From e3167d25c92cb9c852cdd5100de61c30f62ce9d5 Mon Sep 17 00:00:00 2001 From: lxd1190 Date: Wed, 7 Nov 2018 20:39:21 +0800 Subject: [PATCH] docs(architecture docs): update docs content --- README.en.md | 2 +- README.md | 2 +- docs/architecture.md | 24 ++++++++++++++---------- 3 files changed, 16 insertions(+), 12 deletions(-) diff --git a/README.en.md b/README.en.md index 49ff108..df64f17 100644 --- a/README.en.md +++ b/README.en.md @@ -3,7 +3,7 @@ ![](docs/images/Metis_logo.png) [![license](http://img.shields.io/badge/license-BSD3-blue.svg)](https://github.com/tencent/Metis/master/LICENSE.TXT) -[![Release Version](https://img.shields.io/badge/release-0.1.0-red.svg)](https://github.com/tencent/Metis/releases) +[![Release Version](https://img.shields.io/badge/release-0.2.0-red.svg)](https://github.com/tencent/Metis/releases) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tencent/Metis/pulls) The name **Metis** is taken from the Greek goddess of wisdom, Metis, which is a collection of application practices in the AIOps field. It mainly solves the problem of intelligent operation and maintenance in terms of quality, efficiency and cost. The current version of the open source time series anomaly detection learnware is to solve the anomaly detection problem of time series data from the perspective of machine learning. diff --git a/README.md b/README.md index 39a3a27..2aaec47 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ ![](docs/images/Metis_logo.png) [![license](http://img.shields.io/badge/license-BSD3-blue.svg)](https://github.com/tencent/Metis/master/LICENSE.TXT) -[![Release Version](https://img.shields.io/badge/release-0.1.0-red.svg)](https://github.com/tencent/Metis/releases) +[![Release Version](https://img.shields.io/badge/release-0.2.0-red.svg)](https://github.com/tencent/Metis/releases) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tencent/Metis/pulls) **Metis** 这个名字取自希腊神话中的智慧女神墨提斯（Metis），它是一系列AIOps领域的应用实践集合。主要解决在质量、效率、成本方面的智能运维问题。当前版本开源的时间序列异常检测学件，是从机器学习的角度来解决时序数据的异常检测问题。 diff --git a/docs/architecture.md b/docs/architecture.md index a21d405..e5911c5 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -1,21 +1,25 @@ -## ʱ쳣ѧļܹ +## 时间序列异常检测学件的架构 ![code_arch.png](images/code_arch.png) -ʱ쳣ѧֲ漰ԷΪIJ㣺 +时间序列异常检测工程的整体分层，可以分为以下五层： -1. **ݲ㣨DB**洢쳣ϢϢϢ +1. **数据层（DB）**：存储检测异常信息、样本信息、任务信息等 -2. **(server)** 㻮ΪĴģ +2. **服务层(SERVICE)**： 服务层划分为两大模块 - 1. **ģDAO** װ˺DB㳣ݲӿڡ + 1. **数据驱动模块DAO**： 封装了和DB层常见的数据操作接口。 - 2. **ģfeature** ṩʱеͳڶʱݽȡڼලѧϰѵʹá + 2. **业务模块service**： 完成API层的具体业务逻辑。 - 3. **㷨ģfeature** ṩļֻѧϰ㷨װͳб㷨ָƶƽ㷨ʽ㷨GBDTxgboostȣڶݽٲü⡣ +3. **学件层(LEARNWARE)**：学件层划分为三大模块 + + 1. **检测模块detect**： 提供时间序列异常检测接口 - 4. **ҵģbusiness** ҵģǻԭӽӿڷװAPIľҵ߼ + 2. **特征计算模块features**： 提供三类时间序列的特征（统计特征、拟合特征、分类特征）用于对时序数据进行特征提取，在监督学习和训练中使用。 -3. **ӿڲ(api)** ṩAPIʱ쳣ӿںWEBIJӿڡ + 3. **算法模块algorithm**： 提供常见的几种机器学习算法封装（统计判别算法、指数移动平均算法、多项式算法、GBDT和xgboost等）用于对序数据进行联合仲裁检测。 + +4. **接口层(API)**： 提供API能力，时间序列异常检测接口和WEB管理的操作接口。 -4. **WEB(web)** ϵͳṩWEBͨ棬ûԽ쳣ѯעģѵȲ \ No newline at end of file +5. **WEB层(WEB)**： 系统提供的WEB服务，通过服务界面，用户可以进行异常查询、打标标注、样本库管理、模型训练等操作。 \ No newline at end of file