{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-information-theoretic-approach-to-8","title":"An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams","arxiv_id":null,"date":"2006-01-01","proceeding":"INFORMS 2006 1","authors":["Tamraparni Dasu","Shankar Krishnan","Suresh Venkatasubramanian","Ke Yi"],"abstract":"An important problem in processing large data streams is detecting changes in the underlying distribution that generates the data. The challenge in designing change detection schemes\r\nis making them general, scalable, and statistically sound. In this paper, we take a general,\r\ninformation-theoretic approach to the change detection problem, which works for multidimensional as well as categorical data. We use relative entropy, also called the Kullback-Leibler\r\ndistance, to measure the difference between two given distributions. The KL-distance is known\r\nto be related to the optimal error in determining whether the two distributions are the same\r\nand draws on fundamental results in hypothesis testing. The KL-distance also generalizes traditional distance measures in statistics, and has invariance properties that make it ideally suited\r\nfor comparing distributions.\r\nOur scheme is general; it is nonparametric and requires no assumptions on the underlying\r\ndistributions. It employs a statistical inference procedure based on the theory of bootstrapping,\r\nwhich allows us to determine whether our measurements are statistically significant. The scheme\r\nis also quite flexible from a practical perspective; it can be implemented using any spatial partitioning scheme that scales well with dimensionality. In addition to providing change detections,\r\nour method generalizes Kulldorff’s spatial scan statistic, allowing us to quantitatively identify\r\nspecific regions in space where large changes have occurred.\r\nWe provide a detailed experimental study that demonstrates the generality and efficiency of\r\nour approach with different kinds of multidimensional datasets, both synthetic and real.","url_abs":"https://www.researchgate.net/publication/248542520_An_Information-Theoretic_Approach_to_Detecting_Changes_in_MultiDimensional_Data_Streams","url_pdf":"https://www.cse.ust.hk/~yike/datadiff/datadiff.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-information-theoretic-approach-to-8","repo_url":"https://github.com/mitre/menelaus","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}