We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many differe...

MapReduce is an emerging programming paradigm for data parallel applications proposed by Google to simplify large-scale data processing. MapReduce implementation consists of map function that processes input key/value pairs to generate intermediate key/value pairs and reduce function that merges and converts intermediate key/value pairs into final results. The reduce function can only start pro...

Despite increasing data volumes much faster than compute power. This growth demands new strategies for processing and analyzing information. Organizations are determining that significant forecasting can be through sorting and analyze Big Data. Ever since a large amount of data is "amorphous", it should be structured in a manner which is appropriate for mining and succeeding analysis. Hadoop he...

2 Notation. Function composition is written as f · g , and application of f to a is written f a or f . a. Application written as a space has the highest priority and application written as a “low dot” has the lowest priority (as suggested by the wide space surrounding the dot). So, f ·g . x +y = f (g (x +y)). This convention saves parentheses, thus improving readability. In order to facilitate ...

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data ...

Extremely slow, or straggler, tasks are a major performance bottleneck in map-reduce systems. Hadoop infrastructure makes an effort to both avoid them (through minimizing remote data accesses) and handle them in the runtime (through speculative execution). However, the mechanisms in place neither guarantee the avoidance of performance hotspots in task scheduling, nor provide any easy way to tun...

Motif discovery is one of the most challenging problems in bioinformatics today. DNA sequence motifs are becoming increasingly important in analysis of gene regulation. Motifs are short, recurring patterns in DNA that have a biological function. For example, they indicate binding sites for Transcription Factors (TFs) and nucleases. There are a number of Motif Discovery algorithms that run seque...

Nondeterministic Finite-state Automata (NFA) are simple, yet powerful devices that model a plethora of computationally oriented phenomena. One of the advantages of NFA’s is that they are closed under several operations, such as concatenation, intersection, difference, and homomorphic images. This makes NFA’s ideally suited for a modular approach, for instance in the context of protocol design a...

Recent studies and industry practices build data-center-scale computer systems to meet the high storage and processing demands of data-intensive and compute-intensive applications, such as web searches. The Map-Reduce programming model is one of the most popular programming paradigms on these systems. In this paper, we report our experiences and insights gained from implementing three data-inte...