Abstract: A method for determining subject matter experts includes monitoring, by a computer, an activity performed by a user during a period of time, the activity including a sequence of operations, based on the sequence of operations, determining a topic of the activity performed by the user, recognizing, by the computer, a hesitation pattern of the user associated with the topic during the period of time, based on the recognized hesitation pattern, calculating a confidence indicator associated with the activity, the confidence indicator specifies a confidence of the user in performing the activity, based on the confidence indicator being lower than a confidence threshold, identifying one or more subject matter experts for the determined topic, and prompting the user to submit a support request to at least one of the identified subject matter experts.

Abstract: A method, computer program product, and system includes a processor(s) initiating a web conferencing session between a host and a client, by the client receiving a presentation and transmitting the presentation to a semantic engine, wherein the semantic engine performs an automatic machine learning session to generate a presentation concepts list comprising concepts relevant to the presentation, and progressively displaying the presentation in a thin client application on the client. The processor(s) monitors on the client, during the web conferencing session, activities executed on the client and extracts web concepts related to the activities executed on the client. The processor(s) determines an interest level of a user of the client in the presentation, based on determining a presence or absence of a relationship of each web concept to one or more of the concepts relevant to the presentation and displays the interest level in a graphical user interface.

Abstract: A method to replace profile related data is suggested. A history database including recorded profile related data may be provided. Upon receiving actual profile related data, a processor may retrieve, from the history database, at least one recorded profile related data, matching to the actual profile related data. Then, a command may be received, from a user, to replace at least one part of the actual profile related data.

Abstract: Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local anomalies are determined in the clustered KPIs by comparing, for each individual KPI in clusters that conform to a Gaussian distribution, a distance from a respective cluster mean to a threshold. Structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A management action is performed based on the classified structural and local anomalies.

Abstract: Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local and structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A transformation is performed from a space based on the principal axes to an original space. It is determined whether each of the local and structural anomalies is a global or a local anomaly. A management action is performed based on the classified structural and local anomalies.

Abstract: A method for determining subject matter experts includes monitoring, by a computer, an activity performed by a user during a period of time, the activity including a sequence of operations, based on the sequence of operations, determining a topic of the activity performed by the user, recognizing, by the computer, a hesitation pattern of the user associated with the topic during the period of time, based on the recognized hesitation pattern, calculating a confidence indicator associated with the activity, the confidence indicator specifies a confidence of the user in performing the activity, based on the confidence indicator being lower than a confidence threshold, identifying one or more subject matter experts for the determined topic, and prompting the user to submit a support request to at least one of the identified subject matter experts.

Abstract: A method, associated system, and computer program product are disclosed for facilitating improved objectivity of feedback for evaluating software programs. The feedback is provided by a user. The method comprises, upon determining an authorization to monitor usage of a predefined program by the user, retrieving a predefined evaluation profile specifying one or more parameters for assessing usage of the program. The method further comprises accessing usage data associated with the user and the program, and calculating, based on the usage data and relative to the one or more parameters of the evaluation profile, a program usage score associated with the user and the program. The method further comprises transmitting the program usage level from the computing device to an external feedback evaluation system. The external feedback evaluation system is configured to associate the program usage level with the feedback provided by the user and related to the program.

Abstract: A method, computer program product, and system includes a processor(s) initiating a web conferencing session between a host and a client, by the client receiving a presentation and transmitting the presentation to a semantic engine, wherein the semantic engine performs an automatic machine learning session to generate a presentation concepts list comprising concepts relevant to the presentation, and progressively displaying the presentation in a thin client application on the client. The processor(s) monitors on the client, during the web conferencing session, activities executed on the client and extracts web concepts related to the activities executed on the client. The processor(s) determines an interest level of a user of the client in the presentation, based on determining a presence or absence of a relationship of each web concept to one or more of the concepts relevant to the presentation and displays the interest level in a graphical user interface.

Abstract: A method to replace profile related data is suggested. A history database including recorded profile related data may be provided. Upon receiving actual profile related data, a processor may retrieve, from the history database, at least one recorded profile related data, matching to the actual profile related data. Then, a command may be received, from a user, to replace at least one part of the actual profile related data.

Abstract: A method for a mapping of user interfaces includes providing a mapping description between first data elements of a first user interface and second data elements of a second user interface, receiving input events via the first user interface, and intercepting a communication between the first user interface and an application by an interface mapper. The method also includes aggregating a series of the received input events to a first user interface pattern, transforming the first user interface pattern to a second user interface pattern based on the mapping description, and rendering the second user interface pattern in a second user interface in parallel to rendering the first user interface pattern in the first user such that both user interfaces are visible at the same time.

Abstract: Enhancement of a first mind map to become a second enhanced mind includes: providing a content pool with information items; performing a first semantic scan based on a selected object; and generating a new object and a related connection to the selected objectbased on the semantic scan. Furthermore, the enhancement includes determining a strength value for each of the connections of the first mind map and determining the connection withthe lowest strength value and recalculating the strength value using an external knowledge base to define the connection as obsolete if the strength value decreases.

Abstract: A method, computer program product, and system includes a processor(s) initiating a web conferencing session between a host and a client, by the client receiving a presentation and transmitting the presentation to a semantic engine, wherein the semantic engine performs an automatic machine learning session to generate a presentation concepts list comprising concepts relevant to the presentation, and progressively displaying the presentation in a thin client application on the client. The processor(s) monitors on the client, during the web conferencing session, activities executed on the client and extracts web concepts related to the activities executed on the client. The processor(s) determines an interest level of a user of the client in the presentation, based on determining a presence or absence of a relationship of each web concept to one or more of the concepts relevant to the presentation and displays the interest level in a graphical user interface.

Abstract: Embodiments of the present invention provide systems, methods, and program products for automatic ranking of a technical software defect of a software service. Embodiments of the present invention can be used to collect technical environment parameters of a software service responsive to receiving a message associated with a technical software defect of the software service and determine a severity ranking value of the technical software defect based on a combined rating of the collected technical environment parameters wherein the technical environment parameters comprise at least a usage rate of the software service; a percentage of a number of users of the software-as-a-service environment authorized for using the software program; a log pattern of a signature of the technical software defect; and a percentage of a total functionality number of the software program, wherein the percentage defines a defect impact value.

Abstract: Enhancement of a first mind map to become a second enhanced mind includes: providing a content pool with information items; performing a first semantic scan based on a selected object; and generating a new object and a related connection to the selected object based on the semantic scan. Furthermore, the enhancement includes determining a strength value for each of the connections of the first mind map and determining the connection with the lowest strength value and recalculating the strength value using an external knowledge base to define the connection as obsolete if the strength value decreases.

Abstract: Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local and structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A transformation is performed from a space based on the principal axes to an original space. It is determined whether each of the local and structural anomalies is a global or a local anomaly. A management action is performed based on the classified structural and local anomalies.

Abstract: Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local anomalies are determined in the clustered KPIs by comparing, for each individual KPI in clusters that conform to a Gaussian distribution, a distance from a respective cluster mean to a threshold. Structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A management action is performed based on the classified structural and local anomalies.

Abstract: Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local and structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A management action is performed based on the classified structural and local anomalies.

Abstract: A method, computer program product, and system includes a processor(s) initiating a web conferencing session between a host and a client, by the client receiving a presentation and transmitting the presentation to a semantic engine, wherein the semantic engine performs an automatic machine learning session to generate a presentation concepts list comprising concepts relevant to the presentation, and progressively displaying the presentation in a thin client application on the client. The processor(s) monitors on the client, during the web conferencing session, activities executed on the client and extracts web concepts related to the activities executed on the client. The processor(s) determines an interest level of a user of the client in the presentation, based on determining a presence or absence of a relationship of each web concept to one or more of the concepts relevant to the presentation and displays the interest level in a graphical user interface.

Abstract: A method, associated system, and computer program product are disclosed for facilitating improved objectivity of feedback for evaluating software programs. The feedback is provided by a user. The method comprises, upon determining an authorization to monitor usage of a predefined program by the user, retrieving a predefined evaluation profile specifying one or more parameters for assessing usage of the program. The method further comprises accessing usage data associated with the user and the program, and calculating, based on the usage data and relative to the one or more parameters of the evaluation profile, a program usage score associated with the user and the program. The method further comprises transmitting the program usage level from the computing device to an external feedback evaluation system. The external feedback evaluation system is configured to associate the program usage level with the feedback provided by the user and related to the program.

Abstract: Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local and structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A management action is performed based on the classified structural and local anomalies.