His primary areas of investigation include Theoretical computer science, Query language, Behavioral targeting, Data mining and Consistency. His Theoretical computer science research incorporates themes from Finite-state machine, Semantics and Distributed computing. Jonathan Goldstein combines subjects such as Complex event processing, Event stream processing and Stream processing with his study of Query language.
His Complex event processing study combines topics from a wide range of disciplines, such as Database, Composability, STREAMS, Semantics and Debugging. His Data mining research includes themes of Set, Curse of dimensionality and k-nearest neighbors algorithm. As a part of the same scientific family, Jonathan Goldstein mostly works in the field of Consistency, focusing on Asynchronous communication and, on occasion, Query optimization.
His main research concerns Data mining, Distributed computing, Information retrieval, Analytics and Database. His Data mining research is multidisciplinary, incorporating perspectives in Data processing system and Set. His Set research is multidisciplinary, incorporating elements of Perspective, Curse of dimensionality and k-nearest neighbors algorithm.
His Distributed computing study integrates concerns from other disciplines, such as Data modeling, Real-time computing, Cloud computing and Latency. His research in the fields of Query expansion and Search engine indexing overlaps with other disciplines such as Similarity. His research in Analytics intersects with topics in Data management, Data analysis and Big data.
Distributed computing, Cloud computing, Analytics, Information retrieval and Set are his primary areas of study. His Distributed computing study also includes fields such as
Jonathan Goldstein interconnects Query expansion, Computation and RDF query language in the investigation of issues within Analytics. His Information retrieval research incorporates elements of Interactive visualization, Data exchange, Data visualization and Index. He has included themes like Data integrity, Tuple and Type in his Set study.
His primary areas of study are Cloud computing, Distributed computing, Field, Parsing and Real-time computing. His Cloud computing research overlaps with other disciplines such as Database tuning, Virtual memory, Stateful firewall, Abstraction and TRILL. His work deals with themes such as Data management, Digital signal processing, Query language, Relational database and Data stream mining, which intersect with Distributed computing.
His research in Field intersects with topics in Data exchange, Information retrieval, Schema and Index. His Parsing study integrates concerns from other disciplines, such as JSON, Data mining, Data flow diagram, Benchmark and Finite-state machine. His Real-time computing study combines topics in areas such as Event, Complex event processing, Event data, Interface and Upstream.
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.
Optimizing queries using materialized views: a practical, scalable solution
Jonathan Goldstein;Per-Åke Larson.
international conference on management of data (2001)
Consistent Streaming Through Time: A Vision for Event Stream Processing
Roger S. Barga;Jonathan Goldstein;Mohamed H. Ali;Mingsheng Hong.
conference on innovative data systems research (2007)
Trill: a high-performance incremental query processor for diverse analytics
Badrish Chandramouli;Jonathan Goldstein;Mike Barnett;Robert DeLine.
very large data bases (2014)
MTCache: transparent mid-tier database caching in SQL server
P.-A. Larson;J. Goldstein;J. Zhou.
international conference on data engineering (2004)
Temporal Analytics on Big Data for Web Advertising
Badrish Chandramouli;Jonathan Goldstein;Songyun Duan.
international conference on data engineering (2012)
Microsoft CEP server and online behavioral targeting
M. H. Ali;C. Gerea;B. S. Raman;B. Sezgin.
very large data bases (2009)
Relaxed currency and consistency: how to say "good enough" in SQL
Hongfei Guo;Per-Åke Larson;Raghu Ramakrishnan;Jonathan Goldstein.
international conference on management of data (2004)
System and method for optimizing queries using materialized views and fast view matching
Per-Ake Larson;Jonathan Goldstein.
Temporal event stream model
Roger S. Barga;Jonathan D. Goldstein;Mohamed Ali;Mingsheng Hong.
Real-time-ready behavioral targeting in a large-scale advertisement system
Badrish Chandramouli;Jonathan Goldstein;Xin Jin;Balan Sethu Raman.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: