Reference no: EM132411721
Exercise
The Institute of Useless Statistics decided to sweep millions of smartphones throughout the country in order to collect any data referring to users location and displacement.
The CEO of the company we work for wants us to develop an extension module for our user management application in order to be able to gather the data that the Institute is asking for a specific time frame.
Currently, this application stores data for users telephone numbers, as well as their data (name, surname, ID number, ...). Until now, only the last known GPS position for that user was stored. However, in order to provide to the Institute the information they demand, we will have to store all GPS locations for that user in 1-minute intervals in some kind of data structure.
We must take into account that we have a several million clients database and we are asked to provide an operation that stores these GPS positions as quick as possible, in order to avoid bottlenecks and data loss. Moreover, in order to comply with the General Data Protection Regulation (GDPR), all information returned to the Institute must not contain any data that uniquely identifies the client. Therefore, we have performed a client anonymization process and provided the institute with a list of each client identified by a random code.
We have agreed with the Institute of Useless Statistics that they will be periodically asking for the list of GPS positions for a specific date range and a set of random users in order not to provoke an overhead to our system. Therefore, we have to provide them with an operation that, given a specific anonymized client identifier, it provides them with the list of time-ordered GPS positions starting at a specific date and time. Obviously, we are also asked this method to be as efficient as possible.
Taking into account all these features and restrictions, describe which data structures and algorithms of the ones studied in this module you would use to implement the functionalities of inserting GPS positions every minute for all clients, and querying the GPS positions from a start date using the client's anonymized identifier, focusing on providing the maximum performance.