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Welcome to the Database and Bioinformatics Laboratory at Chungbuk National University. We, more than 200 members including our alumni, are preparing a new progress toward the development of studies as well as technologies about spatiotemporal databases, data mining, and bioinformatics since 1986s.
Our research concentrates on the theories and applications of database and data mining especially spatiotemporal databases, text mining and bioinformatics. Our lab is organized into several teams according to the research aspects: Spatiotemporal Database, Spatiotemporal Data Mining, and Bioinformatics, which are described in detail as below.
We have successfully completed more than 50 projects and research grants funded by KRF(Korea Research Foundation), KOSEF(Korea Science and Engineering Foundation), a KISTEP(Korea Institute of Science & Technology Evaluation and Planning), KAIST(Korea Advanced Institute of Science and Technology), KISTI(Korea Institute of Science and Technology Information), ETRI(Electronics and Telecommunication Research Institute), MIC(Ministry of Information and Communication Republic of Korea), SMBA(Small and Medium Business Administration), etc.
Bioinformatics Group
The focus of our study includes biomedical information extraction, prediction of structural binding site, biological sequence analysis and protein network and protein functional network analysis.
The major topics of interest are:
- Biomedical information extraction, namely named entity recognition, protein-protein interaction identification and molecular event extraction as well as machine learning and data mining approaches for IE
- The semantic similarity measures
- The methods to identify the locations of active sites based on the structural and biochemical features of protein surfaces
- The approaches to predict protein-protein interactions based on the biophysical and chemical characters of protein surfaces
- The strategy to infer the functional relations among proteins based on physical and biochemical characteristics
- The prediction of protein functions
- Motif recognition and analysis about biochemistry, geometry, topology and properties of motif
- The relations between ontology-based similarity of genes and functional properties
- The associations of biologically relevant terms to groups of genes
Spatiotemporal Database Group
Our research focuses on efficient query processing techniques to support spatiotemporal queries, spatiotemporal query optimization and indexing methods for supporting efficient query process. We are also interested in processing of objects with uncertainty.
The major topics of interest include:
- Data management and indexing for ubiquitous/pervasive/wearable computing
- Data management in sensor and mobile ad hoc networks
- Data stream processing in mobile/sensor networks
- Web access and Internet applications using mobile devices
- Context-aware computing and location-based services
- Location tracking of vehicles and moving objects
- Spatiotemporal Data Model for Indeterminate Objects
- Spatiotemporal Aggregate Method
- Multidimensional Indexing Method
- Spatiotemporal Query Processing, especially Continuous Nearest Neighbor Query
- Spatiotemporal Aggregate Reasoning (Histogram, Sampling, etc)
Spatiotemporal Data Mining Group
Since 1999, our research group has been studying data mining. In particular, we have mostly focused on spatial and temporal data mining using data mining methods such as association, classification, clustering, analysis of sequential patterns and so on.
The major topics of interest include:
- Spatial data generalization and specialization
- Spatial association/clustering/classification rule
- Visualization for spatial knowledge
- Temporal association/classification rule
- Sequential pattern and knowledge representation
- Aggregation based on wireless sensor networks
- Query indexing based on wireless sensor networks
- Stream query processing
- Stream data classification
- Attribute/Feature selection measure for multivariate stream data