This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. The ST...
Rajeev Motwani, Jennifer Widom, Arvind Arasu, Bria...
In data stream processing systems, Quality of Service (or QoS) requirements, as specified by users, are extremely important. Unlike in a database management system (DBMS), a query...
We define a class of algorithms for constructing coresets of (geometric) data sets, and show that algorithms in this class can be dynamized efficiently in the insertiononly (data ...
Discovering the patterns in evolving data streams is a very important and challenging task. In many applications, it is useful to detect the dierent patterns evolving over time and...
We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, conceptevolution, and limited labeled data. Most o...
Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei ...
Querying live media streams captured by various sensors is becoming a challenging problem, due to the data heterogeneity and the lack of a unifying data model capable of accessing...
Due to the resource limitation in the data stream environment, it has been reported that answering user queries according to the wavelet synopsis of a stream is an essential abili...
Dynamic data streams are those whose underlying distribution changes over time. They occur in a number of application domains, and mining them is important for these applications....
This paper presents an implementation of a high-performance network application layer parser in FPGAs. At the core of the architecture resides a pattern matcher and a parser. The ...
The field of e-science currently faces many challenges. Among the most important ones are the analysis of huge volumes of scientific data and the connection of various sciences an...
Richard Kuntschke, Tobias Scholl, Sebastian Huber,...