Abstract: A mechanism is provided updating a knowledge base of a sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term. Messages comprising natural language are received. Using content of the knowledge base, a decision is made as to whether at least one message of the received messages has a positive sentiment or a negative sentiment. A term is extracted from the message that is not present in the knowledge base. Based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, a score value of the term is calculated, and the term and the calculated score value are stored into the knowledge base.

Abstract: A solution is proposed for controlling invocations of a target component by multiple source components in a software application. A corresponding method comprises associating a plurality of source components in a software application with one or more corresponding source rates for invoking a target component in the software application; monitoring corresponding invocations of the target component by a number of instances of the plurality of source components; receiving an enablement request for a new invocation of the target component from a current instance of a current source component; verifying an enablement of the new invocation; estimating a serving probability indicative of a residual capability of the target component to serve the new invocation; and enabling the new invocation according to the serving probability.

Abstract: A solution is proposed for ranking a plurality of documents in a computing system, each of the documents comprising a plurality of document terms. A corresponding method comprises receiving a query comprising one or more query terms for selecting the documents, calculating corresponding similarity indexes between the query and the documents, each of the similarity indexes being calculated according to a comparison between the query terms and the document terms of the corresponding document, determining corresponding semantic richness indicators of the documents, adjusting the similarity indexes according to the corresponding semantic richness indicators, and ranking at least part of the documents for selection thereof according to the corresponding similarity indexes being adjusted.

Abstract: A solution is proposed for managing a plurality of computing machines. A corresponding method comprises causing each computing machine of at least part of the computing machines to execute a management activity on the computing machine; receiving a corresponding result of the execution of the management activity on each computing machine; determining a success fingerprint according to one or more characteristics of each of at least part of one or more wherein the corresponding result is indicative of a success of the execution of the management activity thereon; calculating a similarity index for each of one or more failure ones of the computing machines wherein the corresponding result is indicative of a failure of the execution of the management activity thereon; and prioritizing the computing machines which failed to accept a policy according to the corresponding similarity indexes.

Abstract: The invention relates to a computer-implemented method for predicting a work progress figure for a user using an assignment test. Receiving an input activity record; receiving an input sequence of activity records; generating a test sequence comprising the input activity record appended to the input sequence; providing the test sequence to an input of a machine learning model; in response to the provision of the test sequence, receiving a metric from an output of the machine learning model, the metric comprising a work progress figure assigned to the test sequence; and providing the metric, the method further comprising: receiving an unassigned activity record specific to the user; receiving a task specific to the user, the task comprising a sequence of assigned activity records; and executing the assignment test with the input activity record being the unassigned activity record and the input sequence being the sequence.

Abstract: A computer-implemented method for training a natural language classifier associated with a chat interface of a computer system is provided. The method may include receiving a training dataset comprising an initial set of expressions corresponding to an intent. Additional expressions corresponding to the intent may be generated, wherein the additional expressions are generated based on the initial set of expressions corresponding to the intent. The natural language classifier may be trained based on the initial set of expressions and the additional expressions corresponding to the intent. The trained natural language classifier may be implemented to determine an intent expressed by a detected query based on the initial set of expressions and the additional expressions by which the natural language classifier was trained.

Abstract: A mechanism is provided updating a knowledge base of a sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term. Messages comprising natural language are received. Using content of the knowledge base, a decision is made as to whether at least one message of the received messages has a positive sentiment or a negative sentiment. A term is extracted from the message that is not present in the knowledge base. Based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, a score value of the term is calculated, and the term and the calculated score value are stored into the knowledge base.

Abstract: A mechanism is provided for updating a knowledge base of a spam detection system for email messages. A first and a second email set of email messages related to a time window are created. The first email set comprises email messages currently classified as spam messages, the second email set email messages currently classified as non-spam messages. A natural language term that is not present in the knowledge base is extracted from one of the email messages contained in one of the two email sets. Based on a frequency of occurrence of the extracted term in email messages contained in one or more of the two email sets and based on whether the respective email messages are spam messages or non-spam messages, a score value of the extracted term is calculated. The extracted term and the calculated score value are stored into the knowledge base.

Abstract: A mechanism is provided updating a knowledge base of a sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term. Messages comprising natural language are received. Using content of the knowledge base, a decision is made as to whether at least one message of the received messages has a positive sentiment or a negative sentiment. A term is extracted from the message that is not present in the knowledge base. Based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, a score value of the term is calculated, and the term and the calculated score value are stored into the knowledge base.

Abstract: A computer-implemented method for training a natural language classifier associated with a chat interface of a computer system is provided. The method may include receiving a training dataset comprising an initial set of expressions corresponding to an intent. Additional expressions corresponding to the intent may be generated, wherein the additional expressions are generated based on the initial set of expressions corresponding to the intent. The natural language classifier may be trained based on the initial set of expressions and the additional expressions corresponding to the intent. The trained natural language classifier may be implemented to determine an intent expressed by a detected query based on the initial set of expressions and the additional expressions by which the natural language classifier was trained.

Abstract: A mechanism is provided for managing a plurality of computing machines. An indication of a management activity having an applicability rule is deployed to a plurality of computing machines to cause each of the plurality of computing machines having corresponding values of one or more characteristics fulfilling the applicability rule to apply the management activity. Results are received indicating that the management activity has been applied successfully on corresponding success computing machines and that the management activity has not been applied on corresponding excluded computing machines. One or more candidate computing machines of the excluded computing machines is selected according to a comparison of the corresponding values of the further characteristics of the excluded computing machines with a success fingerprint.

Abstract: A solution is proposed for ranking a plurality of documents in a computing system, each of the documents comprising a plurality of document terms. A corresponding method comprises receiving a query comprising one or more query terms for selecting the documents, calculating corresponding similarity indexes between the query and the documents, each of the similarity indexes being calculated according to a comparison between the query terms and the document terms of the corresponding document, determining corresponding semantic richness indicators of the documents, adjusting the similarity indexes according to the corresponding semantic richness indicators, and ranking at least part of the documents for selection thereof according to the corresponding similarity indexes being adjusted.

Abstract: A mechanism is provided for updating a knowledge base of a spam detection system for email messages. A first and a second email set of email messages related to a time window are created. The first email set comprises email messages currently classified as spam messages, the second email set email messages currently classified as non-spam messages. A natural language term that is not present in the knowledge base is extracted from one of the email messages contained in one of the two email sets. Based on a frequency of occurrence of the extracted term in email messages contained in one or more of the two email sets and based on whether the respective email messages are spam messages or non-spam messages, a score value of the extracted term is calculated. The extracted term and the calculated score value are stored into the knowledge base.

Abstract: A solution is proposed for managing a plurality of computing machines. A corresponding method comprises causing each computing machine of at least part of the computing machines to execute a management activity on the computing machine; receiving a corresponding result of the execution of the management activity on each computing machine; determining a success fingerprint according to one or more characteristics of each of at least part of one or more wherein the corresponding result is indicative of a success of the execution of the management activity thereon; calculating a similarity index for each of one or more failure ones of the computing machines wherein the corresponding result is indicative of a failure of the execution of the management activity thereon; and prioritizing the computing machines which failed to accept a policy according to the corresponding similarity indexes.

Abstract: A solution is proposed for managing a plurality of computing machines. A corresponding method comprises causing each computing machine of at least part of the computing machines to execute a management activity on the computing machine; receiving a corresponding result of the execution of the management activity on each computing machine; determining a success fingerprint according to one or more characteristics of each of at least part of one or more wherein the corresponding result is indicative of a success of the execution of the management activity thereon; calculating a similarity index for each of one or more failure ones of the computing machines wherein the corresponding result is indicative of a failure of the execution of the management activity thereon; and prioritizing the computing machines which failed to accept a policy according to the corresponding similarity indexes.

Abstract: A mechanism is provided updating a knowledge base of a sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term. Messages comprising natural language are received. Using content of the knowledge base, a decision is made as to whether at least one message of the received messages has a positive sentiment or a negative sentiment. A term is extracted from the message that is not present in the knowledge base. Based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, a score value of the term is calculated, and the term and the calculated score value are stored into the knowledge base.

Abstract: A mechanism is provided for managing a plurality of computing machines. An indication of a management activity having an applicability rule is deployed to a plurality of computing machines to cause each of the plurality of computing machines having corresponding values of one or more characteristics fulfilling the applicability rule to apply the management activity. Results are received indicating that the management activity has been applied successfully on corresponding success computing machines and that the management activity has not been applied on corresponding excluded computing machines. One or more candidate computing machines of the excluded computing machines is selected according to a comparison of the corresponding values of the further characteristics of the excluded computing machines with a success fingerprint.

Abstract: Mechanisms are provided for automatically expanding a virtual storage of a virtual machine. The virtual machine monitors a usage of the virtual storage of the virtual machine. The virtual machine determines, based on the monitoring of the usage of the virtual storage, whether to expand the virtual storage of the virtual machine. In response to the virtual machine determining to expand the virtual storage of the virtual machine, a virtual machine manager executes one or more operations to expand the virtual storage. The monitoring and determining may be performed by a virtual storage management agent executing within the virtual machine and which may send an expansion request to an authorization engine to request expansion of the virtual storage.

Abstract: In an approach to event notification, one or more computer processors determine a baseline for a volume of activity on a social media website in a geographic location. One or more computer processors determine whether a deviation from the baseline for the volume of activity occurs. Responsive to determining a deviation from the baseline occurs, one or more computer processors identify an event that caused the deviation. One or more computer processors calculate an estimate of population for the geographic location. One or more computer processors identify based, at least in part, on the identified event and the estimated population, a relevant party to be notified of the event. One or more computer processors generate a recommendation based, at least in part, upon one or more of the identified event, the estimate of population, and the identified relevant party.

Abstract: In an approach to event notification, one or more computer processors determine a baseline for a volume of activity on a social media website in a geographic location. One or more computer processors determine whether a deviation from the baseline for the volume of activity occurs. Responsive to determining a deviation from the baseline occurs, one or more computer processors identify an event that caused the deviation. One or more computer processors calculate an estimate of population for the geographic location. One or more computer processors identify based, at least in part, on the identified event and the estimated population, a relevant party to be notified of the event. One or more computer processors generate a recommendation based, at least in part, upon one or more of the identified event, the estimate of population, and the identified relevant party.