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Nesime Tatbul
Researcher at Intel
Publications - 126
Citations - 8419
Nesime Tatbul is an academic researcher from Intel. The author has contributed to research in topics: Stream processing & Query optimization. The author has an hindex of 33, co-authored 115 publications receiving 7753 citations. Previous affiliations of Nesime Tatbul include ETH Zurich & École Polytechnique.
Papers
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Book ChapterDOI
A Workflow System through Cooperating Agents for Control and Document Flow over the Internet
Asuman Dogac,Yusuf Tambag,Arif Tumer,M. Ezbiderli,Nesime Tatbul,N. Hamali,C. Icdem,Catriel Beeri +7 more
TL;DR: An architecture that provides for automating and monitoring the flow of control and document over the Internet among different organizations, thereby creating a platform necessary to describe higher order processes involving several organizations and companies is described.
Ariadne: Managing Fine-Grained Provenance on Data Streams
TL;DR: Ariadne as mentioned in this paper uses operator instrumentation to generate and propagate fine-grained provenance through several operators of a query network and decouple provenance computation from query processing to reduce run-time overhead and avoid unnecessary provenance retrieval.
Journal Article
Handling Shared, Mutable State in Stream Processing with Correctness Guarantees.
Nesime Tatbul,Stan Zdonik,John Meehan,Cansu Aslantas,Michael Stonebraker,Kristin Tufte,Chris Giossi,Hong Quach +7 more
TL;DR: This paper explores a number of correctness guarantees required to handle shared, mutable state in streaming applications, including a new model of stream processing that provides support for ACID transactions.
Posted Content
Precision and Recall for Range-Based Anomaly Detection
TL;DR: A new mathematical model is presented to express range-based anomalies, anomalies that occur over a range (or period) of time, to improve classical anomaly detection.
Posted Content
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
Alam Mohammad Mejbah Ul,Justin Gottschlich,Nesime Tatbul,Javier S. Turek,Timothy G. Mattson,Abdullah Muzahid +5 more
TL;DR: This paper presents AutoPerf - a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry.