Anomaly Detection — Another Challenge for Artificial Intelligence

It is true that the Industrial Internet associated with Things will change the world at some point. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions inside real-time. One way to process information faster and more efficiently is in order to detect abnormal activities, changes or shifts in datasets. Thus, anomaly detection, a technology that depends on Artificial Cleverness to identify irregular behavior within the particular pool of gathered data, has become one of the main objectives of the Industrial IoT.



Anomaly detection refers to the identification of items or events that do not conform to an expected pattern or to some other items in the dataset that are usually undetectable by a human expert. Such anomalies can usually be translated into problems such as structural defects, mistakes or frauds.



Examples of possible anomalies




The leaking connection tube that leads to the shutting down of the entire production line;
Multiple failed login attempts indicating the possibility of fishy cyber exercise;
Scams detection in financial transactions.





Why is it important?



Modern businesses are beginning in order to understand the importance associated with interconnected operations to get the full picture of their...


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It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the particular pool of collected data, has become one of the main objectives of the Industrial IoT.

Abnormality detection refers in order to the identification associated with items or occasions that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by the human expert. Such anomalies can generally be translated into problems such as structural defects, errors or frauds.

Examples of potential anomalies

A leaking connection pipe that leads to the shutting down of the entire production line;
Multiple failed login attempts indicating the possibility of fishy cyber activity;
Fraud detection in financial transactions.

Why is it important?

Modern businesses are beginning to understand the particular importance of interconnected operations to get the full picture of their…

Read More on Dataflow

Author

Scottie Todd

Scottie Todd

Digital Marketing Lead

“Level 4 marketing wizard on a quest for
data insights one blog post at a time.”

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