{"id":111952,"date":"2023-04-27T13:48:18","date_gmt":"2023-04-27T17:48:18","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=111952"},"modified":"2024-03-01T15:41:38","modified_gmt":"2024-03-01T19:41:38","slug":"network-construction-on-historical-and-real-time-data","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/network-construction-on-historical-and-real-time-data\/","title":{"rendered":"Network Construction on Historical and Real-Time Data"},"content":{"rendered":"\r\n

Draco Xu<\/h4>\r\n\r\n\r\n\r\n

Advisors<\/h2>\r\n\r\n\r\n\r\n

Fatemeh Nargesian<\/p>\r\n\r\n\r\n\r\n

Committee Members<\/h2>\r\n\r\n\r\n\r\n

Jiebo Luo, Chen Ding<\/p>\r\n\r\n\r\n\r\n

Abstract<\/h2>\r\n\r\n\r\n\r\n

A complex network represents the dynamic of systems by the interactions of a set of anomaly time series. A core task to enable network dynamics analysis on large-scale data is the efficient computation and update of the correlation matrix for user-defined time windows on historical and real-time data. We present TSUBASA, an algorithm for efficiently computing the exact pair-wise time-series correlation based on Pearson\u2019s correlation. By pre-computing simple and low-overhead sketches, TSUBASA can efficiently compute exact pair-wise correlations on arbitrary time windows at query time. For real-time data, TSUBASA proposes a fast and incremental way of updating the correlation matrix. We provide a detailed time and space complexity analysis of TSUBASA. Our experiments show that with the same space overhead as a DFT-based approximate solution, TSUBASA has a lower sketching time and is on par with the approximate solution with respect to query time. TSUBASA is at least one order of magnitude faster than a baseline for both historical and real-time data. We demonstrate <\/span>tsupy<\/span>, a Python library, which extends <\/span>Jupyter<\/span> Notebook as instrumentation for performing climate network construction and analysis at interactive speed. This demonstration focuses on how <\/span>tsupy<\/span> enables dynamic network analysis on climate data. We also show how <\/span>tsupy<\/span> can be applied to neuro-imaging to understand the functional connectivity between brain regions.<\/span><\/span>\u00a0<\/span><\/p>\r\n

\"\"<\/p>\r\n","protected":false},"excerpt":{"rendered":"

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