Space Adaptation: Privacy-Preserving Multiparty Collaborative Mining with Geometric Perturbation

The service-oriented infrastructure has become popular for collaboratively mining data distributed over organizations [3], where the participants are the data providers who submit their perturbed datasets to the designated data mining service provider (the data miner) for mining commonly interested models

A Survey Paper on Implementing Service Oriented Architecture for Data Mining

Web service is working with the web with an object or component to achieve the communication between the distributed applications and between the different platforms through a series of protocols. Web Service provides a set of standard types systems, rules, techniques and internet service-oriented applications for communication between the different platforms, different programming languages and different types of systems to achieve interoperability. This survey paper gives the application of web service for data mining also we build a data mining model based on Web services and going forward it is possible to build a new data mining solution for security according to the prototype of a dynamic web service based data mining process system. DOI: 10.17762/ijritcc2321-8169.15079

Implementing Service Oriented Architecture for Data Mining

With Web technology, data on internet has become increasingly large and complex. No matter users or internet users needs all this data. Also the data which is available on web not all the time useful information or it is knowledgeable. Hence web data mining is necessary to fulfill this demand. Web data mining can extract unstructured, undiscovered data which is possibly useful information and knowledge, from much incomplete, noisy, ambiguous, random, practical application related data from WWW network. It is a new emerging commercial information/data mining technology. Its main characteristic is to extract key data to support business for decision making from business database through the use of extraction, conversion, analysis and other transaction models. Web service is deployed on the web with an object or component to achieve distributed application software platform through a series of protocols. Web Service platform provides a set of standard types systems, rules, techniques and internet service-oriented applications for communication between the different platforms, different programming languages and different types of systems to achieve interoperability. This paper gives the actual and practical application of web services for data mining, we build a data mining model based on Web services and going forward it is possible to implement the new data mining solution for security configuration. This has been achieved with the use of prototypes of a dynamic web service based data mining systems. DOI: 10.17762/ijritcc2321-8169.15079

DAME: A Distributed Web based Framework for Knowledge Discovery in Databases

Massive data sets explored in many e-science communities, as in the Astrophysics case, are gathered by a very large number of techniques and stored in very diversified and often-incompatible data repositories. Moreover, we need to integrate services across distributed, heterogeneous, dynamic virtual organizations formed from the different resources within a single enterprise and/or from external resource sharing and service provider relationships. The DAME project aims at creating a distributed e-infrastructure to guarantee integrated and asynchronous access to data collected by very different experiments and scientific communities in order to correlate them and improve their scientific usability. The project consists of a data mining framework with powerful software instruments capable to work on massive data sets, organized by following Virtual Observatory standards, in a distributed computing environment. The integration process can be technically challenging because of the need to achieve a specific quality of service when running on top of different native platforms. In these terms, the result of the DAME project effort is a service-oriented architecture, by using appropriate standards and incorporating Cloud/Grid paradigms andWeb services, that will have as main target the integration of interdisciplinary distributed systems within and across organizational domains

Design and Implementation of Service-Oriented Expert System

In recent years, the Internet technologies are well developed and the Internet is filled with all kinds of information. Since the data storage is increasingly distributed and data formats are more diverged, data collection and integration for providing value- added services have gradually become important topics. In this study, we propose the Service-Oriented Expert System (SOES) based on Service Component Architecture (SCA) which can make the services on different platforms turn into a common service component on the Internet, concatenate all the service components by combining with the Enterprise Service Bus (ESB), and use both expert rules and data mining techniques to perform the data classification. The SOES is applied to analyze the annual financial information derived from electronic industry in the Taiwan Economic Journal (TEJ) during 2006 to 2008 for discovering the financial crisis enterprises. The experiment results show that using expert rules and decision tree to find the financial crisis enterprise is higher performance

An agent-based service oriented architecture for risk mining

University of Technology, Sydney. Faculty of Engineering and Information Technology.Risk Mining (RM) is the process of analyzing data including risk information by data mining methods, with the mining results for risk prevention. In the last few years, some researchers have proposed the combination of data mining and agent technology (agent mining) to improve the performance of data mining methodology in the heterogeneous business environments. However, problems exist for further research with the application of risk mining systems in real industry environments to enhance the robustness of system architect, dynamic business process and model accuracy etc. Therefore, in this thesis we present an Agent-based Service-oriented Risk Mining Architecture (ABSORM), which has been designed to facilitate the development of agent mining systems to address the above issues. This thesis focuses on developing the following strategies: • The integration of agent technology with web service. In this framework, we propose a new and easier method, by which the system functions are not integrated into the structure of the agents, rather modeled as distributed services and applications which are invoked by the agents acting as controllers and coordinators. Therefore, techniques developed in this framework can improve the interoperability between different modules, distribution of resources, and the lack of dependency of programming languages. • The integration of agent technology with business process management. In this work, we develop the autonomous agents that can collaborate in a business flow, which not only increases the reusability of the system, but also eases the system development in terms of re-usability of the computational resources. A group of agents solves problems in the following way: each individual agent solves the problem individually, and then interacts with each other to finalize a business process. • The integration of agent technology with ensemble learning methods. In this thesis, we are interested in developing agent-based ensemble learning strategies for risk mining: each ensemble agent individually gathers the evidence about model evaluation, and then ensembles learning methods like bagging and boosting is used to obtain prediction from the individually gathered evidence. Agent based ensemble learning can provide a critical boost to risk mining where predictive accuracy is more vital than model interpretability. The proposed architecture has been evaluated for building an online banking fraud detection system and a student risk management system. These two applications have been proved to be a sophisticated, yet user friendly, risk analysis and management tool. They are modular, interactive, dynamic and globally oriented

The DAME/VO-Neural Infrastructure: an Integrated Data Mining System Support for the Science Community

Astronomical data are gathered through a very large number of heterogeneous techniques and stored in very diversified and often incompatible data repositories. Moreover in the e-science environment, it is needed to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise and/or external resource sharing and service provider relationships. The DAME/VONeural project, run jointly by the University Federico II, INAF (National Institute of Astrophysics) Astronomical Observatories of Napoli and the California Institute of Technology, aims at creating a single, sustainable, distributed e-infrastructure for data mining and exploration in massive data sets, to be offered to the astronomical (but not only) community as a web application. The framework makes use of distributed computing environments (e.g. S.Co.P.E.) and matches the international IVOA standards and requirements. The integration process is technically challenging due to the need of achieving a specific quality of service when running on top of different native platforms. In these terms, the result of the DAME/VO-Neural project effort will be a service-oriented architecture, obtained by using appropriate standards and incorporating Grid paradigms and restful Web services frameworks where needed, that will have as main target the integration of interdisciplinary distributed systems within and across organizational domains.Comment: 10 pages, Proceedings of the Final Workshop of the Grid Projects of the Italian National Operational Programme 2000-2006 Call 1575; Edited by Cometa Consortium, 2009, ISBN: 978-88-95892-02-