Abstract: Systems and methods for identifying recommended topics are disclosed. An example method may be performed by one or more processors of a system and include identifying one or more attributes of a system user, identifying a subset of topics relevant to the system user based on analyzing the one or more attributes of the system user using an analysis model trained with a machine learning process to identify relevant topics for system users based on historical user attributes, generating, for each respective topic of the subset of topics, using the trained analysis model, a relevance score for the respective topic based at least in part on a most recent system page previously accessed by the system user and a current system page accessed by the system user, and generating one or more recommended topics for the system user based on the relevance scores.

Abstract: Systems and methods for identifying recommended topics are disclosed. An example method may be performed by one or more processors of a system and include identifying one or more attributes of a system user, identifying a subset of topics relevant to the system user based on analyzing the one or more attributes of the system user using an analysis model trained with a machine learning process to identify relevant topics for system users based on historical user attributes, generating, for each respective topic of the subset of topics, using the trained analysis model, a relevance score for the respective topic based at least in part on a most recent system page previously accessed by the system user and a current system page accessed by the system user, and generating one or more recommended topics for the system user based on the relevance scores.

Abstract: A method and system provides personalized self-help assistance to users of a data management system. The method and system trains an analysis model to identify relevant assistance topics based personal attributes of the user and contextual attributes associated with the user. The method and system outputs personalized assistance topics recommendation data to the user based on the analysis of the analysis model.

Abstract: An aspect of the present disclosure facilitates users of touch screens to conveniently provide values for fields of a form. In an embodiment, a mapping is maintained to map symbols to corresponding profiles, with each profile being defined to specify a respective value for each of a corresponding set of fields. A form is sent for display on a touch screen, and a user is permitted to perform a touch action constituting a sequence of touch points (and representing input symbol) on the touch screen. The mapping is examined to determine a first symbol matching the input symbol, and a first profile corresponding to the first symbol is identified. The form is then auto-filled with values specified by the first profile. The user can continue interaction with the form with such auto-filled values.

Abstract: An aspect of the present invention provides for capturing of audit trail data related to processing of requests. In an embodiment, the received requests are classified into a first category and a second category. For each request in the first category, the corresponding audit trail data is stored directly into non-volatile storage upon processing of the request. On the other hand, for each request in the second category, the audit trail data is first stored into a volatile memory upon processing of the request, and then later copied from the volatile memory to non-volatile storage. Thus, the audit trail data corresponding to both categories of requests is eventually available stored on non-volatile storage.

Abstract: A mechanism for efficient collection of data is described for runtime middleware environments. Two frequencies are used, a collection frequency (CF) to collect the data and an aggregation frequency (AF) to aggregate and persist the data in a repository. The collection cycle is a shorter time interval than the aggregation cycle. An agent residing in the container periodically collects a set of data upon every collection cycle from the components of the middleware system and caches the set of data locally. Upon every aggregation cycle, the agent applies an aggregation function to the collected set of data and persists the set of data into a repository after the aggregation function has been applied. The aggregation function is such that it resulting data represents the behavior of the runtime environment in the total duration of the aggregation cycle.

Abstract: A system and method for detecting an outlier in a usage pattern comprises a computer accessible to perform an operation. The system includes an audit forensics engine having an outlier detection module. When an instance occurs where the operation is performed, audit trail data is captured related to the operation. The outlier detection module determines for the instance where the operation is performed whether the instance is an outlier in a usage pattern based on a comparison of the audit trail data to the usage pattern.

Abstract: An aspect of the present disclosure facilitates users of touch screens to conveniently provide values for fields of a form. In an embodiment, a mapping is maintained to map symbols to corresponding profiles, with each profile being defined to specify a respective value for each of a corresponding set of fields. A form is sent for display on a touch screen, and a user is permitted to perform a touch action constituting a sequence of touch points (and representing input symbol) on the touch screen. The mapping is examined to determine a first symbol matching the input symbol, and a first profile corresponding to the first symbol is identified. The form is then auto-filled with values specified by the first profile. The user can continue interaction with the form with such auto-filled values.

Abstract: Feature and deployment recommendation systems and methods for content management systems comprises a vendor-site deployment and one or more customer-site deployments. The vendor-site deployment is configured to recommend an adaptation of any of the one or more customer-site deployments. The vendor-site deployment including a customer configuration repository adapted to store content usage metrics received from one or more customer-site deployments, an analytical engine configured to generate content usage patterns based on the stored content usage metrics, and a recommendation engine configured to recommend the adaptation.

Abstract: A method of determining a root cause of a performance problem is provided. The method comprises analyzing a plurality of performance indicators/metrics in a first time period and determining that at least one performance indicators/metric is exhibiting abnormal behavior. The method further comprises analyzing the plurality of performance indicators/metrics over a second time period, the second time period is longer than the first time period, and determining trend information for each performance indicators/metric over the second time period. The method further comprises correlating the trend information for each performance indicators/metric with performance problem information stored in a knowledge base, identifying a potential cause of the abnormal behavior based on the correlation, and alerting a user of the potential cause.

Abstract: In specific embodiments, a content management system operable on one or more microprocessors comprises a content repository storing a plurality of content items and a content usage analyzer. The computer is usable to perform one or more operations on one or more contents items stored in the content repository, and when an operation is performed on a content item, audit data is captured related to the operation. The content usage analyzer is configured to identify an idle content item within the content repository based on the captured audit data.

Abstract: Systems and methods are described for diagnosing behavior of software components in an application server. The application server can comprise a plurality of components that process incoming requests. A diagnostics advisor can be deployed with the application server and can determine an efficiency and/or inefficiency of each of the components of the application server or other middleware system. The efficiency determined by computing a ratio of the number of requests that completed execution during a particular sampling time period to the number of requests that were received. The inefficiency is the ratio of the number of requests that are still being executed at the end of the sampling time period to the number of requests that were received. The diagnostics advisor employs the determined efficiency and/or inefficiency to diagnose a misbehavior or other problem of the components in the application server.

Abstract: Feature and deployment recommendation systems and methods for content management systems comprises a vendor-site deployment and one or more customer-site deployments. The vendor-site deployment is configured to recommend an adaptation of any of the one or more customer-site deployments. The vendor-site deployment including a customer configuration repository adapted to store content usage metrics received from one or more customer-site deployments, an analytical engine configured to generate content usage patterns based on the stored content usage metrics, and a recommendation engine configured to recommend the adaptation.

Abstract: A method of isolating relevant components associated with a performance problem. The method comprises creating a static model of a system under test, including determining relationships between components in a middleware system. The method also comprises creating a dynamic problem tree by applying the static model to the system at runtime. The method further comprises identifying a link probability for each relationship in the dynamic problem tree; and traversing the dynamic problem tree to detect redundant components. A component is redundant if it has a link probability with its downstream component greater than a pre-determined threshold value. The method also comprises removing redundant components from the dynamic problem tree; and returning a set of components which are potential components to be analyzed further to determine the root causes of the performance problem.

Abstract: A system and method for detecting an outlier in a usage pattern comprises a computer accessible to perform an operation. The system includes an audit forensics engine having an outlier detection module. When an instance occurs where the operation is performed, audit trail data is captured related to the operation. The outlier detection module determines for the instance where the operation is performed whether the instance is an outlier in a usage pattern based on a comparison of the audit trail data to the usage pattern.

Abstract: In specific embodiments, a content management system operable on one or more microprocessors comprises a content repository storing a plurality of content items and a content usage analyzer. The computer is usable to perform one or more operations on one or more contents items stored in the content repository, and when an operation is performed on a content item, audit data is captured related to the operation. The content usage analyzer is configured to identify an idle content item within the content repository based on the captured audit data.

Abstract: A system and method for providing a static model map of behaviors in a middleware system, for use in identifying problems. In accordance with an embodiment, the system provides a view of the system which allows for analysis of the various components. Locations within the system where a request spends its time are categorized into either “wait” or “execution” locations. The components exhibit static behaviors which affect the response time and can be predefined for different types of components and analyzed independently. In turn the behaviors can be classified as either “wait” or “execution”, each of which can be considered aspects of a component. Each aspect of a component must satisfy the same efficiency/inefficiency as defined by the requirements on the system. An aspect that does not meet the criteria is considered a “problem aspect” and is analyzed further to find the cause of the problem.

Abstract: An aspect of the present invention provides for capturing of audit trail data related to processing of requests. In an embodiment, the received requests are classified into a first category and a second category. For each request in the first category, the corresponding audit trail data is stored directly into non-volatile storage upon processing of the request. On the other hand, for each request in the second category, the audit trail data is first stored into a volatile memory upon processing of the request, and then later copied from the volatile memory to non-volatile storage. Thus, the audit trail data corresponding to both categories of requests is eventually available stored on non-volatile storage.

Abstract: A method of isolating relevant components associated with a performance problem. The method comprises creating a static model of a system under test, including determining relationships between components in a middleware system. The method also comprises creating a dynamic problem tree by applying the static model to the system at runtime. The method further comprises identifying a link probability for each relationship in the dynamic problem tree; and traversing the dynamic problem tree to detect redundant components. A component is redundant if it has a link probability with its downstream component greater than a pre-determined threshold value. The method also comprises removing redundant components from the dynamic problem tree; and returning a set of components which are potential components to be analyzed further to determine the root causes of the performance problem.

Abstract: A system and method for providing a static model map of behaviors in a middleware system, for use in identifying system problems. In accordance with an embodiment, the system provides a view of the system, such as a middleware system, which allows for analysis of the various components involved, such that analysis of one component does not affect the analysis of a second component. Locations within the middleware system where a particular request spends its time before returning a response are broadly categorized into either “wait” or “execution” locations. Irrespective of which request calls a component, the components exhibit the same behaviors which affect the response time of the request serviced. These exhibited behaviors are static and can be predefined for different types of components and analyzed independently of each other. In turn the behaviors can be similarly classified as either “wait” or “execution”, each of which can be considered aspects of a component.