Intelligent Dashboards for Car Parking Flow Monitoring

Abstract

This study shows the importance of online analytical processing (OLAP), the efficiency and effectiveness in supporting decision-making and how the entire flow is crucial for developing smart cities. Without a complete discharge from data collection, extraction, transformation and loading (ETL), data warehouse building, KPIs’ (key performance indicators) development and building of multidimensional cubes, it would be impossible to get practical and valuable information from the city sensors. In this sense, a study was conducted to understand the data context and what information it could extract from them. Then, a model was built to store this data. Some schemas and pre-aggregations were defined to support the parking flow monitoring. These tasks allowed us to extract valuable information from the data and assist in decision-making. The speed and veracity of the indicators obtained enabled the team to conclude that the OLAP mechanism is an asset that allows decision-makers to have a quality data analysis tool. This mechanism makes it possible to determine several indicators, such as which park is the busiest by week and which has the total capacity. This way, the solution consists of six indicators and a data warehouse with four dimensions and two fact tables containing the data necessary to respond to the predefined indicators.