H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 99 Citations 65,446 280 World Ranking 148 National Ranking 89

Research.com Recognitions

Awards & Achievements

2005 - ACM Fellow For contributions to distributed information management.

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Database
  • Programming language

Distributed computing, Wireless sensor network, Scalability, Information retrieval and Data management are his primary areas of study. His work on Fault tolerance as part of his general Distributed computing study is frequently connected to Spark, thereby bridging the divide between different branches of science. Michael J. Franklin interconnects Data acquisition, Real-time computing, Stream processing and Query optimization in the investigation of issues within Wireless sensor network.

The Scalability study combines topics in areas such as Theoretical computer science, Source lines of code, Partition and Transaction processing. The various areas that Michael J. Franklin examines in his Information retrieval study include XML, Data mining and Index. He combines subjects such as Information management and Data science with his study of Data management.

His most cited work include:

  • Spark: cluster computing with working sets (3481 citations)
  • Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing (3062 citations)
  • TAG: a Tiny AGgregation service for Ad-Hoc sensor networks (2753 citations)

What are the main themes of his work throughout his whole career to date?

Michael J. Franklin spends much of his time researching Distributed computing, Database, Scalability, Data science and Data mining. His research investigates the link between Distributed computing and topics such as Computer network that cross with problems in Wireless network. His Database research incorporates elements of Client–server model and Operating system.

The concepts of his Data science study are interwoven with issues in Data management and Big data. His studies deal with areas such as Sargable, Machine learning, Dirty data and Artificial intelligence as well as Data mining. His work is dedicated to discovering how Query optimization, Web query classification are connected with Query expansion and other disciplines.

He most often published in these fields:

  • Distributed computing (25.35%)
  • Database (17.83%)
  • Scalability (13.09%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (8.08%)
  • Data science (12.26%)
  • Machine learning (6.13%)

In recent papers he was focusing on the following fields of study:

Michael J. Franklin mostly deals with Artificial intelligence, Data science, Machine learning, Analytics and Data mining. His Data science research includes themes of Key, Data management and Data discovery. His Analytics research is multidisciplinary, relying on both Workload, Scalability, World Wide Web and Big data.

The study incorporates disciplines such as Multimedia, Distributed computing, Service and Feature vector in addition to Scalability. His biological study spans a wide range of topics, including Python and User interface. His work on Materialized view as part of general Data mining study is frequently linked to Sampling, therefore connecting diverse disciplines of science.

Between 2014 and 2021, his most popular works were:

  • Apache Spark: a unified engine for big data processing (1030 citations)
  • MLlib: machine learning in apache spark (920 citations)
  • Spark SQL: Relational Data Processing in Spark (776 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Database
  • Programming language

His primary scientific interests are in Spark, Machine learning, Artificial intelligence, Scalability and Analytics. His Machine learning study combines topics in areas such as Data labeling, Crowds and Data mining. His Scalability research includes elements of Python, User interface and Iterative and incremental development.

His studies examine the connections between Analytics and genetics, as well as such issues in Data modeling, with regards to Data manipulation language, Sentiment analysis, Data management and Distributed database. His Database study combines topics from a wide range of disciplines, such as Exploratory data analysis and Systems architecture. His Workload study integrates concerns from other disciplines, such as Latency, Real-time computing and Distributed computing.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Spark: cluster computing with working sets

Matei Zaharia;Mosharaf Chowdhury;Michael J. Franklin;Scott Shenker.
ieee international conference on cloud computing technology and science (2010)

5445 Citations

Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing

Matei Zaharia;Mosharaf Chowdhury;Tathagata Das;Ankur Dave.
networked systems design and implementation (2012)

4670 Citations

TAG: a Tiny AGgregation service for Ad-Hoc sensor networks

Samuel Madden;Michael J. Franklin;Joseph M. Hellerstein;Wei Hong.
operating systems design and implementation (2002)

4152 Citations

TinyDB: an acquisitional query processing system for sensor networks

Samuel R. Madden;Michael J. Franklin;Joseph M. Hellerstein;Wei Hong.
international conference on management of data (2005)

2573 Citations

TelegraphCQ: continuous dataflow processing

Sirish Chandrasekaran;Owen Cooper;Amol Deshpande;Michael J. Franklin.
international conference on management of data (2003)

2116 Citations

TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.

Sirish Chandrasekaran;Owen Cooper;Amol Deshpande;Michael J. Franklin.
conference on innovative data systems research (2003)

1726 Citations

Broadcast disks: data management for asymmetric communication environments

Swarup Acharya;Rafael Alonso;Michael Franklin;Stanley Zdonik.
international conference on management of data (1995)

1423 Citations

The design of an acquisitional query processor for sensor networks

Samuel Madden;Michael J. Franklin;Joseph M. Hellerstein;Wei Hong.
international conference on management of data (2003)

1345 Citations

Apache Spark: a unified engine for big data processing

Matei Zaharia;Reynold S. Xin;Patrick Wendell;Tathagata Das.
Communications of The ACM (2016)

1334 Citations

MLlib: machine learning in apache spark

Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks.
Journal of Machine Learning Research (2016)

1282 Citations

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Philip S. Yu

University of Illinois at Chicago

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Panos K. Chrysanthis

Panos K. Chrysanthis

University of Pittsburgh

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Norman W. Paton

Norman W. Paton

University of Manchester

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Joseph M. Hellerstein

Joseph M. Hellerstein

University of California, Berkeley

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Samuel Madden

Samuel Madden

MIT

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Wang-Chien Lee

Wang-Chien Lee

Pennsylvania State University

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Donald Kossmann

Donald Kossmann

Microsoft (United States)

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Walid G. Aref

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Purdue University West Lafayette

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Beng Chin Ooi

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Patrick Valduriez

French Institute for Research in Computer Science and Automation - INRIA

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