1996 - ACM Fellow For contributions to the theory of queuing networks and their application to computer system performance evaluation.
His scientific interests lie mostly in Distributed computing, Computer network, Data mining, Tree and Server. The study incorporates disciplines such as Multiprocessor scheduling, Two-level scheduling, Parallel computing, Earliest deadline first scheduling and Bandwidth in addition to Distributed computing. In his study, Data striping is inextricably linked to Component, which falls within the broad field of Bandwidth.
His work on Local area network and Service as part of his general Computer network study is frequently connected to Waiting time and Processor sharing queue, thereby bridging the divide between different branches of science. His work in the fields of Data mining, such as Data stream mining, overlaps with other areas such as Data stream. Richard R. Muntz has researched Server in several fields, including Piggybacking, Multimedia, Reliability and Type of service.
Distributed computing, Data mining, Computer network, Server and Queueing theory are his primary areas of study. His research on Distributed computing focuses in particular on Distributed database. His Data mining research is multidisciplinary, incorporating elements of Tree and Set.
Richard R. Muntz usually deals with Queueing theory and limits it to topics linked to Mathematical optimization and Markov model. His Parallel computing research includes themes of Multiprocessor scheduling, Rate-monotonic scheduling, Dynamic priority scheduling, Fixed-priority pre-emptive scheduling and Fair-share scheduling. In his research on the topic of Frequent subtree mining, Database is strongly related with Theoretical computer science.
His main research concerns Data mining, Tree, Tree structure, Theoretical computer science and Distributed computing. His Data mining study combines topics from a wide range of disciplines, such as Probabilistic logic and Set. His Tree structure research incorporates themes from Frequent subtree mining and Search tree.
His Theoretical computer science study incorporates themes from Class, Algorithm design, Computational complexity theory and Adaptive system. His research integrates issues of Routing table, Scalability and Computer network, Server in his study of Distributed computing. His studies deal with areas such as Ubiquitous computing and Data cluster as well as Computer network.
Data mining, Tree, Frequent subtree mining, Tree structure and Theoretical computer science are his primary areas of study. His research in Data mining is mostly concerned with Data stream mining. His study looks at the relationship between Data stream mining and topics such as Molecule mining, which overlap with XML.
Tree combines with fields such as Data stream and Boundary in his research. His biological study spans a wide range of topics, including Scalability, Canonical form and Distributed database. His work deals with themes such as Combinatorial explosion, Database and Link/cut tree, which intersect with Tree rotation.
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Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Forest Baskett;K. Mani Chandy;Richard R. Muntz;Fernando G. Palacios.
Journal of the ACM (1975)
STING: A Statistical Information Grid Approach to Spatial Data Mining
Wei Wang;Jiong Yang;Richard R. Muntz.
very large data bases (1997)
A Probabilistic Room Location Service for Wireless Networked Environments
Paul Castro;Patrick Chiu;Ted Kremenek;Richard R. Muntz.
ubiquitous computing (2001)
Staggered striping in multimedia information systems
Steven Berson;Shahram Ghandeharizadeh;Richard Muntz;Xiangyu Ju.
international conference on management of data (1994)
Smart kindergarten: sensor-based wireless networks for smart developmental problem-solving environments
Mani Srivastava;Richard Muntz;Miodrag Potkonjak.
acm/ieee international conference on mobile computing and networking (2001)
Moment: maintaining closed frequent itemsets over a stream sliding window
Yun Chi;Haixun Wang;P.S. Yu;R.R. Muntz.
international conference on data mining (2004)
Frequent Subtree Mining - An Overview
Yun Chi;Richard R. Muntz;Siegfried Nijssen;Joost N. Kok.
Fundamenta Informaticae (2004)
Adaptive piggybacking: a novel technique for data sharing in video-on-demand storage servers
Leana Golubchik;John C. S. Lui;Richard R. Muntz.
Multimedia Systems (1996)
Waiting Time Distributions for Processor-Sharing Systems
E. G. Coffman;R. R. Muntz;H. Trotter.
Journal of the ACM (1970)
Performance Analysis of Disk Arrays under Failure
Richard R. Muntz;John C. S. Lui.
very large data bases (1990)
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