Approximate join processing over data streams

By Abhinandan Das, Johannes Gehrke, Mirek Riedewald. ACM SIGMOD Conference, 2003

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e2b2b2177b6c84652aee8c2435c1c1c21de94c22

How researchers see this paper

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

In Artificial Intelligence

1. Charu C. Aggarwal
2. Brian Babcock
3. Jennifer Widom
4. Arvind Arasu
5. Christos Faloutsos
6. Shivnath Babu
7. Mayur Datar
8. Rajeev Motwani

In Computer Networks and Communications

1. Charu C. Aggarwal
2. Jennifer Widom
3. Samuel Madden
4. Arvind Arasu
5. Shivnath Babu
6. Brian Babcock
7. Mayur Datar
8. M. Stonebraker

In Signal Processing

1. Charu C. Aggarwal
2. Arvind Arasu
3. Jennifer Widom
4. Shivnath Babu
5. Samuel Madden
6. Brian Babcock
7. Christos Faloutsos
8. Mayur Datar

Citations in the corpus

• 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): “The sliding-window join of two streams is an operation that has received a great deal of attention, e.g., [10, 22 , 38].”
• 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].”
• Load shedding for aggregation queries over data streams (2004, methodology): “For this reason, the techniques proposed in [9, 18] are not applicable to our problem setting, where there are no joins between streams and the metric being optimized is the accuracy of approximate answers to aggregate queries.”
• 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].”
• Data Streams: Models and Algorithms (Advances in Database Systems) (2006, background): “[7] Aggarwal C, Han J., Wang J., Yu P. (2004). On-Demand Classification of Evolving Data Streams.”