What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Database
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 most cited work include:
- When Is ''Nearest Neighbor'' Meaningful? (1799 citations)
- Optimizing queries using materialized views: a practical, scalable solution (278 citations)
- Consistent Streaming Through Time: A Vision for Event Stream Processing. (231 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Data mining (18.82%)
- Distributed computing (17.65%)
- Information retrieval (14.12%)
What were the highlights of his more recent work (between 2014-2020)?
- Distributed computing (17.65%)
- Cloud computing (8.24%)
- Analytics (14.12%)
In recent papers he was focusing on the following fields of study:
Distributed computing, Cloud computing, Analytics, Information retrieval and Set are his primary areas of study.
His Distributed computing study also includes fields such as
- Data modeling and related Wireless sensor network, Online and offline, Relational database and SIGNAL,
- Data management which intersects with area such as Disparate system, Stream processing and Data stream mining,
- Digital signal processing which is related to area like Circular buffer.
As part of the same scientific family, he usually focuses on Relational database, concentrating on Real-time computing and intersecting with Event.
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.
Between 2014 and 2020, his most popular works were:
- Mison: a fast JSON parser for data analytics (40 citations)
- Complex event processor for historic/live/replayed data (38 citations)
- Enabling Signal Processing over Data Streams (13 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Database
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.
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