Inspiration

We wanted to make the use of building sensor data to understand the users's behaviours in order to make their lives easier by automating decision making processes. A lot of daily tasks at the building is mostly done manually despite the advancement of technology and therefore with the help of sensors around the buildings, we could reduce the amount of redundant work tasks for employees to focus on what they are good at. This will further motivate the employees of the buildings to increase the work efficiency and satisfaction of work placement.

What it does

Meeting optimizer uses the user's input specification such as date, time, the profile of invited people, the number of people and the type of the meeting to search for the best ranked meeting room to choose from. This will reduce the redundant tasks of going through a list of unsuitable meeting rooms to choose from and in return it shows the best suited top 3 meeting rooms. The less options make the decision making process quicker and easier. Using what the user has chosen for the room provides a valuable data to feed the intelligent machine to learn about what this particular user has chosen for a set of specifications (e.g. date, time, invited people profile, number of people invited) later on. This training data will be used to rank the meeting rooms according to the users' preferences to facilitate an excellent environment for effective and fun meeting. Furthermore, this will help reduce the resources for the building facilitators to allocate the resources more efficiently by duplicating the most popular room options models and by reducing the most undesired meeting room models. On our User Interface, the floorplan shows the intensity of colours to tell the users how frequently the desks or rooms are used by the employees using the data collected from the sensors.

How we built it

We used node.js for the backend and html, css, jquery, and javascript for the frontend. The semantic ui css framework is used for the improved User Experience. We also used CubiCasa Content Platform to provide floor plan material as svg file and tieto data API guides to learn about what kind of data is available to implement. The floor plan shows a visually beautiful and informative data about the desk and meeting room usage. This helps the decision making leaders to decide what rooms are serving the best for their employees' needs. The Floor plan has rooms and desks as elements to click on and it contains information about the frequency of usage, the capacity of room, is it being used, and when they are booked for. This information for each room is used in our algorithm to show the best options to choose from.

Challenges we ran into

Not enough time and sleep. The data was not sufficient to get for our project and therefore we needed to create an example data to work with.

Accomplishments that we're proud of

Visually showing the building data to convey the information quickly to the users. The seamless and easy meeting room booking flow to reduce the amount of time spent on creating event and reserving meeting rooms.

What we learned

We learnt the use of building data and data structure. How to implement User Friendly features to make an intuitive app. Taking raw data and turn them into valuable information to show them visually appealing as well as intuitive to the users.

What's next for Meeting Optimizer

Implementing Machine Learning to learn the user's preferences on which rooms are most wanted and used. This data can provide a valuable feedback to decide how resources should be allocated.

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