View source for 20240056484. METHOD FOR IMPUTATION OF CATEGORICAL DATA INTO MULTIPLE TIME SERIES OF CYBERSECURITY EVENTS simplified abstract (Siemens Aktiengesellschaft)

=METHOD FOR IMPUTATION OF CATEGORICAL DATA INTO MULTIPLE TIME SERIES OF CYBERSECURITY EVENTS=


==Organization Name==

[[:Category:Siemens Aktiengesellschaft|Siemens Aktiengesellschaft]]


[[Category:Siemens Aktiengesellschaft]]

==Inventor(s)==

[[:Category:Leandro Pfleger De Aguiar of Leander TX (US)|Leandro Pfleger De Aguiar of Leander TX (US)]][[Category:Leandro Pfleger De Aguiar of Leander TX (US)]]

[[:Category:Henning Janssen of Karlsruhe (DE)|Henning Janssen of Karlsruhe (DE)]][[Category:Henning Janssen of Karlsruhe (DE)]]

[[:Category:Daniel Sadoc Menasche of Rio de Janeiro (BR)|Daniel Sadoc Menasche of Rio de Janeiro (BR)]][[Category:Daniel Sadoc Menasche of Rio de Janeiro (BR)]]

[[:Category:Lucas Miranda of Taquara (BR)|Lucas Miranda of Taquara (BR)]][[Category:Lucas Miranda of Taquara (BR)]]

[[:Category:Mateus Nogueira of Flamengo (BR)|Mateus Nogueira of Flamengo (BR)]][[Category:Mateus Nogueira of Flamengo (BR)]]

[[:Category:Daniel Vieira of Campo Grande (BR)|Daniel Vieira of Campo Grande (BR)]][[Category:Daniel Vieira of Campo Grande (BR)]]

[[:Category:Miguel Angelo Santos Bicudo of Aldeia da Prata (BR)|Miguel Angelo Santos Bicudo of Aldeia da Prata (BR)]][[Category:Miguel Angelo Santos Bicudo of Aldeia da Prata (BR)]]

[[:Category:Anton Kocheturov of Langhorne PA (US)|Anton Kocheturov of Langhorne PA (US)]][[Category:Anton Kocheturov of Langhorne PA (US)]]

==METHOD FOR IMPUTATION OF CATEGORICAL DATA INTO MULTIPLE TIME SERIES OF CYBERSECURITY EVENTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240056484 titled 'METHOD FOR IMPUTATION OF CATEGORICAL DATA INTO MULTIPLE TIME SERIES OF CYBERSECURITY EVENTS

==Simplified Explanation==

The method described in the patent application involves imputing data to a time series of events by collecting data, storing it in a database, defining rules based on observed patterns, and generating new data based on these rules through iterative processes.

* Data relating to multiple events is collected and stored in a database.
* Rules are defined based on patterns observed in the collected data.
* New data is generated for events based on the set of rules.
* Iterative processes involve defining additional rules and generating new data based on the rules established in previous iterations.
* The process may stop when no new rules or data is established in a previous iteration.
* The new data can be sequential temporal information of the event or a tag related to the event class.
* Rule mining is used to generate new data, and additional rules are defined based on the new data.

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### Potential Applications
- Time series analysis in various industries such as finance, healthcare, and manufacturing.
- Predictive maintenance in equipment monitoring.
- Fraud detection in financial transactions.
- Anomaly detection in network security.

### Problems Solved
- Imputing missing data in a time series of events.
- Automating the process of defining rules and generating new data based on patterns.
- Improving the accuracy and efficiency of data imputation in time series analysis.

### Benefits
- Enhanced decision-making based on more complete and accurate data.
- Increased efficiency in data analysis processes.
- Automation of rule generation and data imputation tasks.
- Improved predictive capabilities in various applications.


==Original Abstract Submitted==

a method for imputing data to a time series of events include collecting data relating to a plurality of events, storing the collected data in a database, defining a set of rules based on patterns observed, defining a new data relating to one of the plurality of events based on the set of rules. defining additional rules and new data is iteratively performed based on new data and rules established in a prior iteration. the iterations may be stopped when no new rules or data is established in a previous iteration. the new data may be sequential temporal information of the event in the time series or may be a tag relating to the class of the event. the new data may be generated using rule mining. the new data is propagated to the rule mining and additional rules are defined based on the new data.



[[Category:H04L9/40]]