Distributed Inter Process Communications

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in this experiment. The result on computational times differences among different algorithms were recorded and compared.

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in this experiment. The result on computational times differences among different algorithms were recorded and compared.

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in this experiment. The result on computational times differences among different algorithms were recorded and compared.

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in his experiment. The result on computational times differences among different algorithms were re...

Classification modeling in data mining has evolved since 1990's. Many methods have been introduced and experimented. Among them were Multi Layer Perceptron and Radial Basis Function in Neural Network and Multiple Regressions in Statistical Analysis. Not many researches have been proposed in the field of rough classification modeling. When SIP/DRIP algorithm was ported on rough classification model, its accuracy has shown competitive results [1]. The performance of the proposed rough model is compared with neural classifiers on different datasets. This paper made experiments on the combination of SIP/DRIP algorithm with DIPC distributed system to increase the computation speed of the method. Comparison made with another distributed computing system will be presented.

Classification modeling in data mining has evolved since 1990psilas. Many methods have been introduced and experimented. Among them were Multi Layer Perceptron and Radial Basis Function in Neural Network and Multiple Regressions in Statistical Analysis. Not many researches have been proposed in the field of rough classification modeling. When SIP/DRIP algorithm was ported on rough classification model, its accuracy has

Fuzzy-rough set theory, an extension to classical rough set theory, is effectively used for attribute reduction in hybrid decision systems. However, it’s applicability is restricted to smaller size datasets because of higher space and time complexities. In this work, an algorithm MR_IMQRA is developed as a MapReduce based distributed/parallel approach for standalone fuzzy-rough attribute reduction algorithm IMQRA. This algorithm uses a vertical partitioning technique to distribute the input data in the cluster environment of the MapReduce framework. Owing to the vertical partitioning, the proposed algorithm is scalable in attribute space and is relevant for scalable attribute reduction in the areas of Bioinformatics and document classification. This technique reduces the complexity of movement of data in shuffle and sort phase of MapReduce framework. A comparative and performance analysis is conducted on larger attribute space (high dimensional) hybrid decision systems. The comparat...

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in this experiment. The result on computational times differences among different algorithms were recorded and compared.

Classification modeling in data mining has evolved since 1990psilas. Many methods have been introduced and experimented. Among them were Multi Layer Perceptron and Radial Basis Function in Neural Network and Multiple Regressions in Statistical Analysis. Not many researches have been proposed in the field of rough classification modeling. When SIP/DRIP algorithm was ported on rough classification model, its accuracy has

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in this experiment. The result on computational times differences among different algorithms were recorded and compared.

Big Data Analytics have becoming more important in Industrial Revolution 4.0 (IR4.0). Data Analytics is a superset to Data Mining. Data mining consist of several popular methods. Rough Set or Rough Classification Modeling (RCM), Statistical analysis and Neural Network are among prevalent algorithms in Data Analytics. Satisfiable Integer
Programming (SIP) algorithm in RCM consume lots of time to execute especially on a single node environment. SIP capability is to give better result in terms of reducts calculation accuracy on huge dataset. Distributed Inter Process Communication (DIPC) is an open source distributed operating system. Among its services are shared memory and semaphore. Combination of SIP algorithm and DIPC is proposed in order to expedite the computational times and processing speed. Standardized i386 machines were used to develop clusters of distributed operating system in his experiment. The result on computational times differences among different algorithms were recorded and compared.