Amdocs Development Limited

Yaniv Avrashi

Gilli Shama

Pavel Vodolazov

Lior Turgeman

Nadeem Azaizah

Irit Shamir

Erez Gabay

Uri Weissler

Mihir Parikh

As described herein, a system, method, and computer program are provided for identifying foreign keys between distinct tables based on a statistical analysis of table values. In use, a plurality of candidate field combinations are determined between a first table and a second table. A statistical analysis of values stored in fields of each candidate field combination of the plurality of the candidate field combinations is performed. Foreign keys between the first table and the second table are identified, based on a result of the statistical analysis.

System, method, and computer program for identifying foreign keys between distinct tables

As described herein, a system, method, and computer program are provided for anomaly detection in time-series data with mixed seasonality. In use, time-series data of a mixed seasonality type is received. Additionally, the time-series data is segmented, by a defined unit of time, to form a plurality of time-series data segments. Further, the plurality of time-series data segments are processed to determine one or more patterns across the plurality of time-series data segments. Still yet, the one or more patterns are stored and used to perform pattern matching for an input time-series.

System, method, and computer program for anomaly detection in time-series data with mixed seasonality

A system, method, and computer program product are provided for automated resolution of free-text incidents, based on machine learning. In operation, a system receives incident information from at least one IT service management (ITSM) system. The system reads the incident information including free text and classifies an incident associated with the incident information to at least one automation flow. The system generates robotic process automation (RPA) compatible information from the incident information by parsing parameters associated with the incident from text into required fields based on the at least one automation flow. Further, the system sends the generated RPA compatible information to the at least one ITSM system or at least one RPA system.

System, method, and computer program for automated resolution of free-text incidents, based on machine learning

As described herein, a system, method, and computer program are provided for dynamic prioritization of monitoring system related alerts. A plurality of alerts generated for a monitoring system are accessed. A first set of alert features predefined as high-level features are identified, wherein each of the high-level features is mapped to one or more alert features in a second set of alert features predefined as low-level features. The plurality of alerts are processed to determine a plurality of the most central high-level features. The plurality of alerts are grouped according to the plurality of the most central high-level features. Each group of alerts is processed to determine a plurality of the most central low-level features for the alerts in the group of alerts. A prioritized set of alerts are selected from the plurality of alerts based on the plurality of the most central low-level features.

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Petach Tikva, Israel

Yaniv Avrashi