Research

The goal of our research

Data science lab은 추천시스템과 지식진화의 연구그룹으로 구성됩니다.

Recommendation system

Recommendation systems are computer programs that provide personalized recommendations to users. They analyze users’ past behavior to understand their preferences and interests, and then recommend various products, services, or content to them accordingly.

  • Recommendation algorithms: This is the core technology of recommendation systems. Researchers develop algorithms that analyze users’ past behavior data to provide personalized recommendations.

  • User experience: Researchers study user experience to improve the quality of recommendations. This includes factors such as transparency, diversity, and trustworthiness.

  • Use of recommendation systems: Researchers study how users interact with recommendation systems. For example, how recommendation systems may influence users’ preferences or purchasing behavior.

  • Application areas: Recommendation systems are applied in various fields, such as music, movies, books, shopping, and travel. Researchers study how recommendation systems can be tailored to specific application areas.

Knowledge evolution

The topic of the knowledge evolution is to combine artificial intelligence, the semantic web, and (stream-)database integration techniques to solve complex, fast-changing information on the Web. We leverage general research techniques across information-intensive disciplines, including smart energy, data integration and the social web.

  • Knowledge representation learning: is a subfield of machine learning that focuses on learning high-quality, interpretable representations of knowledge from structured or unstructured data. These representations are typically in the form of graphs or embeddings that can be used for various tasks such as reasoning, prediction, or decision-making. The research areas in knowledge representation learning include:
    • Graph neural networks: Developing deep learning models that can learn representations of graphs to perform various tasks such as node classification, link prediction, or graph classification.
    • Knowledge graph completion: Learning to infer missing relationships between entities in a knowledge graph.
  • Stream reasoning: is a subfield of artificial intelligence that focuses on developing algorithms that can reason over data streams in real-time. It involves processing data as it arrives, making inferences, and updating beliefs in a continuous and online fashion.
    • Incremental reasoning: Developing reasoning algorithms that can update their beliefs as new data arrives.
    • Online learning: Learning models that can adapt to changes in the data stream over time.
    • Temporal reasoning: Reasoning about temporal relationships between events in the data stream.
    • Uncertainty modeling: Modeling and quantifying uncertainty in the reasoning process.
  • Carbon-Aware Computing: is a subfield of computing that focuses on reducing the environmental impact, particularly carbon emissions, of computing resources
    • Energy Consumption Prediction and Management: Techniques for monitoring, predicting, and optimizing energy use in data centers and network systems.
    • Environmental Impact of Cloud Computing and Virtualization: Analyzing the effects of cloud services and virtualization on energy consumption and carbon emissions.
    • Energy-Aware Scheduling Algorithms: Developing algorithms for scheduling computing tasks in an energy-efficient manner.
    • Carbon-Aware Migration: Dynamic migration in the geo-distributed cloud, according to availability of renewable energy sources
  • Theoretical Foundation for Knowledge Representation and Reasoning(KR&R)
    • Graph-based models such as RDF/RDFS, Ontology, and Labeled Property Graph(LPG)
    • Logics such as Description Logic, Description Logic, Metric Temporal Logic, and other logics
    • Graph Query Languages such as Gremlin, Cypher, SPARQL
    • Theoretical investigation that surpasses Complex Event Processing(CEP) and Data Stream Management(DSMS)
    • Notions of knowledge stream reasoning
  • Application areas:
    • Smart-grid/Micro-grid, Energy management systems, Virtual power plants, Electric vehicle charging applications
    • Real-time systems for monitoring air pollution, e.g., TMS(Tele-Monitoring Systems)
    • Knowledge tracing, Scholarly knowledge graph, bibliographic database