Abstract: A device may receive an alarm from a network and determine whether to process the alarm using a robotic process automation (RPA) engine or a machine learning automation (MLA) engine. Based on the determination, the device may selectively cause the alarm to be processed by the RPA engine or the MLA engine. The device may receive data associated with a network performance indicator and may provide the data to the MLA engine. The MLA engine may use a machine learning model to assign a score to the data and may determine whether to generate a trouble ticket based on the score.

Abstract: The present invention is directed towards systems and methods for directing a user request for content over a network to a given content server on the basis of one or more rules. The method of the present invention comprises receiving a request for content form a user, the request for content including a profile of the user identifying one or more characteristics associated with the user. One or more rules are retrieved for identifying a content server to which a request for content is to be delivered, the one or more rules including at least one of business rules, network rules, and user profile rules. The one or more retrieved rules are applied to the request for content to identify a content server to which the request for content is to be delivered and the request for content is delivered to the identified content server.

Abstract: A system (830) and method (800) for diagnosing a malfunctioning machine. A fault event is selected (806) together with sequential operating parameter data (808) from a selectively focused time interval about the fault event for evaluation of a machine (810). The selectively focused time interval may include data occurring just before, just after, or spanning the fault event. Characterizing information such as slope, rate of change, and absolute sign of the data may be derived (809) from the operating parameter data over the selectively focused time interval and used in the diagnosis. The fault event and operating parameter data may be compared to existing cases in a case database (834). A set of rules (817) or candidate anomalies (841) may be executed over the operating parameter data to further improve the accuracy of the diagnosis.

Abstract: Relational database applications such as index selection, histogram tuning, approximate query processing, and statistics selection have recognized the importance of leveraging workloads. Often these applications are presented with large workloads, i.e., a set of SQL DML statements, as input. A key factor affecting the scalability of such applications is the size of the workload. The invention concerns workload compression which helps improve the scalability of such applications. The exemplary embodiment is broadly applicable to a variety of workload-driven applications, while allowing for incorporation of application specific knowledge. The process is described in detail in the context of two workload-driven applications: index selection and approximate query processing.

Abstract: A method of processing data such as alarms from a communications network, by alarm correlation, the network comprising entities which offer and receive services to and from each other, the method comprising the step of: adapting a virtual model (87) of the network according to events in the network. The model comprises a plurality of managed units (91,92) corresponding to the network entities, each of said units containing information about the services offered and received by its corresponding entity to and from other entities, and having associated knowledge based reasoning capacity such as rules, for adapting the model by adapting said information. When one of the managed units is notified of an event such as an alarm raised by its corresponding entity, the cause of the alarm is determined using the virtual model. The development and maintenance of rules is easier, and correlation quicker since the rules for each unit need not relate to all the other units.

Abstract: The invention features a system and method to enable real-time establishment and maintenance of a standard of operation for a data communications network. The standard is a data set which includes network activity which is historically categorized by traffic type and by activity. The process begins with monitoring the network media or some network component over some period of time. The monitoring information is used to build benchmark data sets. The benchmark data sets contain a standard of operation for the network, which are historically categorized by either traffic type or activity. This standard of operation is constantly built by the intelligent monitoring facilities. After some period of time which is referred to as the benchmark data set refresh interval, the benchmark that was created is employed in a fashion to allow a determination as to whether the data that is taken from the current monitoring activity indicates normal network behavior.