Method for automatically analyzing transaction logs of a distributed computing system

A method (100) for automatically analyzing a transaction log comprising at least one transaction log (200) of a distributed computing system, comprising a plurality of lines (201), the method comprising:

• for each line (201) of the transaction log (200)
• cutting the line (201) into words and associating the line with a size that is defined as a number of words (101);
• constructing a comparison vector by comparing the line (201) with other lines (201) of the transaction log (200) associated with a same size as said line (201, 103);
• constructing a pattern from at least one word of the line (201) from the comparison vector (104);
• creating an event (401) per pattern (105);
• constructing at least one prediction model by training in an unsupervised manner an artificial neural network (300) on a group (400_1) of training events (401) comprising at least one event (401), corresponding to a set (202_1) of training lines (201) comprising a plurality of lines (201) of the transaction log (200), the at least one prediction model being configured to predict a next event (401) that will take place in the transaction log (200, 107);
• for at least one event (401)
• using the at least one prediction model to predict the event (401) as the next event (401) that will take place in the transaction log (200), from a group (400_2) of prediction events comprising at least one event (401) having taken place before the event (401) in the transaction log (200, 108);
• generating from the at least one prediction model, a causal graph (500) of the event (401) comprising a causal relation for each event (401) of the group (400_2) of prediction events (401) responding to a relevance condition (109).

A computer program product comprising instructions that when executed on a computer lead the computer to implement a method for automatically analyzing a transaction log comprising at least one transaction log (200) of a distributed computing system, comprising a plurality of lines (201), the method comprising:

• for each line (201) of the transaction log (200)
• cutting the line (201) into words and associating the line with a size that is defined as a number of words (101);
• constructing a comparison vector by comparing the line (201) with other lines (201) of the transaction log (200) associated with a same size as said line (201, 103);
• constructing a pattern from at least one word of the line (201) from the comparison vector (104);
• creating an event (401) per pattern (105);
• constructing at least one prediction model by training in an unsupervised manner an artificial neural network (300) on a group (400_1) of training events (401) comprising at least one event (401), corresponding to a set (202_1) of training lines (201) comprising a plurality of lines (201) of the transaction log (200), the at least one prediction model being configured to predict a next event (401) that will take place in the transaction log (200, 107);
• for at least one event (401)
• using the at least one prediction model to predict the event (401) as the next event (401) that will take place in the transaction log (200), from a group (400_2) of prediction events comprising at least one event (401) having taken place before the event (401) in the transaction log (200, 108);
• generating from the at least one prediction model, a causal graph (500) of the event (401) comprising a causal relation for each event (401) of the group (400_2) of prediction events (401) responding to a relevance condition (109).