounts of these resources, however, remain unexplored and under exploited. Data pertaining natural resources, such as mineral and petroleum, are, in general, heterogeneous and complex in nature. Volumes of these types of data are geographically distributed among many companies in Australia and abroad. The existing historical resources data are logically and physically organized using warehousing techniques. Entity relationship (ER) and object oriented (OO) data mapping techniques are used for analyzing the data entities, dimensions and objects. In this paper object oriented data and warehousing of object class data models have been described. Data mining techniques can be employed to explore many more resources hidden, under great depths of the earth?s crust, without additional efforts of exploration and development. Warehoused object oriented resources data can significantly reduce the complexity of the resources data structuring and enhance the data integration and information sharing among various operational units of the resources industry. Large amount of financial inputs can be saved if these technologies are successfully implemented in the resources industry

SOLAM: A Novel Approach of Spatial Aggregation in SOLAP Systems

In the context of a data driven approach aimed to detect the real and responsible factors of the transmission of diseases and explaining its emergence or re-emergence, we suggest SOLAM (Spatial on Line Analytical Mining) system, an extension of Spatial On Line Analytical Processing (SOLAP) with Spatial Data Mining (SDM) techniques. Our approach consists of integrating EPISOLAP system, tailored for epidemiological surveillance, with spatial generalization method allowing the predictive evaluation of health risk in the presence of hazards and awareness of the vulnerability of the exposed population. The proposed architecture is a single integrated decision-making platform of knowledge discovery from spatial databases. Spatial generalization methods allow exploring the data at different semantic and spatial scales while reducing the unnecessary dimensions. The principle of the method is selecting and deleting attributes of low importance in data characterization, thus produces zones of homogeneous characteristics that will be merged

Technical report of data mining

No abstract availabl

Impact of Fuzzy Logic in Object-Oriented Database Through Blockchain

In this article, we show that applying fuzzy reasoning to an object-arranged data set produces noticeably better results than applying it to a social data set by applying it to both social and object-situated data sets. A Relational Data Base Management System (RDBMS) product structure offers a practical and efficient way to locate, store, and retrieve accurate data included inside a data collection. In any case, clients typically have to make vague, ambiguous, or fanciful requests. Our work allows clients the freedom to utilise FRDB to examine the database in everyday language, enabling us to provide a range of solutions that would benefit clients in a variety of ways. Given that the degree of attributes in a fuzzy knowledge base goes from 0 to 1, the term "fuzzy" was coined. This is due to the base's fictitious formalization's reliance on fuzzy reasoning. In order to lessen the fuzziness of the fuzzy social data set as a result of the abundance of uncertainty and vulnerabilities in clinical medical services information, a fuzzy article located information base is designed here for the Health-Care space. In order to validate the presentation and sufficiency of the fuzzy logic on both data sets, certain fuzzy questions are thus posed of the fuzzy social data set and the fuzzy item-situated information base.

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining