Data Systems and Preprocessing

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Data systems are computerized systems which store educational, student, and school data and permit users to access information, manage and analyze data. These systems are known by many names like student information system (SIS), learning management system and decision support system and data warehouse.

Data system design seeks to improve the way data is collected, stored and retrieved within an organisation. It involves determining which methods for retrieval and storage are most efficient, designing schemas and models for data and establishing a robust security. Data system design involves identifying the tools and technologies best for storing, delivering and processing information.

Big sensor data systems are based on a variety of different data sources, sourced from various physical and non-physical sensors, such as mobile and wireless devices as well as wearables, telecommunication networks, and public databases. Each of these sources generates an array of sensor readings, each with their own metrics. The primary challenge is to find a time resolution that is suitable for the data, and also an way to aggregate the sensor data to be portrayed in a single format with an underlying metric.

In order to ensure the accuracy of data analysis, it is important to ensure the information can be understood correctly. Preprocessing is a method that encompasses all the activities that prepare the data for analysis and transformations like formatting combination, formatting, and replication. Preprocessing can be either batch or stream based.