The mission of the Key Laboratory of Network Data Science and Technology at the Institute of Computing Technology, Chinese Academy of Sciences (ICT, CAS) is to push the research frontier of data science and innovate Big Data analytics technology. Since the laboratory was founded in 2013, it has published hundreds of papers in top-tier international journals and conferences including TKDE, TOIS, SIGIR, WWW, ACL, CIKM, and WSDM. This key laboratory developed the Big Data analysis engine GoIn. Its award-winning work has been recognized with a Best Paper Award at CIKM 2011, the Best Student Paper award at SIGIR 2012, Best Full Paper runner-up award at CIKM 2017, and Best Paper award at WSDM 2022.
Research Areas
The research areas of the Key Laboratory of Network Data Science and Technology encompass specific tasks for data science, Big Data analytics and Big Data computing.
The research areas of the Key Laboratory of Network Data Science and Technology encompass specific tasks for data science, Big Data analytics and Big Data computing.
Data Science
Information retrieval: Extracting relevant information from Big Data, with a focus on query representation and understanding, learning to rank, neural ranking models, dense retrieval, pre-training for information retrieval (IR), and conversational IR.
Knowledge computing: Leveraging Big Data and knowledge graphs to learn and predict, with an emphasis on named-entity recognition, entity linking, multivariate relational representation learning, event extraction, event prediction, event information complementation, and event relationship inference.
Trustworthy AI: Developing AI algorithms that are trustworthy from a data science perspective, stressing improvements to causal representation learning, structural causal mechanism inference, out-of-distribution prediction, robust machine learning, and trustworthy text generation.
Information retrieval: Extracting relevant information from Big Data, with a focus on query representation and understanding, learning to rank, neural ranking models, dense retrieval, pre-training for information retrieval (IR), and conversational IR.
Knowledge computing: Leveraging Big Data and knowledge graphs to learn and predict, with an emphasis on named-entity recognition, entity linking, multivariate relational representation learning, event extraction, event prediction, event information complementation, and event relationship inference.
Trustworthy AI: Developing AI algorithms that are trustworthy from a data science perspective, stressing improvements to causal representation learning, structural causal mechanism inference, out-of-distribution prediction, robust machine learning, and trustworthy text generation.
Big Data Analysis
Big Data analysis with machine learning: Using representation learning and augmented learning to improve basic algorithms’ accuracy and applicability in functions such as classification, clustering, query, retrieval, matching, correlation analysis, and regression analysis.
Heterogeneous data analysis: Using cognitive computing and deep learning to enhance advanced algorithms’ effectiveness for heterogeneous representation, cross-media extraction, and content understanding; heterogeneous data fusion and analysis, and anomalous pattern recognition.
Big Data analysis for decision making: Leveraging machine learning and knowledge modeling to make predictions and extract insights from Big Data, with a focus on techniques such as knowledge extrapolation and visual analysis, to support decision making and other downstream tasks.
Big Data analysis with machine learning: Using representation learning and augmented learning to improve basic algorithms’ accuracy and applicability in functions such as classification, clustering, query, retrieval, matching, correlation analysis, and regression analysis.
Heterogeneous data analysis: Using cognitive computing and deep learning to enhance advanced algorithms’ effectiveness for heterogeneous representation, cross-media extraction, and content understanding; heterogeneous data fusion and analysis, and anomalous pattern recognition.
Big Data analysis for decision making: Leveraging machine learning and knowledge modeling to make predictions and extract insights from Big Data, with a focus on techniques such as knowledge extrapolation and visual analysis, to support decision making and other downstream tasks.
Big Data Computing
Distributed Big Data management: Developing intelligent analytics hardware and flexible streaming analysis engine to improve the timeliness and scale scalability of complex Big Data analytics.
Cloud-Edge Big Data computing: Developing the cloud-edge computing architecture for Big Data computing to improve the ease of use and engineering capability of Big Data analytic technology.
Distributed Big Data management: Developing intelligent analytics hardware and flexible streaming analysis engine to improve the timeliness and scale scalability of complex Big Data analytics.
Cloud-Edge Big Data computing: Developing the cloud-edge computing architecture for Big Data computing to improve the ease of use and engineering capability of Big Data analytic technology.
Director: Jiafeng Guo
Homepage: http://www.bigdatalab.ac.cn/gjf/
Homepage: http://www.bigdatalab.ac.cn/gjf/
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