Big Data Begets Big Database Theory

Suciu, Dan

doi:10.1007/978-3-642-39467-6_1

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Parallel database theory questions characterization of Big Data through 3Vs and suggests alternative dimensions: communication, iteration and failure [46]. Despite high scale-out of a few NoSQL products, the comprehensive survey [15] discusses issues and limitations in horizontal scaling of other NoSQL products.…”

Section: Discussionmentioning

confidence: 99%

Big Data Analytics Using SQL: Quo Vadis?

Sridhar

2018

Lecture Notes in Business Information Processing

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Discussionmentioning

confidence: 99%

Big Data Analytics Using SQL: Quo Vadis?

Sridhar

2018

Lecture Notes in Business Information Processing

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“…For these examples, a popular data processing engine for Big Data is Hadoop MapReduce [27]. The main problem is that data arrives too fast for optimal storage and indexing [28]. There are other several processing platforms for Big Data: Mesos [29], YARN (Hortonworks, Hadoop YARN: A next-generation framework for Hadoop data processing, 2013 (http://hortonworks.com/hadoop/yarn/)), Corona (Corona, Under the Hood: Scheduling MapReduce jobs more efficiently with Corona, 2012 (Facebook)), and so forth.…”

Section: New Challenges For Big Data Sciencementioning

confidence: 99%

High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science

Pop

2014

Advances in High Energy Physics

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Modern physics is based on both theoretical analysis and experimental validation. Complex scenarios like subatomic dimensions, high energy, and lower absolute temperature are frontiers for many theoretical models. Simulation with stable numerical methods represents an excellent instrument for high accuracy analysis, experimental validation, and visualization. High performance computing support offers possibility to make simulations at large scale, in parallel, but the volume of data generated by these experiments creates a new challenge for Big Data Science. This paper presents existing computational methods for high energy physics (HEP) analyzed from two perspectives: numerical methods and high performance computing. The computational methods presented are Monte Carlo methods and simulations of HEP processes, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation in HEP, and Random Matrix Theory used in analysis of particles spectrum. All of these methods produce data-intensive applications, which introduce new challenges and requirements for ICT systems architecture, programming paradigms, and storage capabilities.

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Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data

Sterner

Franz

2017

Biol Theory

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We use "data aggregation" to refer to merging multiple sets of data of the same kind (e.g., multiple collections of specimens or multiple runs of the same experiment) as distinct from "data integration," which refers to combining multiple kinds of data to solve an inference problem (Berman 2013). The limits of this distinction, where aggregation and integration become hard to tell apart, are an important topic outside the scope of this article.

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Big Data Begets Big Database Theory

Cited by 4 publications

References 7 publications

Big Data Analytics Using SQL: Quo Vadis?

Big Data Analytics Using SQL: Quo Vadis?

High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science

Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data

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