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: This disclosure relates generally to memory suspect detection, and more particularly to system and method for detection of memory suspects in an application runtime environment. The method includes systematically executing a plurality of transactions associated with an application. Executing the plurality of transactions results in generation of metrics. Said metrics includes application memory information and memory allocation information associated with the transactions. Said metrics are periodically captured, Based on the metrics that are periodically captured, a set of transactions are detected from amongst the plurality of transactions that are impacted due to suspected memory allocations.

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 a method and system for correlation analysis of performance metrics. In one embodiment, a computing system for correlation analysis of performance metrics is described. The computing system includes a processor, and a memory which is coupled to the processor. Further, the memory comprises a profiler agent, a correlation engine, and a graphics controller. The profiler agent collects the performance metrics based on execution of a target application. The correlation engine establishes correlation between the performance metrics based on a predefined parameter. The graphics controller generates a consolidated interface of the performance metrics depicting the established correlation between the performance metrics.

Abstract: This disclosure relates generally to anomaly detection, and more particularly to system and method for detecting anomalies. In one embodiment, the method includes executing at least one thread associated with the application. Executing the at least one thread results in invoking one or more methods associated with the at least one thread. During the execution metrics associated with the one or more methods are captured. The metrics are systematically arranged in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs. One or more anomalies associated with the one or more methods are identified from the data structure based on a detection of at least one predetermined condition in the data structure. An anomaly of the one or more anomalies includes one of un-exited anomaly, an exception anomaly and a user-defined anomaly.

Abstract: This disclosure relates generally to anomaly detection, and more particularly to system and method for detecting anomalies. In one embodiment, the method includes executing at least one thread associated with the application. Executing the at least one thread results in invoking one or more methods associated with the at least one thread. During the execution metrics associated with the one or more methods are captured. The metrics are systematically arranged in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs. One or more anomalies associated with the one or more methods are identified from the data structure based on a detection of at least one predetermined condition in the data structure. An anomaly of the one or more anomalies includes one of un-exited anomaly, an exception anomaly and a user-defined anomaly.

Abstract: This disclosure relates generally to memory suspect detection, and more particularly to system and method for detection of memory suspects in an application runtime environment. The method includes systematically executing a plurality of transactions associated with an application. Executing the plurality of transactions results in generation of metrics. Said metrics includes application memory information and memory allocation information associated with the transactions. Said metrics are periodically captured. Based on the metrics that are periodically captured, a set of transactions are detected from amongst the plurality of transactions that are impacted due to suspected memory allocations.

Abstract: A computer implemented method for selective profiling of an application is disclosed herein. The method includes selecting at least one filter type for selecting one or more categories of functional blocks in an application file to be profiled. Based on the selected filter type, a relational hierarchical structure of functional blocks of the application file in the selected category is determined. The determining can be further based on an archive file associated with the application file. Further, one or more functional blocks are selected from the relational hierarchical structure for profiling, the selected functional blocks being associated with a functionality of the application. The selected functional blocks are profiled to achieve selective profiling of the application file for the functionality.

Abstract: Systems and methods related to performance measurement and reporting are described. In one method, a first application is profiled in a first profiling session to generate a first parameter dataset related to performance of segments of the first application. A session report is created based on the first parameter dataset. Further, based in part on the session report, comments for one or more of the segments of the application are received. A consolidated report is then generated based on the first parameter dataset and the comments.

Abstract: A computer implemented method for selective profiling of an application is disclosed herein. The method includes selecting at least one filter type for selecting one or more categories of functional blocks in an application file to be profiled. Based on the selected filter type, a relational hierarchical structure of functional blocks of the application file in the selected category is determined. The determining can be further based on an archive file associated with the application file. Further, one or more functional blocks are selected from the relational hierarchical structure for profiling, the selected functional blocks being associated with a functionality of the application. The selected functional blocks are profiled to achieve selective profiling of the application file for the functionality.

Abstract: The present subject matter relates to a method and system for correlation analysis of performance metrics. In one embodiment, a computing system for correlation analysis of performance metrics is described. The computing system includes a processor, and a memory which is coupled to the processor. Further, the memory comprises a profiler agent, a correlation engine, and a graphics controller. The profiler agent collects the performance metrics based on execution of a target application. The correlation engine establishes correlation between the performance metrics based on a predefined parameter. The graphics controller generates a consolidated interface of the performance metrics depicting the established correlation between the performance metrics.

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.

Abstract: Systems and methods related to performance measurement and reporting are described. In one method, a first application is profiled in a first profiling session to generate a first parameter dataset related to performance of segments of the first application. A session report is created based on the first parameter dataset. Further, based in part on the session report, comments for one or more of the segments of the application are received. A consolidated report is then generated based on the first parameter dataset and the comments.