Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of Map-Reduce leads to much low-level hacking to deal with the many-step, branching dataflows that arise in practice. Moreover, users must repeatedly code standard operations such as join by hand. These practices waste time, introduce bugs, harm readability, and impede optimizations. Pig is a high-level dataflow system that aims at a sweet spot between SQL and Map-Reduce. Pig offers SQL-style high-level data manipulation constructs, which can be assembled in an explicit dataflow and interleaved with custom Map-and Reduce-style functions or executables. Pig programs are compiled into sequences of Map-Reduce jobs, and executed in the Hadoop Map-Reduce environment. Both Pig and Hadoop are open-source projects administered by the Apache Software Foundation. This paper describes the challenges we faced in developing Pig, and reports performance comparisons between Pig execution and raw Map-Reduce execution.

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Hadoop is a massively scalable parallel computation platform capable of running hundreds of jobs concurrently, and many thousands of jobs per day. Managing all these computations demands for a workflow and scheduling system. In this paper, we identify four indispensable qualities that a Hadoop workflow management system must fulfill namely Scalability, Security, Multi-tenancy, and Operability. We find that conventional workflow management tools lack at least one of these qualities, and therefore present Apache Oozie, a workflow management system specialized for Hadoop. We discuss the architecture of Oozie, share our production experience over the last few years at Yahoo, and evaluate Oozie's scalability and performance.

Refrigeration systems have been widely used to maintain reduced temperature for specific applications. The work deals with modifying the existing design by installing fins in evaporator which acts as the key factor in refrigeration system. A study has been carried out with HC and R134a refrigerants which are commonly used in typical refrigeration system. Comparison studies has been made on the cooling capacity of the two refrigerants in hourly basis, so as to check the compatibility of the alternative refrigerant in working conditions with reduced global warming effect and power consumption. This can be achieved by increasing the heat transfer rate.

Automatic air conditioning system is encouraged in most of the automotive especially passenger cars. This system can enable higher standard of comfort to the passengers, so the automotive industries are trying to implement the automatic air conditioning system in most of their vehicle. One the other hand manufacturing simulation is additional processing experienced in most of the manufacturing industry, to analysis the complete performance of the product or vehicle before it manufacturing. In recent decade more than 100 simulators are developed to analysis the various operation of the manufacturing and vehicle. But simulation analysis of air conditioning system and automatic air conditioning system is challenging to the engineer. They may require to spend more time to analysis the performance of the automatic air conditioning system. Thus in later period soft computing based system for the effective performance prediction of automatic air conditioning system is proposed. But the prediction accuracy of the past technique is not in the satisfactory level. Hence in this paper, a novel soft computing technique is proposed for the effective prediction of the performance of the automatic air conditioning system. In the proposed system support vector machine is used for the prediction of the performance of automatic air conditioning system. The performance of the proposed technique is compared with the ANN.