What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Computer network
Bugra Gedik mostly deals with Distributed computing, Stream processing, Scalability, Real-time computing and Parallel computing.
His Distributed computing research incorporates elements of Computation and Parallel processing.
His Stream processing study combines topics in areas such as Middleware, Stream, Software design pattern and Programming paradigm.
His biological study spans a wide range of topics, including Computer network and Mobile computing.
His Mobile computing study integrates concerns from other disciplines, such as Location-based service, Personalization and Computer security, Information privacy, Anonymity.
His Parallel computing research includes themes of Compiler and Operator.
His most cited work include:
- Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms (717 citations)
- Location Privacy in Mobile Systems: A Personalized Anonymization Model (624 citations)
- SPADE: the system s declarative stream processing engine (462 citations)
What are the main themes of his work throughout his whole career to date?
Bugra Gedik mainly investigates Stream processing, Distributed computing, Real-time computing, Scalability and Parallel computing.
His studies in Stream processing integrate themes in fields like Programming language, Operator, Database, Data stream mining and Code generation.
As a part of the same scientific study, Bugra Gedik usually deals with the Distributed computing, concentrating on Computer network and frequently concerns with Overlay network and Anonymity.
The Scalability study combines topics in areas such as Set, Data mining and Mobile computing.
His Mobile computing study incorporates themes from Node and Overhead.
Bugra Gedik interconnects Tuple and Data stream processing in the investigation of issues within Parallel computing.
He most often published in these fields:
- Stream processing (40.46%)
- Distributed computing (28.32%)
- Real-time computing (19.08%)
What were the highlights of his more recent work (between 2014-2021)?
- Data mining (12.72%)
- Stream processing (40.46%)
- Scalability (16.76%)
In recent papers he was focusing on the following fields of study:
Bugra Gedik spends much of his time researching Data mining, Stream processing, Scalability, Service provider and Matching.
His work deals with themes such as Metric, Social network, Inference, PageRank and Dynamic network analysis, which intersect with Data mining.
His study focuses on the intersection of Inference and fields such as Computation with connections in the field of Distributed computing.
His work in Distributed computing tackles topics such as Heuristics which are related to areas like Vertex and Graph.
Stream processing is a subfield of Parallel computing that Bugra Gedik studies.
His work carried out in the field of Scalability brings together such families of science as Matching, Broadcasting, Multicast, Anycast and Load balancing.
Between 2014 and 2021, his most popular works were:
- Incremental k-core decomposition: algorithms and evaluation (30 citations)
- Safe Data Parallelism for General Streaming (29 citations)
- Sliding windows over uncertain data streams (18 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Computer network
Bugra Gedik mainly focuses on Data stream mining, Stream processing, Scalability, Data parallelism and Parallel computing.
The concepts of his Data stream mining study are interwoven with issues in Default gateway, Processing and Extensibility, Operating system, Big data.
The study incorporates disciplines such as Group method of data handling and Tuple in addition to Stream processing.
His research investigates the connection between Tuple and topics such as Uncertain data that intersect with issues in Theoretical computer science.
He performs multidisciplinary study in Scalability and Throughput in his work.
His Data parallelism research is multidisciplinary, incorporating perspectives in Compiler, Task parallelism, Parallel processing and Runtime system.
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