Data Systems and Preprocessing
Data systems are computerized systems that hold student, educator and school data and permit users to retrieve information, manage and analyze data. They are referred to under various names, including student information system (SIS), learning management system decision support system and data warehouse.
Data system design aims to improve the way data is collected, stored and recovered within an organization. It is about identifying the most efficient mechanisms for storage and retrieval, designing schemas and models of data and implementing robust security measures. Data system design also involves identifying the best tools and technologies for storing, processing and delivering data.
Big sensor data systems rely on a mix of diverse data sources derived from an array of physical and non-physical sensors, like wireless and mobile devices, wearables, telecommunication networks, and public databases. Each of these sources produces a set sensor readings with their own metrics. The main challenge is to find a resolution that works for the data, and also an aggregation method that allows the sensor data to be presented in a single way with the same metric.
In order to facilitate efficient data analysis, it is necessary to ensure that the data is understood and interpreted correctly. This is why you need to preprocess which covers all the activities that prepare data for further analysis and transformations, including formatting, combination, and replication. Preprocessing is either batch-based or stream-based.