STREAM: The Stanford Stream Data Manager
By Arvind Arasu, Brian Babcock, Shivnath Babu, Mayur Datar, K. Ito, Rajeev Motwani, I. Nishizawa, Utkarsh Srivastava, Dilys Thomas, Rohit Varma and 1 more. IEEE Data Engineering Bulletin, 2003
675 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/d55310c9b226770fc9f87f6ee3045a00a112b3a3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Matei A. Zaharia
- M. Balazinska
- Arvind Arasu
- Ion Stoica
- Charu C. Aggarwal
- Daniel J. Abadi
- Scott Shenker
- Haoyuan Li
Citations in the corpus
- Twitter Heron: Stream Processing at Scale (2015, background): “Spouts are sources of input data (e.g. a stream of Tweets), and bolts are an abstraction to represent computation on the stream.”
- SPADE: the system s declarative stream processing engine (2008, background): “This paradigm shift has recently created a strong interest in DSPS-related research, in academia [1, 4, 6, 7] and industry [8, 17, 21, 25] alike.”
- Data streams: algorithms and applications (2005, background): “Projects at various universities of this type include Nia-garaCQ [70], Aurora [72], Telegraph [73], Stanford Stream [71] etc.”
- The CQL continuous query language: semantic foundations and query execution (2006, methodology): “In this section we briefly discuss how a DSMS might implement this semantics under realistic conditions.”
- Storm@twitter (2014, background): “We note that Storm traces its lineage to the rich body of work on stream data processing (e.g. [1, 2, 3, 4]), and borrows heavily from that line of thinking.”
- 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].”
- Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters (2012, background): “reduceByKey(_+_) yields a D-Stream of word counts on intervals [0, 5), [1, 6), [2, 7), etc.”
- The 8 requirements of real-time stream processing (2005, background): “In time, process control and automation of industrial facilities, ranging from oil refineries to corn flakes factories, will also move to such firehose data volumes and sub-second latency requirements.”