Flux: an adaptive partitioning operator for continuous query systems

By Mehul A. Shah, Joseph M Hellerstein, Sirish Chandrasekaran, M. Franklin. Proceedings / International Conference on Data Engineering, 2003

364 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/dfb20c96a57e150665b7163db2b8f71308f1c6f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. M. Tamer Özsu
2. Lukasz Golab
3. M. Stonebraker
4. S. Zdonik
5. M. Balazinska
6. Daniel Mills
7. Sam Whittle
8. Reuven Lax

In Hardware and Architecture

1. M. Stonebraker
2. Daniel Mills
3. Sam Whittle
4. Reuven Lax
5. Sam McVeety
6. Tyler Akidau
7. Slava Chernyak
8. A. Rasin

Citations in the corpus

• Issues in data stream management (2003, background): “Distributed optimization strategies aim at decreasing communication costs by re-ordering query operators across sites [19, 59] and performing simple query functions (e.”
• StreamCloud: An Elastic and Scalable Data Streaming System (2012, neutral): “Further, Flux’s evaluation was performed using simulations and focused on a single operator.”
• High-availability algorithms for distributed stream processing (2005, background): “Recently, there has been much work on data-stream processing (e.g., Aurora [1, 5], STREAM [18], TelegraphCQ [6]), including proposals for distributed engines [7, 21 ].”
• MillWheel: Fault-Tolerant Stream Processing at Internet Scale (2013, methodology): “We observe similarities between components of our implementation and their counterparts in streaming SQL, such as the use of partitioned operators in Flux [28] for load balancing.”