Abstract: A machine-learning model is trained to cluster support requests based on the contents of the support requests. A user of the recommendation system may select a set of support requests to be clustered. Based on the selected set of support requests, the trained machine-learning model may be tuned and used to cluster the selected set of support requests. Using the characteristics of the support requests in one or more generated insights, one or more tools suitable for providing automated support for the cluster of support requests may be identified. Using a knapsack-based approach, one or more of the identified tools is selected for recommendation to the user.

Abstract: This disclosure describes implementations for categorizing and displaying a Gantt chart of categorized errors, as well as performing an odd pattern analysis to identify anomalous errors including obtaining a log file that includes a number of log entries where each log entry includes an error message. Converting each error message into an error vector using a predictive model. Determining a category for each error vector using a K-nearest neighbor algorithm, and generating a Gantt chart representing error messages arranged hierarchically by category as a function of time for display at a user interface.

Abstract: Support for a product may be provided using an interactive chat interface. The interface may provide recommendations for actions the user may take to address the issue. A support assistant tree may be used in providing information to the user via the user interface. The user interface may include options selectable by the user to traverse the support assistant tree. For example, a server may initially record the state of the user as being at the root node of the support assistant tree. Each child node of the root node may be associated with an answer to a question or an option. When the user selects the associated answer or option, the server provides information related to the corresponding node and updates the recorded state of the user to be the corresponding node. The process may be repeated until a leaf node is reached.

Abstract: Techniques for conserving system resources using smart document retention are disclosed. A computer system may identify a first portion of a plurality of documents as being decision-making documents based on each document in the first portion being scheduled for deletion within a specified period of time, identify a second portion of the plurality of documents as being non-decision-making documents based on each document in the second portion either being scheduled for deletion outside of the specified period of time or having been converted into and stored as a knowledge base document, and determine a corresponding level of similarity between each decision-making document and each non-decision-making document. The computer system may then cause identifications of a subset of the decision-making documents to be displayed on a computing device based on the corresponding level of similarity between each decision-making document in the subset and at least one of the non-decision-making documents.

Abstract: This disclosure describes implementations for categorizing and displaying a Gantt chart of categorized errors, as well as performing an odd pattern analysis to identify anomalous errors including obtaining a log file that includes a number of log entries where each log entry includes an error message. Converting each error message into an error vector using a predictive model. Determining a category for each error vector using a K-nearest neighbor algorithm, and generating a Gantt chart representing error messages arranged hierarchically by category as a function of time for display at a user interface.

Abstract: This disclosure describes implementations for categorizing and displaying a Gantt chart of categorized errors, as well as performing an odd pattern analysis to identify anomalous errors including obtaining a log file that includes a number of log entries where each log entry includes an error message. Converting each error message into an error vector using a predictive model. Determining a category for each error vector using a K-nearest neighbor algorithm, and generating a Gantt chart representing error messages arranged hierarchically by category as a function of time for display at a user interface.

Abstract: This disclosure describes implementations for categorizing and displaying a Gantt chart of categorized errors, as well as performing an odd pattern analysis to identify anomalous errors including obtaining a log file that includes a number of log entries where each log entry includes an error message. Converting each error message into an error vector using a predictive model. Determining a category for each error vector using a K-nearest neighbor algorithm, and generating a Gantt chart representing error messages arranged hierarchically by category as a function of time for display at a user interface.

Abstract: Programs, systems, and methods for generating a support tree for automated resolution of user issues. In some embodiments, historical data may be obtained from a history of support interactions including historical issues and solutions to the historical issues. Most used solutions may be determined and stored with associated historical issues. Clusters of issues and solutions may be generated and labeled for generation of support tree nodes. Furthermore, solutions may be correlate such that a plurality of solutions may be provided for the resolving user issues. In some embodiments, issue data indicative of user issues may be provided by users. As the user provides issue data, deflections between nodes on the support tree may narrow a field of potential solutions to one or more solutions to be provided to the user.

Abstract: Techniques for implementing a dynamic intelligent log analysis tool are disclosed. In some embodiments, a computer system performs operations comprising: obtaining a log file comprising a plurality of log entries, each log entry comprising an error message; identifying a set of unique words from the error messages; for each error message, computing a term-frequency vector based on a frequency of occurrence for each unique word of the set of unique words in the error message; for each error message, computing a similarity measure between the term-frequency vectors of the error message and every other error message of the log entries; for each error message, computing a score based on a sum of the similarity measures; and displaying an indication of one or more of the error messages on a computing device based on the scores for the one or more of the error messages.

Abstract: Systems, methods, and computer media are described for user risk assessment using similarity analysis. Records of transactions performed by a user while in previous enhanced application access sessions can be evaluated against records of transactions performed by other users in previous sessions. The more similar a user is to other users, the more likely it is the user was acting in a typical manner, and the less likely the user poses a security risk. A similarity analysis can be performed using a bipartite graph linking a group of users and a group of application transactions. By examining an edge between a user and a performed transaction, other edges (and corresponding other users) can be identified that also performed the transaction. A similarity score can be calculated based on the bipartite graph and can be used to determine a risk classification and allow or deny an enhanced application access session request.

Abstract: A computer system may obtain reference ticket data for reference tickets of an online system, with the reference ticket data indicating a reference issue for a reference component and a reference solution, and also obtain learning content items from a learning management system. The computer system may then create a mapping between the reference issues indicated by the reference ticket data for the reference tickets and the learning content items using an unsupervised machine learning algorithm. The computer system may detect target ticket data that has been provided by a user to the online system via a computing device, with the target ticket data indicating a target issue for a target component, identify a target solution for the target issue based on the target ticket data using the mapping, and cause a recommendation of the target solution to be displayed on the computing device.

Abstract: Programs, systems, and methods for generating a support tree for automated resolution of user issues. In some embodiments, historical data may be obtained from a history of support interactions including historical issues and solutions to the historical issues. Most used solutions may be determined and stored with associated historical issues. Clusters of issues and solutions may be generated and labeled for generation of support tree nodes. Furthermore, solutions may be correlate such that a plurality of solutions may be provided for the resolving user issues. In some embodiments, issue data indicative of user issues may be provided by users. As the user provides issue data, deflections between nodes on the support tree may narrow a field of potential solutions to one or more solutions to be provided to the user.

Abstract: Techniques for implementing a dynamic intelligent log analysis tool are disclosed. In some embodiments, a computer system performs operations comprising: obtaining a log file comprising a plurality of log entries, each log entry comprising an error message; identifying a set of unique words from the error messages; for each error message, computing a term-frequency vector based on a frequency of occurrence for each unique word of the set of unique words in the error message; for each error message, computing a similarity measure between the term-frequency vectors of the error message and every other error message of the log entries; for each error message, computing a score based on a sum of the similarity measures; and displaying an indication of one or more of the error messages on a computing device based on the scores for the one or more of the error messages.

Abstract: A system, a method, and a computer program product for generation of keywords for a solution note for resolving an issue associated with a computing component. A dataset for training a keyword data model is received. The dataset includes a plurality of variables associated with one or more values. The keyword data model is configured for determination, as a function of one or more variables in the plurality of variables, of one or more keywords in a plurality of keywords associated with a computing solution in a plurality of computing solutions for resolving a problem with an operation of a computing component in a plurality of computing components. The keyword data model is trained using the received dataset and the keyword data model is applied to one or more variables in the received dataset to generate one or more keywords. One or more keywords associated with the computing solution is generated.

Abstract: Systems, methods, and computer media are described for user risk assessment using similarity analysis. Records of transactions performed by a user while in previous enhanced application access sessions can be evaluated against records of transactions performed by other users in previous sessions. The more similar a user is to other users, the more likely it is the user was acting in a typical manner, and the less likely the user poses a security risk. A similarity analysis can be performed using a bipartite graph linking a group of users and a group of application transactions. By examining an edge between a user and a performed transaction, other edges (and corresponding other users) can be identified that also performed the transaction. A similarity score can be calculated based on the bipartite graph and can be used to determine a risk classification and allow or deny an enhanced application access session request.

Abstract: A business data database may store transaction business information of an enterprise along with existing structures, rules, and classification recommendations. A business rules framework agent platform may execute supervised machine learning and generate industry agnostic relationship scores and classification scores based on data in the business data database. The business rules framework agent platform may then optimize data and table structures, using relation graph-based evaluation, in accordance with taxonomy data and the classification scores. A rules and configuration database may store the optimized data and table structures from the business rules framework agent platform. Further, a business configuration and rules mapper platform may identify business configuration data and business rules classification data based on the optimized data and table structures.

Abstract: Methods and system are disclosed that relate to optimizing search for data. In one aspect, an attribute vector may include unique value identifiers and be associated with a dictionary structure. For a unique value identifiers stored in an attribute vector and associated with the dictionary structure, start address and end address associated with the unique value identifiers is computed. Based on the computation, a range of positional addresses associated with the unique value identifiers may be generated and stored in a data structure. Upon receiving a request to search for data, the range of positional addresses in which the unique value identifiers may be searched is determined. Based on the determination, a database search engine optimizes the search for data in the attribute vector.

Abstract: A business data database may store transaction business information of an enterprise along with existing structures, rules, and classification recommendations. A business rules framework agent platform may execute supervised machine learning and generate industry agnostic relationship scores and classification scores based on data in the business data database. The business rules framework agent platform may then optimize data and table structures, using relation graph-based evaluation, in accordance with taxonomy data and the classification scores. A rules and configuration database may store the optimized data and table structures from the business rules framework agent platform. Further, a business configuration and rules mapper platform may identify business configuration data and business rules classification data based on the optimized data and table structures.

Abstract: An attribute vector including value identifiers and corresponding to a dictionary structure is identified. A dictionary type encoding structure is generated by virtually partitioning the dictionary structure. The dictionary type encoding structure may include multiple dictionary types. Based on the dictionary encoding structure, the attribute vector may be split to generate multiple attribute vector blocks that may be identified by block transition indices. Based on the dictionary types in the dictionary encoding structure, the value identifiers in the attribute vector blocks are rearranged. Such a rearrangement optimizes the attribute vector for searching the value identifiers.

Abstract: Methods and system are disclosed that generate an execution schedule to optimize a transformation of business. In one aspect, from multiple tables residing in multiple databases and storing business data associate with multiple business management systems, dependencies between the tables may be determined based on attributes associated with the tables. When execution time for transforming business data exists, a decrease time algorithm or a critical path algorithm may be executed to generate execution schedule and to calculate processor idle times during the transformation of business data. Based on the calculated processor idle times, whether or not to execute a local optimization algorithm may be determined. Based on the determination, execution schedule that optimize the transformation of business data may be generated. The transformation of business data may be executed based to the generated execution schedule that optimizes a time consumed for transforming the business data in the tables.