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, I

Source URL: www-bcf.usc.edu

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, I

Source URL: www.cs.utah.edu

TelegraphCQ: Continuous Dataflow Processing for an Uncertain World+ Sirish Chandrasekaran, Owen Cooper, Amol Deshpande, Michael J. Franklin, Joseph M. Hellerstein, Wei Hong*, Sailesh Krishnamurthy, Sam Madden, Vijayshank

Source URL: db.lcs.mit.edu

• Computing
• Data management
• Software
• Database management systems
• Database theory
• Query languages
• Data stream management system
• Relational database management systems
• Relational database
• PostgreSQL
• Dataflow
• Query optimization

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, I

Source URL: nil.csail.mit.edu

• Computing
• Hadoop
• Apache Software Foundation
• Parallel computing
• Apache Spark
• Cluster computing
• Java platform
• Apache Hadoop
• Data-intensive computing
• MapReduce
• Apache HBase
• PageRank

Spark: Cluster Computing with Working Sets Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica University of California, Berkeley Abstract MapReduce/Dryad job, each job must reload the data

Source URL: people.csail.mit.edu

• Computing
• Hadoop
• Apache Software Foundation
• Parallel computing
• Cluster computing
• Java platform
• Apache Spark
• MapReduce
• Data-intensive computing
• Apache Hadoop
• Apache Hive
• Scala

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, I

Source URL: www.cs.princeton.edu

• Computing
• Mathematics
• Apache Software Foundation
• Hadoop
• Combinatorics
• Apache Spark
• Cluster computing
• Java platform
• MapReduce
• Apache Hadoop
• Partition
• RDD

Design Considerations for High Fan-in Systems: The HiFi Approach Michael J. Franklin*, Shawn R. Jeffery*, Sailesh Krishnamurthy*, Frederick Reiss*, Shariq Rizvi*, Eugene Wu*, Owen Cooper*, Anil Edakkunni*, and Wei Hong+

Source URL: www-db.cs.wisc.edu

• Computing
• Data management
• Cognition
• Information systems
• Lowest common ancestor
• Theoretical computer science
• Radio-frequency identification
• Wireless sensor network
• Data integration
• Data
• Sensor node
• Complex event processing

Functional Dependency Generation and Applications in pay-as-you-go data integration systems∗ Daisy Zhe Wang∗ , Luna Dong† , Anish Das Sarma‡ , Michael J. Franklin∗ , and Alon Halevy§ UC Berkeley and

Source URL: webdb09.cse.buffalo.edu

• Data management
• Computing
• Information retrieval
• Database theory
• Data modeling
• Relational model
• Functional dependency
• Data integration
• BoyceCodd normal form
• Database
• Data model
• Relation

A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data Jiannan Wang, Sanjay Krishnan, Michael J. Franklin, Ken Goldberg, Tova Milo # , Tim Kraska † UC Berkeley, #

Source URL: www.cs.tau.ac.il

• Measurement
• Statistical theory
• Error
• Statistical inference
• Sampling
• Errors and residuals
• Statistics
• Confidence interval
• Outlier
• Bias of an estimator
• Sample size determination
• Statistical power

Spark: Cluster Computing with Working Sets Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica University of California, Berkeley Abstract MapReduce/Dryad job, each job must reload the data

Source URL: www.usenix.org