A prominent parallel data processing tool MapReduce is gaining significant momentum from both industry and academia as the volume of data to analyze grows rapidly. While MapReduce is used in many areas where massive data analysis is required, there are still debates on its performance, efficiency per node, and simple abstraction. This survey intends to assist the database and open source communities in understanding various technical aspects of the MapReduce framework. In this survey, we characterize the MapReduce framework and discuss its inherent pros and cons. We then introduce its optimization strategies reported in the recent literature. We also discuss the open issues and challenges raised on parallel data analysis with MapReduce.

https://www.cs.arizona.edu/~bkmoon/papers/sigmodrec11.pdf

Parallel data processing with MapReduce: a survey

https://olcf.ornl.gov/wp-content/uploads/2011/10/hybrid.pdf

Implementing molecular dynamics on hybrid high performance computers - short range forces

Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.

/pdf/mr-dbscan-an-efficient-parallel-density-based-clustering-1fspv4fkp2.pdf

MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce

Fuzzy clustering is considered as an important tool in pattern recognition and knowledge discovery from a database; thus has been being applied broadly to various practical problems. Recent advances in data organization and processing such as the cloud computing technology which are suitable for the management, privacy and storing big datasets have made a significant breakthrough to information sciences and to the enhancement of the efficiency of fuzzy clustering. Distributed fuzzy clustering is an efficient mining technique that adapts the traditional fuzzy clustering with a new storage behavior where parts of the dataset are stored in different sites instead of the centralized main site. Some distributed fuzzy clustering algorithms were presented including the most effective one – the CDFCM of Zhou et al. (2013). Based upon the observation that the communication cost and the quality of results in CDFCM could be ameliorated through the integration of a distributed picture fuzzy clustering with the facilitator model, in this paper we will present a novel distributed picture fuzzy clustering method on picture fuzzy sets so-called DPFCM. Experimental results on various datasets show that the clustering quality of DPFCM is better than those of CDFCM and relevant algorithms.

DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets

Hadoop has become the de facto platform for large-scale data analysis in commercial applications, and increasingly so in scientific applications. However, Hadoop's byte stream data model causes inefficiencies when used to process scientific data that is commonly stored in highly-structured, array-based binary file formats resulting in limited scalability of Hadoop applications in science. We introduce Sci-Hadoop, a Hadoop plugin allowing scientists to specify logical queries over array-based data models. Sci-Hadoop executes queries as map/reduce programs defined over the logical data model. We describe the implementation of a Sci-Hadoop prototype for NetCDF data sets and quantify the performance of five separate optimizations that address the following goals for several representative aggregate queries: reduce total data transfers, reduce remote reads, and reduce unnecessary reads. Two optimizations allow holistic aggregate queries to be evaluated opportunistically during the map phase; two additional optimizations intelligently partition input data to increase read locality, and one optimization avoids block scans by examining the data dependencies of an executing query to prune input partitions. Experiments involving a holistic function show run-time improvements of up to 8x, with drastic reductions of IO, both locally and over the network.

SciHadoop: array-based query processing in Hadoop

As the datasets used to fuel modern scientific discovery grow increasingly large, they become increasingly difficult to manage using conventional software. Parallel database management systems (DBMSs) and massive-scale data processing systems such as MapReduce hold promise to address this challenge. However, since these systems have not been expressly designed for scientific applications, their efficacy in this domain has not been thoroughly tested. In this paper, we study the performance of these engines in one specific domain: massive astrophysical simulations. We develop a use case that comprises five representative queries. We implement this use case in one distributed DBMS and in the Pig/Hadoop system. We compare the performance of the tools to each other and to hand-written IDL scripts. We find that certain representative analyses are easy to express in each engine's highlevel language and both systems provide competitive performance and improved scalability relative to current IDL-based methods.

Analyzing massive astrophysical datasets: Can Pig/Hadoop or a relational DBMS help?

http://www.computer.org/csdl/proceedings/cluster/2009/5011/00/05289149.pdf

2009 IEEE International Conference on Cluster Computing and Workshops

Scientists' ability to generate and collect massive-scale datasets is increasing. As a result, constraints in data analysis capability rather than limitations in the availability of data have become the bottleneck to scientific discovery. MapReduce-style platforms hold the promise to address this growing data analysis problem, but it is not easy to express many scientific analyses in these new frameworks. In this paper, we study data analysis challenges found in the astronomy simulation domain. In particular, we present a scalable, parallel algorithm for data clustering in this domain. Our algorithm makes two contributions. First, it shows how a clustering problem can be efficiently implemented in a MapReduce-style framework. Second, it includes optimizations that enable scalability, even in the presence of skew. We implement our solution in the Dryad parallel data processing system using DryadLINQ. We evaluate its performance and scalability using a real dataset comprised of 906 million points, and show that in an 8-node cluster, our algorithm can process even a highly skewed dataset 17 times faster than the conventional implementation and offers near-linear scalability. Our approach matches the performance of an existing hand-optimized implementation used in astrophysics on a dataset with little skew and significantly outperforms it on a skewed dataset.

/pdf/scalable-clustering-algorithm-for-n-body-simulations-in-a-oim9hnqjwc.pdf

Scalable clustering algorithm for N-body simulations in a shared-nothing cluster

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Analyzing massive astrophysical datasets: Can Pig/Hadoop or a relational DBMS help?

2009 IEEE International Conference on Cluster Computing and Workshops

Scalable clustering algorithm for N-body simulations in a shared-nothing cluster