Enrich project initialization

CONTRIBUTING.md

@@ -0,0 +1,21 @@
+# Contributing to this project
+
+This project welcomes contribution from all fellow developers or machine learning researchers.
+
+## Code of Conduct
+Contributors and participators are expected to adhere to a ethical code of common sense, where unacceptable 
+behavior will not be tolerated.
+
+## How to contribute?
+We welcome multiple types of contributions:
+
+* Code (General/ Algorithms)
+* Documentation
+* Tests
+* Bug reports
+* Feature requests
+* Blogs and user feedbacks
+
+## How to report an issue?
+
+Please refer to the issues section.

README.md

@@ -1,11 +1,80 @@
-# sw-alert-engine
-This is a POC repository of the Apache SkyWalking AIOps Alert Engine.
+# SkyWalking AIOps Engine
+**An AIOps Engine for Observability.**
 
-This project covers a experimental ML pipeline intergration with SkyWalking OAP backend.
+A usable open-source AIOps framework for the domain of cloud computing observability. 
+
+### Why this project matters?
+We could answer this from the following progressive questions:
+1. Are there existing algorithms for telemetry data? 
+   - **Abundant.**
+
+2. Are the existing algorithms empirically verified? 
+
+   - **Most proposed algorithms are not empirically verified**
+
+3. Are there AIOps tools that embed machine learning algorithms? 
+   - **Limited, often out of maintenance or commercialized.**
+
+4. Are there open-source AIOps solutions that integrates with popular backends?
+   - **Hardly any.**
+
+5. Why would I need that?
+   1. For developers & organizations curious for AIOps:
+      - a. Just install and start using it, saves budget, saves head-scratching.
+      - b. Treat this project as a good (or bad) reference for your own AIOps pipeline.
+   2. For researchers in the AIOps domain:
+      - a. For software engineering researchers - sample for AIOps evolution and empirical study.
+      - b. For algorithm researchers - playground for new algorithms, solid case studies.
+
+
+The above is where we place the value of this project, though our current aim is to become the official AIOps engine 
+of [Apache SkyWalking](https://github.com/apache/skywalking), each component could be easily swapped given its 
+plugable design.
+
+### Current Goal
+
+At the current stage, it serves as an **anomaly detection** engine, in the future, we will also explore root cause analysis and 
+automatic problem recovery.
 
 This is also the tentative repository for OSPP 2022 and GSOC 2022 student project outcomes.
 
 Project `Exploration of Advanced Metrics Anomaly Detection & Alerts with Machine Learning in Apache SkyWalking`
 
 Project `Log Outlier Detection in Apache SkyWalking`
 
+### Architecture
+
+**TBA**
+
+**Data pulling:**
+
+The current data pulling and retention rely on a common set of ingestion methods, with a 
+first focus on SkyWalking OAP GraphQL and static file loader. We maintain a local storage for processed data.
+
+**Alert component:**
+
+An anomaly does not directly trigger an alert, it 
+goes through a tolerance mechanism.
+
+### Roadmap
+
+Phase 0 (current)
+1. [ ] Implement essential development infrastructure.
+2. [ ] Implement naive algorithms as baseline & pipline POC (on existing datasets).
+3. [ ] Implement a SkyWalking `GraphQLDataLoaderProvider` to test data pulling.
+
+Phase 1 (summer -> fall 2022, OSPP & GSOC period)
+1. [ ] Implement the remaining core default providers.
+2. [ ] **Research and implement algorithms with OSPP & GSOC students.**
+3. [ ] Integrate with Apache Airflow for orchestration.
+5. [ ] Evaluation based on benchmark microservices systems (anomaly injection).
+6. [ ] MVP ready without UI-side changes. 
+
+Phase 2 (fall -> end of 2022)
+1. [ ] Join as an Apache SkyWalking subproject.
+2. [ ] Integrate with SkyWalking Backend & rule-based alert module.
+3. [ ] Propose and request SkyWalking UI-side changes.
+4. [ ] First release for end-user testing.
+
+Phase Next 
+1.[ ] Towards production-ready.