Abstract: Data analysis plays a crucial role to get significant information out of the data. A real time system and method for analyzing data streams have been provided. The system can utilize many different types of data formats such as numeric, text, video, audio, image, or combination thereof. The analysis takes place as per the requirement using an analytical engine and an intermediate output is generated. The intermediate output is further processed using a distributed real time business rule processing engine to determine required conditions in the data. The business rules comprise one or more set of meta data. On match of the business rule, the system triggers an alert or propagates the required information to integrating solution for required actions. The system and method are technology and communication protocol agnostic, and designed with highly efficient load balanced technique, thereby facilitating highly concurrent data processing with minimal latency.

Abstract: Data analysis plays a crucial role to get significant information out of the data. A real time system and method for analyzing data streams have been provided. The system can utilize many different types of data formats such as numeric, text, video, audio, image, or combination thereof. The analysis takes place as per the requirement using an analytical engine and an intermediate output is generated. The intermediate output is further processed using a distributed real time business rule processing engine to determine required conditions in the data. The business rules comprise one or more set of meta data. On match of the business rule, the system triggers an alert or propagates the required information to integrating solution for required actions. The system and method are technology and communication protocol agnostic, and designed with highly efficient load balanced technique, thereby facilitating highly concurrent data processing with minimal latency.

Abstract: In the monitoring domain, event management can deal with the monitoring of behavioural changes for any type of entity. Normally, the event management requires a lot of human intervention to generate a set of rules which results in adding effort and cost. A method and system for managing a plurality of events using automated rule generation has been provided. The system automatically generates a set of rules by studying the trend of incoming events over a period of time by computing moving percentile, thereby removing the need of human intervention to define rules for alert generation. The reported alerts are more specific and accurate, since the trend of incoming data is used to define the rules for alert generation. Further the method also provides the provision for the resolving the generated alerts either manually or automatically.

Abstract: The present subject matter relates to monitoring of an environment using data trend analysis. The method comprises obtaining at least one data trend pertaining to at least one data stream for a pre-defined period of time. The at least one data trend is indicative of a pattern followed by the at least one data stream. Further, at least one delta value pertaining to the at least one data stream may be computed, the at least one delta value being indicative of a deviation in the at least one data stream with respect to the at least one data trend at a specific time instance. Furthermore, at least one relationship between a plurality of data streams is identified based on the at least one data trend and identity metadata associated with each data stream. Based on the at least one delta value and the at least one relationship, at least one rule is generated.

Abstract: In the monitoring domain, event management can deal with the monitoring of behavioural changes for any type of entity. Normally, the event management requires a lot of human intervention to generate a set of rules which results in adding effort and cost. A method and system for managing a plurality of events using automated rule generation has been provided. The system automatically generates a set of rules by studying the trend of incoming events over a period of time by computing moving percentile, thereby removing the need of human intervention to define rules for alert generation. The reported alerts are more specific and accurate, since the trend of incoming data is used to define the rules for alert generation. Further the method also provides the provision for the resolving the generated alerts either manually or automatically.

Abstract: The present subject matter relates to monitoring of an environment using data trend analysis. The method comprises obtaining at least one data trend pertaining to at least one data stream for a pre-defined period of time. The at least one data trend is indicative of a pattern followed by the at least one data stream. Further, at least one delta value pertaining to the at least one data stream may be computed, the at least one delta value being indicative of a deviation in the at least one data stream with respect to the at least one data trend at a specific time instance. Furthermore, at least one relationship between a plurality of data streams is identified based on the at least one data trend and identity metadata associated with each data stream. Based on the at least one delta value and the at least one relationship, at least one rule is generated.

Abstract: A method and system for monitoring performance of network transactions is described herein. According to the present invention, in a testing environment, transaction flow signatures representing a hierarchical flow of sub-transactions constituting the said network transactions are generated and stored for monitoring and analysis of the network transactions. In a real-time environment, the sub-transactions are identified through the stored transaction flow signatures for a particular network transaction and the identified sub-transactions are then monitored by metrics analysis engine to determine the performance of the network.

Abstract: A method and system for monitoring performance of network transactions is described herein. According to the present invention, in a testing environment, transaction flow signatures representing a hierarchical flow of sub-transactions constituting the said network transactions are generated and stored for monitoring and analysis of the network transactions. In a real-time environment, the sub-transactions are identified through the stored transaction flow signatures for a particular network transaction and the identified sub-transactions are then monitored by metrics analysis engine to determine the performance of the network.