Web-Scale Analytical Processing is a much investigated topic in current research. Next to parallel databases, new flavors of parallel data processors have recently emerged. One of the most discussed approaches is MapReduce. MapReduce is highlighted by its programming model: All programs expressed as the second-order functions map and reduce can be automatically parallelized. Although MapReduce ...

In this paper, MapReduce programming model is used to parallelize training and tagging proceess in maximum entropy part of speech tagging for Bahasa Indonesia. In training process, MapReduce model is implemented dictionary, tagtoken, and feature creation. In tagging process, MapReduce is implemented to tag lines of document in parallel. The training experiments showed that total training time u...

MapReduce has emerged as a viable competitor to database systems in big data analytics. MapReduce programs are being written for a wide variety of application domains including business data processing, text analysis, natural language processing, Web graph and social network analysis, and computational science. However, MapReduce systems lack a feature that has been key to the historical succes...

The MapReduce framework has been generating a lot of interest in a wide range of areas. It has been widely adopted in industry and has been used to solve a number of non-trivial problems in academia. Putting MapReduce on strong theoretical foundations is crucial in understanding its capabilities. This work links MapReduce to the BSP model of computation, underlining the relevance of BSP to mode...

The MapReduce programming model is widely acclaimed as a key solution to designing data-intensive applications. However, many of the computations that fit this model cannot be expressed as a single MapReduce execution, but require a more complex design. Such applications consisting of multiple jobs chained into a long-running execution are called pipeline MapReduce applications. Standard MapRed...

MapReduce has emerged as a popular programming model for large-scale distributed computing. Its framework enforces strict synchronization between successive map and reduce phases and limited data-sharing within a phase. Use of keyvalue based persistent storage with MapReduce presents intriguing opportunities and challenges. These challenges relate primarily to semantic inconsistencies arising f...

An increasing number of MapReduce applications are written using high-level SQL-like abstractions on top of MapReduce engines. Such programs are translated into MapReduce workflows where the output of one job becomes the input of the next job in a workflow. A user must specify the number of reduce tasks for each MapReduce job in a workflow. The reduce task setting may have a significant impact ...

MapReduce has emerged as a viable competitor to database systems in big data analytics. MapReduce programs are being written for a wide variety of application domains including business data processing, text analysis, natural language processing, Web graph and social network analysis, and computational science. However, MapReduce systems lack a feature that has been key to the historical succes...