Introduction to the Database Lab
The databases laboratory at KAIST was created in 1984 at KAIST, Korea. We have published numerous research papers to top-level conferences and journals such as SIGMOD, VLDB and ICDE. We do research on all areas of database systems. Recent researches range from big data storage management, big data processing and stream data filtering.
Stream data processing
We research on stream data filtering system which is a part of DSMS. To get meaningful data from stream, a stream data filtering system should be more robust to deal a huge amount of stream data.
Big data processing in Solid State Drives
We research on big data processing in SSDs. Flash based solid state disks are replacing hard disk drives owing to its high performance and low energy consumption. We carry out in-depth studies on features of SSDs and modify existing algorithms which are optimized on HDDs.
Efficient data management on Hadoop
We research on various topics on Hadoop. We proposed a novel method to process multiple queries for massive XML data using MapReduce. We also reduce the I/O cost for MapReduce operation. Besides, we introduced a novel approach to reduce storage overhead of Hadoop DFS.
Big Data & Data Mining
As in the case of multidimensional or textual data, we can design mining problems for graph data. This includes techniques such as frequent pattern mining, clustering, and classification. We note that these methods are much more challenging in the graph domain because the structural nature of the data makes the intermediate representation and interpretability of the mining results more complex.
Image & Video Query Processing
The advance of portable recording devices enables anyone to easily produce or manipulate video or image data. Thus, handling such multimedia data has been an important issue in the data management field. We research topic such as image & video data management, automatic object/event detection and annotation, high-dimensional index structure, and image & video query processing and its optimization.
As technology evolves, the average human lifespan is increasing. However, higher average lifespan also increases the mobility rate of disease. To improve quality of life, we need to develop new medicines or reveal ways to cure diseases. But the classical method of developing new drug costs a great deal of money and time. To achieve efficient development of new drugs, we need to use computer resources. Thus, we can extract critical informations in bioinformatics more efficiently.