We propose a flexible framework that can be easily customized to enforce a large variety of information flow properties. Our framework combines the ideas of secure multi-execution and map-reduce computations. The information flow property of choice can be obtained by simply changes to a map (or reduce) program that control parallel executions. We present the architecture of the enforcement mech...

Given a large graph G = (V,E) with millions of nodes and edges, how do we compute its connected components efficiently? Recent work addresses this problem in map-reduce, where a fundamental trade-off exists between the number of mapreduce rounds and the communication of each round. Denoting d the diameter of the graph, and n the number of nodes in the largest component, all prior techniques for...

Large scale graphs containing O(billion) of vertices are becoming increasingly common in various applications. With graphs of such proportion, efficient querying infrastructure becomes crucial. In this paper, we propose WOOster a hosted querying infrastructure designed specifically for the large graphs. We make two key contributions: a) Design of the WOOster framework. b)Scalable map-reduce alg...

Efficiently scheduling MapReduce tasks is considered as one of the major challenges that face MapReduce frameworks. Many algorithms were introduced to tackle this issue. Most of these algorithms are focusing on the data locality property for tasks scheduling. The data locality may cause less physical resources utilization in non-virtualized clusters and more power consumption. Virtualized clust...

This paper presents a method that proposes the composition of the Map-Reduce algorithm and the Hough Transform method to research particular features of shape in the Big Data of images. We introduce the first formal translation of the Hough Transform method into the Map-Reduce pattern. The Hough transform is applied to one image or to several images in parallel. The context of the application o...

Periodic Frequent patterns (PFPs) are an important class of regularities that exist in a transactional database. In the literature, pattern growth-based approaches to mine PFPs have be proposed by considering a single machine. In this paper, we propose a Map-Reduce framework to mine PFPs by considering multiple machines. We have proposed a parallel algorithm by including the step of distributin...

Information retrieval is the area of finding particular web pages via a query to an internet search engine. Even though well sophisticated algorithms and data structures are used in traditional computer techniques to create indexes for efficiently organize and retrieve information systems, currently data mining techniques like clustering are used to enhance the efficiency of retrieval process. ...

The programming paradigm Map-Reduce [3] and its main open-source implementation, Hadoop [1], have had an enormous impact on large scale data processing. Our goal in this expository writeup is twofold: first, we want to present some complexity measures that allow us to talk about Map-Reduce algorithms formally, and second, we want to point out why this model is actually different from other mode...

Since few years Map Reduce programming model have shown great success in processing huge amount of data. Map Reduce is a framework for data-intensive distributed computing of batch jobs. This data-intensive processing creates skew in Map Reduce framework and degrades performance by great value. This leads to greatly varying execution time for the Map Reduce jobs. Due to this varying execution t...

he Map/Reduce framework-a parallel processing paradigm-is widely being used for large scale distributed data processing. Map/Reduce can perform typical relational database operations like selection, aggregation, and projection etc. However, binary relational operators like join, cartesian product, and set operations are difficult to implement with Map/Reduce. Map/Reduce can process homogeneous ...