PPT-Ocasta: Clustering Configuration Settings for Error Recovery

Zhen Huang David Lie Department of Electrical and Computer Engineering University of Toronto Outline Motivation Design Evaluation User Study Conclusion 2 Configuration errors Configuration errors are a leading cause of computer failures. And a great value too! We understand the importance of your data and the value of a full, fast and secure recovery. At Data Rescue MDs, our motto is “Lose your fear, not your data!” We demonstrate our data recovery commitment to our customers every day working tirelessly to successfully rescue their data. Our data recovery engineers are HIPAA certified to appropriately manage sensitive data throughout our secure data recovery process. Adapted from Chapter 3. Of. Lei Tang and . Huan. Liu’s . Book. Slides prepared by . Qiang. Yang, . UST, . HongKong. 1. Chapter 3, Community Detection and Mining in Social Media.  Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Ensemble Clustering. unlabeled . data. ……. F. inal . partition. clustering algorithm 1. combine. clustering algorithm . N. ……. clustering algorithm 2. Combine multiple partitions of . given. data . Mitchel Sellers, CEO. IowaComputerGurus Inc.. About Mitchel. Active in the DotNetNuke eco system for about 3 years. Author of . Professional DotNetNuke Module Programming. Provider of support to many clients with varying DotNetNuke installation configurations. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Dheeraj Lokam. Compiler Microarchitecture Lab. Arizona State University. 2. Key Takeaways . 3. Implementing light weight checkpointing at assembly level. Accomplishing a quick recovery . on top of an existing detection . Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Abhishek Agrawal, Senior Lead Program Mgr.. Hemant Mahawar, Senior Program Manager. Ryan Sokolowski, Senior Program Manager. DCIM-B377. Overview of DR technologies . for Cloud OS: . Azure Site Recovery (formerly Hyper-V Recovery Manager), Hyper-V Replica, SQL Always On. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. What is clustering?. Grouping set of documents into subsets or clusters.. The Goal of clustering algorithm is:. To create clusters that are coherent internally, but clearly different from each other. Jongouk Choi. *, . , . *University of Central Florida, .  . NVMW’23. The . Problem. IoT market is . bottlenecked by batteries. 2. Intermittently compute only when enough energy is secured in a capacitor.