Data Mining
The abundance of available data, such as images, time series, trees and graphs, allows for educators and governments to gain previously unavailable insights through a deeper understanding of the data and make better predictions with real-world datasets. Our group is focused on discovering knowledge from data and developing tools that improve the process of data analytics. These analytics have created real-world, trustworthy intelligence. We work on a variety of techniques, including pattern mining, deep learning and generative models. These techniques provide tools to address issues ranging from data summary and search, to time series analysis, to space weather forecasting.
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Jason Wang |
Research Areas: Data mining, machine learning, deep learning, explainable AI, generative AI, trustworthy AI, data science Mining Big Data Through Deep Learning We are designing new deep learning algorithms for mining big data. We have developed a 3D-atrous convolutional neural network, used it as a deep visual feature extractor and stacked convolutional long short-term memory networks on top of the feature extractor. This allows us to capture not only deep spatial information but also long-term temporal information in the data. In addition, we use stacked denoising autoencoders to learn latent representations of the data, which can be used to construct feature vectors suitable for classification. We also develop new recurrent neural networks to mine time-series data for space weather prediction. Currently, we are building a deep learning framework with generative adversarial networks and conditional diffusion models. The framework can handle model uncertainty as well as data uncertainty and sparsity. Our deep learning models are suited for big data applications that have few, incomplete, imperfect, missing, noisy or uncertain training data. |
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Dimitri Theodoratos |
Research Areas: Data mining, pattern extraction Mining Big Data Through Deep Learning Extracting frequent patterns hidden in trees and graphs is critical for analyzing data and a first step for downstream data mining. Most pattern-mining algorithms do not scale to big data applications. We have designed algorithms to extract patterns from large trees and graphs, leveraging results using compressed bitmap views. Searching Structured and Semi-Structured Data with Keyword Queries Disambiguating a user’s intention in posing a keyword query and efficiently retrieving relevant results is an immense challenge for keyword search when using big data. We have devised an approach that exploits a structural summary of the data to extract pattern graphs for keyword queries. This empowers nonexpert users to extract information from data sources and databases without mastering a query language and without any knowledge of the organization or structure of data sources. |
