With the advancement of wireless communication, Internet Things (IoT), and big data, high performance data analytic tools algorithms are required. Data clustering, a promising technique is widely used to solve IoT big-data-based problems, since it does not require labeled datasets. Recently, metaheuristic have been efficiently various clustering problems. However, handle datasets produced from ...

MapReduce is a distributed processing algorithm which breaks up large problem sets into small pieces, such that a large cluster of computers can work on those small pieces in an efficient, timely manner. MapReduce was created and popularized by Google, and is widely used as a means of processing large amounts of textual data for the purpose of indexing it for search later on. This paper examine...

We have incorporated cluster computing fundamentals into the introductory computer science curriculum at UC Berkeley. For the first course, we have developed coursework and programming problems in Scheme centered around Google’s MapReduce. To allow students only familiar with Scheme to write and run MapReduce programs, we designed a functional interface in Scheme and implemented software to all...

The efficiency of MapReduce is closely related to its load balance. Existing works on MapReduce load balance focus on coarse-grained scheduling. This study concerns finegrained scheduling on MapReduce operations, with each operation representing one invocation of the Map or Reduce function. By default, MapReduce adopts the hash-based method to schedule Reduce operations, which often leads to po...

Extract-Transform-Load (ETL) flows periodically populate data warehouses (DWs) with data from different source systems. An increasing challenge for ETL flows is processing huge volumes of data quickly. MapReduce is establishing itself as the de-facto standard for large-scale data-intensive processing. However, MapReduce lacks support for high-level ETL specific constructs, resulting in low ETL ...

MapReduce is arguably the most successful parallelization framework especially for processing large data sets in datacenters comprising commodity computers. However, difficulties are observed in porting sophisticated applications to MapReduce, albeit the existence of numerous parallelization opportunities. Intrinsically, the MapReduce design allows a program to scale up to handle extremely larg...

Integer factorization is a very hard computational problem. Currently no e cient algorithm for integer factorization is publicly known. However, this is an important problem on which it relies the security of many real world cryptographic systems. I present an implementation of a fast factorization algorithm on MapReduce. MapReduce is a programming model for high performance applications develo...

MapReduce[1] is a popular programming framework that is intended for automatical paralellization of computation in the cloud. MapReduce deals with data intensive applications; huge amount of data is first loaded from remote DFS, then copied as intermediate results from Mapper to Reducer, and finally written back to DFS. Along with this large amount of data transfer, many I/O operations are incu...

In this work, we build a large scale reasoning engine under temporal RDFS semantics using MapReduce. We identify the major challenges of applying MapReduce framework to reason over temporal information, and present our solutions to tackle them.