Performance test case generation for microprocessors
Pradip Bose
VTS 1998
Many massive web and communication network applications create data which can be represented as a massive sequential stream of edges. For example, conversations in a telecom- munication network or messages in a social network can be represented as a massive stream of edges. Such streams are typically very large, because of the large amount of un- derlying activity in such networks. An important applica- tion in these domains is to determine frequently occurring dense structures in the underlying graph stream. In gen- eral, we would like to determine frequent and dense patterns in the underlying interactions. We introduce a model for dense pattern mining and propose probabilistic algorithms for determining such structural patterns effectively and ef- ficiently. The purpose of the probabilistic approach is to create a summarization of the graph stream, which can be used for further pattern mining. We show that this summa- rization approach leads to effective and efficient results for stream pattern mining over a number of real and synthetic data sets. © 2010 VLDB Endowment.
Pradip Bose
VTS 1998
Hendrik F. Hamann
InterPACK 2013
Yun Mao, Hani Jamjoom, et al.
CoNEXT 2006
Chidanand Apté, Fred Damerau, et al.
ACM Transactions on Information Systems (TOIS)