2020 - ACM Fellow For contributions to data management and sensor computing systems
His primary scientific interests are in Wireless sensor network, Database, Distributed computing, Real-time computing and Query optimization. His Wireless sensor network research incorporates themes from Data acquisition and Key distribution in wireless sensor networks, Wireless network. Database is frequently linked to Data science in his study.
His Distributed computing research focuses on subjects like Set, which are linked to Adaptive optimization and Response time. His Real-time computing research is multidisciplinary, incorporating perspectives in Simulation, Software deployment, Global Positioning System and Data visualization. His studies in Query optimization integrate themes in fields like Query language, Query expansion and Sargable.
His primary areas of study are Database, Wireless sensor network, Data mining, Distributed computing and Real-time computing. His Wireless sensor network study integrates concerns from other disciplines, such as Data acquisition, Key distribution in wireless sensor networks, Data management and Query optimization. Samuel Madden has researched Query optimization in several fields, including Query expansion and Sargable.
His Data mining research focuses on Set and how it relates to Massively parallel. Real-time computing is closely attributed to Global Positioning System in his study. His Computer network study combines topics in areas such as Wireless and Wireless network.
Samuel Madden spends much of his time researching Artificial intelligence, Analytics, Data mining, Set and Information retrieval. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition. The concepts of his Data mining study are interwoven with issues in Inverted index, Hash function and Global Positioning System.
Samuel Madden focuses mostly in the field of Key, narrowing it down to matters related to Snapshot and, in some cases, Distributed computing. Samuel Madden combines topics linked to Database with his work on Bandwidth. Particularly relevant to Replication is his body of work in Database.
His primary areas of investigation include Artificial intelligence, Set, Data mining, Process and Debugger. His Artificial intelligence research includes elements of Machine learning, Software system, Software deployment and Natural language processing. His study looks at the relationship between Set and fields such as Analytics, as well as how they intersect with chemical problems.
Many of his studies on Data mining apply to Contrast as well. Samuel Madden focuses mostly in the field of Process, narrowing it down to topics relating to Object detection and, in certain cases, Component, Key, Real-time computing and Baseline. The study incorporates disciplines such as Visualization, Workflow and Code in addition to Debugger.
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.
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)
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)
TinyOS: An Operating System for Sensor Networks
Philip Levis;Samuel Madden;Joseph Polastre;Robert Szewczyk.
ambient intelligence (2005)
TelegraphCQ: continuous dataflow processing
Sirish Chandrasekaran;Owen Cooper;Amol Deshpande;Michael J. Franklin.
international conference on management of data (2003)
TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.
Sirish Chandrasekaran;Owen Cooper;Amol Deshpande;Michael J. Franklin.
conference on innovative data systems research (2003)
A comparison of approaches to large-scale data analysis
Andrew Pavlo;Erik Paulson;Alexander Rasin;Daniel J. Abadi.
international conference on management of data (2009)
Model-driven data acquisition in sensor networks
Amol Deshpande;Carlos Guestrin;Samuel R. Madden;Joseph M. Hellerstein.
very large data bases (2004)
CarTel: a distributed mobile sensor computing system
Bret Hull;Vladimir Bychkovsky;Yang Zhang;Kevin Chen.
international conference on embedded networked sensor systems (2006)
C-store: a column-oriented DBMS
Mike Stonebraker;Daniel J. Abadi;Adam Batkin;Xuedong Chen.
very large data bases (2005)
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)
University of Maryland, College Park
University of Chicago
University of California, Berkeley
MIT
University of Maryland, College Park
MIT
Qatar Computing Research Institute
TU Dresden
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
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