Abstract: A computer-implemented method for identifying a use anomaly potentially exposing sensitive data is disclosed. The method comprises receiving data comprising logs of a communication involving a computing device, where the logs comprise distinct logs of at least three communication abstraction levels. At least three anomaly classifiers are operated for logs from each of the at least three communication abstraction levels. An ensemble model is used to identify an anomaly in the communication, by processing output from each of the at least three anomaly classifiers. The various logs from a moment in time when the anomaly occurred are collated, and a graphical user interface is generated for reviewing the identified anomaly and collated logs. A human reviewer is then alerted that an anomaly has been identified.

Abstract: Systems and methods are provided for managing the order of data written to a transaction log in a distributed storage system. In a system with multiple nodes, if sequencers are naively generated without taking into consideration inconsistencies among the different nodes generating the sequencers, then the sequencers may not increase for each data transaction. To alleviate this problem, the node committing the transaction to the transaction log may perform a consistent read and verifies that the sequencer advances. If the sequencer does not advance, the node can perform a context-dependent operation such as adjusting the sequencer, acknowledging the write without committing it to the transaction log, or rejecting the write altogether.

Abstract: A computer-implemented method for identifying a use anomaly potentially exposing sensitive data is disclosed. The method comprises receiving data comprising logs of a communication involving a computing device, where the logs comprise distinct logs of at least three communication abstraction levels. At least three anomaly classifiers are operated for logs from each of the at least three communication abstraction levels. An ensemble model is used to identify an anomaly in the communication, by processing output from each of the at least three anomaly classifiers. The various logs from a moment in time when the anomaly occurred are collated, and a graphical user interface is generated for reviewing the identified anomaly and collated logs. A human reviewer is then alerted that an anomaly has been identified.

Abstract: Systems and methods for predicting and preventing system incidents such as outages or failures based on advanced log analytics are described. A processing center comprising an incident prediction server and log database may receive application server logs generated by an application server and historical incident data generated by an incident database server. The processing center may be configured to cluster a subset of application server logs and based on the subset of application server logs and the incident data, determine in real time or near real time the likelihood of occurrence of an incident such as a system outage or failure.

Abstract: Systems and methods for predicting and preventing system incidents such as outages or failures based on advanced log analytics are described. A processing center comprising an incident prediction server and log database may receive application server logs generated by an application server and historical incident data generated by an incident database server. The processing center may be configured to cluster a subset of application server logs and based on the subset of application server logs and the incident data, determine in real time or near real time the likelihood of occurrence of an incident such as a system outage or failure.