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 ArticleDOI
LCJoin: Set Containment Join via List Crosscutting
TL;DR: The prefix tree structure is utilized and extended and the novel list intersection method is extended to operate on the prefix tree to improve the efficiency and share computation in set containment join methods.
Proceedings ArticleDOI
Melanoma Risk Prediction with Structured Electronic Health Records
TL;DR: This is the first to use routinely collected EHR data rather than expert features targeted specifically for melanoma to build a risk model for the disease, and the random forest model achieves similar or better performance than previous models.
Proceedings ArticleDOI
Development of A Predictive Maintenance Platform for Cyber-Physical Systems
Fang-Ning Yang,Huei-Yung Lin +1 more
TL;DR: This paper presents a development and implementation of a predictive maintenance platform based on the cyber-physical system under the Industry 4.0 architecture, targeted at products from manufacturing big data to cloud computing, and predictive maintenance for all factories around the world.
Proceedings ArticleDOI
Parallel Index-based Stream Join on a Multicore CPU
TL;DR: This paper introduces an index data structure, called the partitioned in-memory merge tree, to address the challenges that arise when indexing highly dynamic data, which are common in streaming settings, and proposes a low-cost and effective concurrency control mechanism to meet the demands of high-rate update queries.
Proceedings ArticleDOI
Open-Source Big Data Analytics Architecture for Businesses
TL;DR: Technical, domain-specific, and firm-specific soft challenges related to establishing a big data architecture in an organization, and how these challenges are reshaping the big data research domain are discussed.
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.