2019 - ACM Fellow For contributions to scalable distributed data systems
Her primary areas of study are Distributed computing, Stream processing, Scalability, The Internet and World Wide Web. Her Distributed computing research includes themes of Theoretical computer science, Class, Directed acyclic graph, Node and Key. Magdalena Balazinska interconnects Fault tolerance, High availability, Set and Data stream management system in the investigation of issues within Stream processing.
In her study, Cache and Very large database is inextricably linked to Task, which falls within the broad field of Scalability. Her work on Web navigation as part of general The Internet research is frequently linked to Web application security, thereby connecting diverse disciplines of science. Her World Wide Web study incorporates themes from Control and Software deployment.
Her main research concerns Data mining, Database, Data management, Distributed computing and Data science. Her Data mining research is multidisciplinary, incorporating perspectives in Event, Probabilistic logic, Theoretical computer science and Search engine indexing. Her work carried out in the field of Data management brings together such families of science as Sensor web, Wireless sensor network and World Wide Web.
Her Distributed computing research is multidisciplinary, relying on both Skew, Scalability and Cloud computing. The study incorporates disciplines such as Computer architecture, Data stream mining, Data processing and Relational database in addition to Scalability. Magdalena Balazinska has included themes like High availability and Set in her Stream processing study.
Artificial intelligence, Analytics, Sampling, Machine learning and Database are her primary areas of study. Her Analytics study combines topics from a wide range of disciplines, such as Cloud data, Cloud computing, Codec and World Wide Web. Her work in Cloud data addresses subjects such as Provisioning, which are connected to disciplines such as Distributed computing.
Her Distributed computing research includes elements of Telecommunications network, Overhead and Data stream management system. Her biological study spans a wide range of topics, including Stream processing, Orientation, Computer graphics and Virtual reality. She combines subjects such as Set and Data management with her study of Clustering high-dimensional data.
Magdalena Balazinska spends much of her time researching Analytics, Query optimization, World Wide Web, Data science and Artificial intelligence. Her studies deal with areas such as Representation, State, Relational algebra and Reinforcement learning as well as Query optimization. Her work carried out in the field of World Wide Web brings together such families of science as Cloud data, Cloud computing, myria- and Big data management.
She combines subjects such as Use case, Image and Big data with her study of Data science. Her Artificial intelligence research incorporates elements of Cardinality, Machine learning and Estimation. Magdalena Balazinska works mostly in the field of Estimation, limiting it down to topics relating to Cardinality and, in certain cases, Parameterized complexity, Hash function and Foreign key.
The Design of the Borealis Stream Processing Engine
Daniel J. Abadi;Yanif Ahmad;Magdalena Balazinska;Mitch Cherniack.
conference on innovative data systems research (2005)
HaLoop: efficient iterative data processing on large clusters
Yingyi Bu;Bill Howe;Magdalena Balazinska;Michael D. Ernst.
very large data bases (2010)
Building the Internet of Things Using RFID: The RFID Ecosystem Experience
E. Welbourne;L. Battle;G. Cole;K. Gould.
IEEE Internet Computing (2009)
Scalable Distributed Stream Processing
Mitch Cherniack;Hari Balakrishnan;Magdalena Balazinska;Don Carney.
conference on innovative data systems research (2003)
Characterizing mobility and network usage in a corporate wireless local-area network
Magdalena Balazinska;Paul Castro.
international conference on mobile systems, applications, and services (2003)
SkewTune: mitigating skew in mapreduce applications
YongChul Kwon;Magdalena Balazinska;Bill Howe;Jerome Rolia.
international conference on management of data (2012)
INS/Twine: A Scalable Peer-to-Peer Architecture for Intentional Resource Discovery
Magdalena Balazinska;Hari Balakrishnan;David Karger.
international conference on pervasive computing (2002)
High-availability algorithms for distributed stream processing
J.-H. Hwang;M. Balazinska;A. Rasin;U. Cetintemel.
international conference on data engineering (2005)
Data Management in the Worldwide Sensor Web
M. Balazinska;A. Deshpande;M.J. Franklin;P.B. Gibbons.
IEEE Pervasive Computing (2007)
Advanced clone-analysis to support object-oriented system refactoring
M. Balazinska;E. Merlo;M. Dagenais;B. Lague.
working conference on reverse engineering (2000)
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