Data Science for IoT vs Classic Data Science: 10 Differences
Real Time processing and IoT
IoT involves both fast and big data. Hence, Real Time applications provide a natural synergy with IoT. Many IoT applications like Fleet management, Smart grid, Twitter stream processing etc have unique analytics requirements based on both fast and large data streaming. These include:
- Real time tagging: As unstructured data flows from various sources, the only way to extract signal from noise is to classify the data as it comes. This could involve working with Schema on the fly concepts.
- Real time aggregation: Any time you aggregate and compute data along a sliding time window you are doing real time aggregation: Find a user behaviour logging pattern in the last 5 seconds and compare it to the last 5 years to detect deviation
- Real time temporal correlation: Ex: Identifying emerging events based on location and time, real-time event association from largescale streaming social media data (above adapted from logtrust)
Source: https://www.datasciencecentral.com/profiles/blogs/data-science-for-iot-vs-classic-data-science-10-differences