Book ChapterDOI
Database Techniques for Big Data
Parinaz Ameri
- pp 139-159
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TLDR
It is shown that due to the appearance of new Big Data applications, many obsolete data models resurfaced and have combined with new scalable techniques to provide a proper solution to deal with ever-increasing amount of data.Abstract:
This chapter is dedicated to the challenges that the appearance of Big Data has enforced on databases. It describes the most recent movements in a database community to design scalable solutions. These challenges include strategies for solving consistency and data locality on distributed database systems. Therefore, various NoSQL data models, as well as their advantages and disadvantages in dealing with Big Data, are discussed. It is shown that due to the appearance of new Big Data applications, many obsolete data models resurfaced and have combined with new scalable techniques to provide a proper solution. A brief overview on recent database research trends is also covered to show that NoSQL data models as well as relational ones are under constant evolution to find better solutions for dealing with ever-increasing amount of data.read more
Citations
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Journal ArticleDOI
Big database technologies: shaping the future world
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An Adaptive Index Recommendation System (AIRs) on Document-Based Databases
TL;DR: This thesis focuses on covering the missing aspects of this complicated optimization problem by formulating a mathematical model considering the relevant factors and developing an algorithm and the analysis of its complexity that extracts limited combinations and simultaneously verifies their costs by a query optimizer with index intersection capability.
NoSQL Databases for Big Data Management
Dražena Gašpar,Mirela Mabić +1 more
TL;DR: The aim of this paper is to present the main features of NoSQL databases that make them the first choice in Big Data management.
References
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Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Journal ArticleDOI
A view of cloud computing
Michael Armbrust,Armando Fox,Rean Griffith,Anthony D. Joseph,Randy H. Katz,Andy Konwinski,Gunho Lee,David A. Patterson,Ariel Rabkin,Ion Stoica,Matei Zaharia +10 more
TL;DR: The clouds are clearing the clouds away from the true potential and obstacles posed by this computing capability.
Journal ArticleDOI
A relational model of data for large shared data banks
TL;DR: In this article, a model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced, and certain operations on relations are discussed and applied to the problems of redundancy and consistency in the user's model.
Proceedings Article
Bigtable: A Distributed Storage System for Structured Data (Awarded Best Paper!).
Fay W. Chang,Jeffrey Dean,Sanjay Ghemawat,Wilson C. Hsieh,Deborah A. Wallach,Michael Burrows,Tushar Deepak Chandra,Andrew Fikes,Robert Gruber +8 more
TL;DR: Bigtable as mentioned in this paper is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers, including web indexing, Google Earth and Google Finance.