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CLOUD
2010
ACM
14 years 2 months ago
Comet: batched stream processing for data intensive distributed computing
Batched stream processing is a new distributed data processing paradigm that models recurring batch computations on incrementally bulk-appended data streams. The model is inspired...
Bingsheng He, Mao Yang, Zhenyu Guo, Rishan Chen, B...
VLDB
2007
ACM
169views Database» more  VLDB 2007»
14 years 9 months ago
Staying FIT: Efficient Load Shedding Techniques for Distributed Stream Processing
In distributed stream processing environments, large numbers of continuous queries are distributed onto multiple servers. When one or more of these servers become overloaded due t...
Nesime Tatbul, Stanley B. Zdonik, Ugur Çeti...
DIS
2009
Springer
14 years 3 months ago
A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams
Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered...
Fabio Fumarola, Anna Ciampi, Annalisa Appice, Dona...
ICDE
2009
IEEE
143views Database» more  ICDE 2009»
14 years 3 months ago
Supporting Generic Cost Models for Wide-Area Stream Processing
— Existing stream processing systems are optimized for a specific metric, which may limit their applicability to diverse applications and environments. This paper presents XFlow...
Olga Papaemmanouil, Ugur Çetintemel, John J...
EAGC
2004
Springer
14 years 2 months ago
Using Global Snapshots to Access Data Streams on the Grid
Data streams are a prevalent and growing source of timely data. As streams become more prevalent, richer interrogation of the contents of the streams are required. Value of the con...
Beth Plale