Optimizing and Tuning MapReduce Jobs to Improve the Large‐Scale Data Analysis Process

Data‐intensive applications process large volumes of data using a parallel processing method. MapReduce is a programming model designed for data‐intensive applications for massive data sets and an execution framework for large‐scale data processing on clusters of commodity servers. While fault tolerance, easy programming structure, and high scalability are considered strong points of MapReduce; however its configuration parameters must be fine‐tuned to the specific deployment, which makes it more complex in configuration and performance. This paper explains tuning of the Hadoop configuration parameters, which directly affect MapReduce's job workflow performance under various conditions to achieve maximum performance. On the basis of the empirical data we collected, it became apparent that three main methodologies can affect the execution time of MapReduce running on cluster systems. Therefore, in this paper, we present a model that consists of three main modules: (1) Extending a data redistribution technique in order to find the high‐performance nodes, (2) Utilizing the number of map/reduce slots in order to make it more efficient in terms of execution time, and (3) Developing a new hybrid routing schedule shuffle phase in order to define the scheduler task while memory management level is reduced.     

Polycarbonate microfilters by nuclear tracking and chemical etching (track‐etching) technique: Preparation and characterization

Track‐etched polycarbonate (PC) microfilters were successfully prepared by tracking with fission fragments in the Thai Research Reactor‐1/Modification‐1 nuclear reactor and chemical etching with sodium hydroxide aqueous solutions. The porosity of the as‐prepared microfilters can be controlled by varying the exposure time in the nuclear reactor and the average pore diameter by varying the etching conditions. In the present work, the average pore diameter of the as‐prepared microfilters ranged from ～2.0 to 9.5 μm (workable values) and the highest pore density achieved was ～150,000 pores cm^?2. It was found that chemical etching also caused a reduction in the film thickness. Fourier‐transform infrared spectroscopy results suggested some modifications in the chemical structure of the PC molecules after both nuclear tracking and chemical etching steps. Lastly, water permeability of the as‐prepared, track‐etched PC microfilters was found to increase with increasing average pore diameter. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 982–990, 2006     

HYBRID PROFILING FOR HYBRID MULTICRITERIA RECOMMENDATION BASED ON IMPLICIT MULTICRITERIA INFORMATION

Multicriteria recommender systems typically gather the user preferences by asking a user to rate different aspects of an item on a sliding scale explicitly. However, this approach could possibly cause intrusiveness and conflict on user preferences. For example, an individual's preference on each aspect of an item may conflict with an overall preference. To overcome such limitations, we proposed the hybrid profiling framework to generate a set of useful implicit dataset to support multicriteria recommender systems. We also proposed two hybrid multicriteria recommendation approaches, namely the user-attribute-based (UAB) and the user-item matching (UIM) to improve recommendation accuracy. Finally, we conducted experiments to confirm the efficiency of the proposed approaches. The experiments show that the profiling framework and two hybrid recommendation approaches can alleviate the problem in an intrusive manner and decrease the degree of preference conflict without decreasing the accuracy of the recommendation. They also show that our proposed hybrid multicriteria recommendation approaches can significantly outperform both the traditional collaborative filtering and the simple multicriteria filtering approaches.