Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other d...
Christopher Potter, Haibin Cheng, Pang-Ning Tan, S...
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...
We study the problem of schema evolution in the RDF data model. RDF and the RDFS schema language are W3C standards for flexibly modeling and sharing data on the web. Although sch...
Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing coclustering algorithms are hard partitioning, assigning each...
— Applications raising in many scientific fields exhibit both data and task parallelism that have to be exploited efficiently. A classic approach is to structure those applica...