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Anomaly 2 demo12/30/2023 ![]() We have released both the kernel-version and the stand-alone version of Simba system, which is a Spark/Spark-SQL extention to in-memory cluster-based large scale spatial data analytical engine.The first release contains the PosgreSQL (9.4.2) version that has fully integrated online aggregation (with the support for SPJ and Group By queries, including joins over multiple tables) into the kernel of PostgreSQL. We are excited to open source the first release of the XDB (approXimate DB) system.Dong Xie is one of the 10 Microsoft Research PhD Fellows for Class of 2018-2019! See the news release for more details.Zhuoyue Zhao received the Google PhD fellowshipof Class 2019 in Structured Data and Database Management: Google PhD Fellows.Yanqing Peng received the Google PhD fellowshipof Class 2020 in Structured Data and Database Management: Google PhD Fellows.I was a member of the Data Group at Utah. A more detailed version about myself is here. In summer 2007, and was an Assitant Professor in the Computer Science Department at Florida State University from Aug 2007 to Aug 2011. Obtained my Ph.D in computer science from theĬomputer Science Department at Boston University I wasĪ Professor in the School of Computing at University of Utah. I currently lead the database team at Alibaba/Alibaba Cloud. Zone-resiliency for Anomaly Detector resources is available by default and managed by the service itself.Formerly Professor at SoC, Now at Alibaba Cloud, ACM Fellow, IEEE Fellow No customer configuration is necessary to enable zone-resiliency. How do I configure the Anomaly Detector service to be zone-resilient? The Anomaly Detector service is zone-resilient by default. Service availability and redundancy Is the Anomaly Detector service zone resilient? In the notebook, add your valid Anomaly Detector API subscription key to the subscription_key variable, and change the endpoint variable to your endpoint. To run the Notebook, you should get a valid Anomaly Detector API subscription key and an API endpoint. This Jupyter Notebook shows you how to send an API request and visualize the result. ![]() To learn how to call the Anomaly Detector API, try this Notebook. To run the demo, you need to create an Anomaly Detector resource and get the API key and endpoint. DemoĬheck out this interactive demo to understand how Anomaly Detector works. You would have to look at all those time series signals from those sensors to decide whether there is system level issue. Each of these assets has tens or hundreds of different types of sensors. For example, you have an expensive physical asset like aircraft, equipment on an oil rig, or a satellite. Particularly, when any individual time series won't tell you much, and you have to look at all signals (a group of time series) holistically to determine a system level issue. If your goal is to detect system level anomalies from a group of time series data, use multivariate anomaly detection APIs. For example, you want to detect daily revenue anomalies based on revenue data itself, or you want to detect a CPU spike purely based on CPU data. If your goal is to detect anomalies out of a normal pattern on each individual time series purely based on their own historical data, use univariate anomaly detection APIs. When to use Univariate Anomaly Detector v.s. The underlying model used is Graph attention network. The model was selected automatically based on your data pattern.ĭetect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. Featureĭetect anomalies in one variable, like revenue, cost, etc. With the Anomaly Detector, you can either detect anomalies in one variable using Univariate Anomaly Detector, or detect anomalies in multiple variables with Multivariate Anomaly Detector. The tutorials are longer guides that show you how to use this service as a component in broader business solutions.The conceptual articles provide in-depth explanations of the service's functionality and features.The how-to guides contain instructions for using the service in more specific or customized ways.The quickstarts are step-by-step instructions that let you make calls to the service and get results in a short period of time.This documentation contains the following types of articles: Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little ML knowledge, either batch validation or real-time inference.
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