Journal ArticleDOI
Apache Spark: a unified engine for big data processing
Matei Zaharia,Reynold Xin,Patrick Wendell,Tathagata Das,Michael Armbrust,Ankur Dave,Xiangrui Meng,Josh Rosen,Shivaram Venkataraman,Michael J. Franklin,Ali Ghodsi,Joseph E. Gonzalez,Scott Shenker,Ion Stoica +13 more
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This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applications.Abstract:
This open source computing framework unifies streaming, batch, and interactive big data workloads to unlock new applicationsread more
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Proceedings Article
PerfIso: performance isolation for commercial latency-sensitive services
Calin Iorgulescu,Reza Azimi,Youngjin Kwon,Sameh Elnikety,Manoj Syamala,Vivek Narasayya,Herodotos Herodotou,Paulo Tomita,Alex Chen,Jack Zhang,Junhua Wang +10 more
TL;DR: It is shown that colocating CPU-intensive jobs with latency-sensitive services increases average CPU utilization from 21% to 66% for off-peak load without impacting tail latency.
Journal ArticleDOI
Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance
TL;DR: By using AI, commercial banks can reduce losses in lending, increase security in processing payments, automate compliance-related work, and improve customer targeting, according to a structured literature review.
Journal ArticleDOI
Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis
TL;DR: The results show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers.
Journal ArticleDOI
Health Big Data Analytics: A Technology Survey
TL;DR: A do-it-yourself review that delivers a holistic, simplified, and easily understandable view of various technologies that are used to develop an integrated health analytic application is provided.
Journal ArticleDOI
Distributed multi-label feature selection using individual mutual information measures
TL;DR: A distributed model to compute a score that measures the quality of each feature with respect to multiple labels on Apache Spark is proposed and results validated through statistical analysis indicate that ENM is able to outperform the reference methods by maximizing the relevance while minimizing the redundancy of the selected features in constant selection time.
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.
Proceedings Article
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Matei Zaharia,Mosharaf Chowdhury,Tathagata Das,Ankur Dave,Justin Ma,Murphy McCauley,Michael J. Franklin,Scott Shenker,Ion Stoica +8 more
TL;DR: Resilient Distributed Datasets is presented, a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner and is implemented in a system called Spark, which is evaluated through a variety of user applications and benchmarks.
Journal ArticleDOI
A bridging model for parallel computation
TL;DR: The bulk-synchronous parallel (BSP) model is introduced as a candidate for this role, and results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.
Proceedings ArticleDOI
Pregel: a system for large-scale graph processing
Grzegorz Malewicz,Matthew H. Austern,Aart J. C. Bik,James C. Dehnert,Ilan Horn,Naty Leiser,Grzegorz Czajkowski +6 more
TL;DR: A model for processing large graphs that has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier.
Proceedings ArticleDOI
Dryad: distributed data-parallel programs from sequential building blocks
TL;DR: The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.