The Visions of Organizing LinkSmart Hackathon
- Building capacity with the aim of developing a working prototype (in short time?).
- Having requisite infrastructure for hardware, data source, software, and expertise
- Setting up platform of sharing knowledge from experienced developers to new comers in order to keep the ball rolling
- Incorporating new ideas and innovations into the existing LS stack
The Hackathon Roadmap
We will have one hackathon per quarter:
Installation and Maintenance
In this hackathon, we set up devices and keep them running. We document the configuration of the devices and installed applications.
Collection and Storage
In this hackathon, we collect and store the data. Here we make decision of data storage and develop the data codebook. We document the data codebook, data storage system and sample of the collected data
Selection, Analysis and Modeling
In this hackathon, we select, filter, organize and preprocess the data collected from sensors in order to build a machine learning algorithm in the context of a selected problem domain.
In this hackathon, implementation of automated response to the data received, i.e., actuation is accomplished.
Visualization is an overarching activity in all the hackathons. In this hackathon, we visualize the data in the model developed in other hackathons. This hackathon will document the visualized parameters and provides interpretation. The outcome of this hackathon is the recommendation of how to use the knowledge extracted from the data collected by the devices. This will be used for further analysis and/or improving the preprocessing and modeling phase in other Hackathons.
Between all hackathons are API development to channel output of one hackathon to the next so that the processes in each hackathon is as independent as possible.
Session leaders makes 10 minute presentation and serves as TRP (Technical Resource Person) during hackathon
- Setting up and integrating the required systems
- Collection, analysis and visualization sensor data
- Annonymise the data
- Analyse the data
- Publish the result and
- Publishing the process in the form of tutorials, hands-on
- Developed a working prototype
- Understanding and addressing all the technical challenges we will have to face to get to a minimum viable product (MVP).
- To build the confidence to ship an MVP in short deadlines.
- To enable team members to learn a lot from each other and get to know each other better.
Processes and outcomes of the hackathons are documented in: