Operator Scheduling in a Data Stream Manager
By Donald Carney, Ugur Çetintemel, A. Rasin, S. Zdonik, Mitch Cherniack, M. Stonebraker. Very Large Data Bases Conference, 2003
321 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/8a8e2873b627088a564c449a3ae9276259814220
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
- Samuel Madden
- M. Stonebraker
- Jennifer Widom
- S. Zdonik
- Hari Balakrishnan
- Daniel J. Abadi
- Shivnath Babu
- Ugur Çetintemel
In Information Systems
- Christos Faloutsos
- Jennifer Widom
- Jimeng Sun
- Daniel J. Abadi
- Samuel Madden
- Hari Balakrishnan
- David Maier
- M. Stonebraker
Citations in the corpus
- Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “In stream processing systems, queries describe their requirements for throughput, latency, and fraction of dropped tuples [22,34,60,79].”
- Linear Road: A Stream Data Management Benchmark (2004, background): “Stream data management has become a highly active research area and has inspired the development of several prototype systems including Aurora [1], STREAM [8], TelegraphCQ [4] and Niagara [5].”
- Fault-tolerance in the Borealis distributed stream processing system (2005, background): “Much work has been done on data models and operators [1, 6, 16, 28, 41], efficient processing [7, 8, 12, 30], and resource management [13, 17, 30, 35, 38] for SPEs.”
- The CQL continuous query language: semantic foundations and query execution (2006, background): “Our semantics does not dictate “liveness” of continuous query output – that issue is relegated to latency management in the query processor [7, 17].”
- The Design of the Borealis Stream Processing Engine (2005, background): “Several groups have developed working prototypes [1, 4, 16] and many papers have been published on detailed aspects of the technology such as data models [2, 5, 46], scheduling [8, 15], and load shedding [9, 20, 44].”
- Streaming Pattern Discovery in Multiple Time-Series (2005, background): “The new challenges have made researchers re-think many parts of traditional DBMS design in the streaming context, especially on query processing using correlated attributes [14], scheduling [6, 8], load shedding [35, 12] and memory requirements [5].”