Research of
Continuous Query Processing Techniques over Data Streams
During the last couple
of years, a number of researchers have paid their attention to data stream
management, which is different from the conventional database management. At present,
the new type of data management system, called data stream management system
(DSMS), has become one of the most popular research areas in data engineering
field. Lots of research projects have made great progress in this area, such as
1. Continuous Query
Processing of Relational Data
For relational data,
like the conventional database query processing, the main issue is how to do
the query optimization. The novel features coming from DSMS include the
continuous changes of distributions and arrival rates over query and data
streams, and also the system overload problem. As a result, this project will
respectively study the scalability and adaptability issues of continuous query
processing as well as the load shedding problem in three years.
2. Monitoring of Query and
Data Streams
The change statistics
of query and data streams can be utilized for query optimization. Due to the
dynamic feature of data stream environment, we need more efficient data
analysis techniques in order to capture the current changes over query and data
streams. As a consequence, this project will respectively study the pattern
mining on query streams, the statistics approximation and burst detection on
data streams in three years.
3. Continuous Query
Processing of Sequential Data
Since the current DSMS
does not support queries on sequence data, this project will study the issues
related to three types of sequence data. In the first year, we will study the
content filtering on multi-valued streams, such as network packets and radio
music. In the second year, we will focus on the content filtering on
multi-attributed streams, such as video films. At last, we will consider the
integration of multiple streams, where both types of sequence data are
involved. We will discuss the related issues such as how to build an efficient
index for all queries on different streams.
In this project, we
will research into the continuous query processing technology and develop the
needed data mining techniques. This project expects to compete with the best
research teams in this area. Moreover, this project also proposes novel
techniques for continuous query processing on sequence data. The research
results will lead the entire area to a new field with more applications and
greatly contribute to the technological and academic promotion of our country.