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The exponential growth of data in current times and the demand to gaininformation and knowledge from the data present new challenges for databaseresearchers. Known database systems and algorithms are no longer capable ofeffectively handling such large data sets. MapReduce is a novel programmingparadigm for processing distributable problems over large-scale data using acomputer cluster.  In this work we explore the MapReduce paradigm from three different angles.We begin by examining a well-known problem in the field of data mining: miningclosed frequent itemsets over a large dataset. By harnessing the power ofMapReduce, we present a novel algorithm for mining closed frequent itemsetsthat outperforms existing algorithms.  Next, we explore one of the fundamental implications of "Big Data": The datais not known with complete certainty. A probabilistic database is a relationaldatabase with the addendum that each tuple is associated with a probability ofits existence. A natural development of MapReduce is of a distributedrelational database management system, where relational calculus has beenreduced to a combination of map and reduce function. We take this development astep further by proposing a query optimizer over distributed, probabilisticdatabase.  Finally, we analyze the best known implementation of MapReduce called Hadoop,aiming to overcome one of its major drawbacks: it does not directly support theexplicit specification of the data repeatedly processed throughout differentjobs.Many data-mining algorithms, such as clustering and association-rulesrequire iterative computation: the same data are processed again and againuntil the computation converges or a stopping condition is satisfied. Wepropose a modification to Hadoop such that it will support efficient access tothe same data in different jobs.

Analyzing Large-Scale, Distributed and Uncertain Data