As the de facto standard for data mining models, the Predictive Model Markup Language (PMML) provides tremendous benefits for business, IT, and the data mining industry in general, since it allows for predictive models to be easily moved between applications. Due to the cross-platform and vendor-independent nature of such an open-standard, auto-generated PMML code is often represented in different versions of PMML. A tool may export PMML 2.1 and another import PMML 4.0. This problem raises the issue of conversion. For true interoperability, PMML needs to be easily converted from one version to another.In this paper, we describe the capabilities associated with the "PMML Converter". This application represents a great step in the PMML path towards true interoperability in data mining. Besides converting older versions of PMML to its latest, the PMML converter checks PMML files for syntax issues and, if issues are encountered, automatically corrects them. This paper also describes the capabilities associated with an interactive PMML-based application, the "Transformations Generator." Auto-generated PMML code can omit important data pre-processing steps which are an integral part of a predictive solution. The Transformations Generator aims to bridge this gap by providing a graphical interface for the development and expression of data pre-processing steps in PMML.
This paper describes PMML extensions for the modular open source data analytics platform KNIME adding preprocessing support and the ability to edit existing PMML code. It is also shown how the PMML model representation in KNIME can be used within meta learning schemes such as boosting and bagging.
Modular exponentiation with a large modulus, which is usually accomplished by repeated modular multiplications, has been widely used in public key cryptosystems for secured data communications. To speed up the computation, the Montgomery modular multiplication algorithm is used to relax the process of quotient determination, and the carry-save addition (CSA) is employed to reduce the critical path delay. In this paper, based on the inherent data dependency between the modular multiplication and square operations in the H-algorithm of modular exponentiation, we present a new modular exponentiation architecture with a unified modular multiplication/square module and show how to reduce the number of input operands for the CSA tree by mathematical manipulation. The developed architecture has the following advantages. 1) There is no need to convert the carry-save form of an operand into its binary representation at the end of each modular multiplication. In this way, except the final step to get the result of modular exponentiation, the time-consuming carry propagation can then be eliminated. 2) The number of input operands for the CSA tree is reduced in a very efficient way. 3) The hardware saving is achieved with very limited impact on the original critical path delay when designed with two distinct modular multiplication and square components. Experimental results show that our modular exponentiation design obtains the least hardware complexity compared with the existing work and outperforms them in terms of area-time (AT) complexity as well.
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