Time Series Insights Models
Question
You work for an automotive manufacturer which already has an Azure IoT solution in place.
The solution collects millions of data from thousands of field equipment like presses, welding robots, conveyors etc.
The quality department wants to add an improvement which can provide them with near real-time visualization of the trends, by asset categories.
You are suggested to use Time Series Insights models.
Which is not an advantage of TSI models?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: D.
Option A is incorrect because TSI models have a Types feature which allows for defining variables and to add computational rules to them.
Option B is incorrect because both the Hierarchies and the Types features of the TSI models enable enrichment of time series instances, either by organizing them into hierarchies or adding derived computed information to the raw data.
Option C is incorrect because TSI model hierarchies organize instances by specifying names of properties and their relationships.
Option D is CORRECT because Time Series Models don't have any relations to ML models.
Diagram:
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References:
Time Series Insights (TSI) is a fully-managed analytics, storage, and visualization service that makes it simple to explore and analyze billions of events from an IoT solution. TSI provides near real-time analytics and insights over large amounts of time-stamped data, making it an ideal solution for analyzing data from IoT devices.
The advantages of using TSI models for analyzing time-series data are numerous, including the ability to:
A. Add Computational Rules: TSI allows adding computational rules to the data stream. This enables users to define complex calculations on the data, such as moving averages, rolling aggregates, or other custom calculations.
B. Enrich the Time Series Data: TSI allows adding metadata to time-series data, which can be used to enhance the analysis of the data. The metadata can be used to add context to the data, such as the type of equipment or the location of the data source.
C. Organize Time Series Data into Hierarchies: TSI allows users to organize time-series data into hierarchies, which can be used to group related data together. This feature makes it easy to analyze and compare data across different assets, devices, or locations.
D. Apply ML models: TSI also provides an option to create and deploy Machine Learning models for predictive analysis.
Therefore, based on the advantages of TSI models listed above, the answer to the question is that all the options are the advantages of TSI models.