added_eScience_2019_SMM.pptx
- how to authenticate and authorize
- what's the data structure like
- what is the platforms policy on data collection and ownership
- learn GraphQL and define your own schema
- currently we have Sentiment analysis, NLP Preprocessing, Topic modeling, Automated phrase detection, Name Entity Recognition, and Twitter Network analysis
- you can bring in your own analyses, ideally in Python, but can be any programming languages.
- Alternatively, in each of the above mentioned analyses, if you have specific algorithms or innovated ways to perform that analysis, you are welcomed to bring in those
- Useful links:
- Other cloud computing platform capability other than AWS; for example Azure since we have allocation for that
- Container orchestration - one of the direction we are exploring is to dockerize all the components of the tools, and we plan to scale it up using kubernetes
- Exploring the capability of existing analytics and integrate to the tools: such as Google Trend API, IBM watson, Tableau and etc
- How to advertise SMM project and tools to the community; How to find our target audience and satisfy their needs
- Workflow or standards to include new data source and algorithms