Abstract: Recommending applications is disclosed. An application graph is disclosed that represents applications. Each node of the graph corresponds to an application and edges relate applications that can handle the same file type. When an input application is identified, the graph can be used to recommend other applications that may be a suitable replacement for the input application. The recommendation is based on the graph and its links and on characteristics of the organization.

Abstract: Described is a system for creating an intelligent testing queue for improved quality assurance (QA) testing of services (or microservices). The system may perform a graphical analysis of interactions between services to derive testing constraints. For example, the system may monitor services to identify interactions (e.g. API accesses) between the services, and store the interactions as a directed graph representation. The system may traverse the directed graph (e.g. via a breadth-first search) to determine service dependencies. Based on the probability of failure for the testing operations and the service dependencies, the system create a specialized testing queue. By performing testing operations according to the specialized queue, the system may improve certain metrics associated with QA processes such as mean time to failure (MTTF) and mean time to diagnose (MTTD).

Abstract: Described is a system for creating an intelligent testing queue for improved quality assurance (QA) testing of services (or microservices). The system may perform a graphical analysis of interactions between services to derive testing constraints. For example, the system may monitor services to identify interactions (e.g. API accesses) between the services, and store the interactions as a directed graph representation. The system may traverse the directed graph (e.g. via a breadth-first search) to determine service dependencies. Based on the probability of failure for the testing operations and the service dependencies, the system create a specialized testing queue. By performing testing operations according to the specialized queue, the system may improve certain metrics associated with QA processes such as mean time to failure (MTTF) and mean time to diagnose (MTTD).

Abstract: Techniques are provided for customer service ticket processing using cluster-based data driven guidebooks. One method comprises obtaining a customer service ticket; extracting features related to the customer service ticket, wherein the features comprise a representation of a problem associated with the customer service ticket; assigning the customer service ticket to a given cluster of multiple of customer service ticket clusters based on the features; obtaining a customer service ticket processing guidebook associated with the given cluster that identifies independent actions to perform to address the problem; and processing the customer service ticket based on the customer service ticket processing guidebook. A customer service ticket processing guidebook may be generated for each customer service ticket cluster using historical customer service tickets from the respective cluster.

Abstract: A method and system for performance-driven load shifting. Specifically, the method and system disclosed herein entail transferring user program workloads, for processing, between local computing resources available on a client device and cloud computing resources available on an offload domain based on the assessed performance score of the client device at any given point in time. Seamless load shifting is further guaranteed due substantively to the employment of a mobile network facilitating communications between the client device and the offload domain.

Abstract: Techniques are provided for compression and decompression of log data using pattern recognition. An exemplary method comprises: obtaining at least one log message, wherein the at least one log message comprises a pattern-based message template and one or more message variables, wherein the pattern-based message template was learned from historical log data using a log pattern mining technique; obtaining a compression index that maps a plurality of pattern-based message templates to a corresponding message signature; and writing the one or more message variables and a message signature corresponding to the pattern-based message template of the at least one log message to a log file. The log pattern mining technique learns a given pattern-based message template from the historical log data to represent a plurality of historical log messages. The at least one log message may comprise a historical log message or may be from a real-time stream of log messages.

Abstract: Techniques are provided for compression of log data using field types. An exemplary method comprises: obtaining at least one log message, wherein the at least one log message comprises a message template and at least one message variable, wherein the message template comprises at least one field corresponding to the at least one message variable; obtaining a compression index that maps a plurality of message templates to a corresponding message signature; identifying a predefined field type of the at least one field; selecting a compression technique to apply to the at least one message variable based on the predefined field type to obtain a compressed message variable; and writing the compressed message variable and a message signature corresponding to the message template of the at least one log message to a log file. The at least one log message can be a historical log message or part of a real-time stream of log messages.

Abstract: One example method includes transcribing a portion of the audio component to create a transcription file that includes text, searching the text of the transcription file and identifying information in the text that may include personal information, defining a textual window that includes the information, evaluating the text in the textual window to identify personal information, and masking the personal information in the audio component of the recording. The personal information may be masked with information of a non-personal nature.

Abstract: Recommending applications is disclosed. An application graph is disclosed that represents applications. Each node of the graph corresponds to an application and edges relate applications that can handle the same file type. When an input application is identified, the graph can be used to recommend other applications that may be a suitable replacement for the input application. The recommendation is based on the graph and its links and on characteristics of the organization.

Abstract: Techniques are provided for data-driven scheduling of automated software program test suites. An exemplary method comprises: obtaining a plurality of test cases that test software programs; obtaining a failure likelihood for each of the plurality of test cases; and scheduling the test cases in an order based on the failure likelihoods. Generally, test cases that are more likely to fail are scheduled before test cases that are less likely to fail. Dependencies and/or priorities among the plurality of test cases are also optionally obtained and the scheduling of the test cases is based on the dependencies and/or priorities. The dependencies among the plurality of test cases comprise, for example, an indication of whether a given test case constrains one or more additional test cases.

Abstract: Techniques are provided for identifying anomalies in an Internet of Things (IoT) activity profile of a user using an analytic engine. An exemplary method comprises obtaining data from a plurality of IoT devices of a user, wherein at least one IoT device comprises an agent device that performs an action on behalf of the user; applying the obtained data to a feature engineering module to convert the obtained data into time-series features that capture behavior and/or characteristics of an IoT environment of the user; and applying the time-series features to an analytic engine comprising a multi-variate anomaly detection method that learns one or more patterns in the IoT activity profile of the user for a normal state and identifies an anomaly with respect to an action performed by the agent device based on a health score indicating a deviation from the learned patterns.

Abstract: A DNA storage system for binary digital data. The digital data is deduplicated is encoded into a format for representation in DNA, and DNA representing the data is synthesized and stored in pools in a primary library, a hash library and a data library. The primary library stores DNA for accessing a hash object in the hash library corresponding to a hash of the data, and the hash library stores DNA for accessing a data object in the data library that contains the data. The information in the libraries includes information identifying objects, including keys, unique identifiers (UIDs), pool identifiers, and primers.

Abstract: One example method includes identifying a changed microservice whose code has been changed, mapping, for each microservice in a group of microservices that includes the changed microservice, microservice dependencies associated with the changed microservice, determining, for each microservice in the group, a relative risk that the microservice will be adversely affected by the change to the code of the changed microservice, and based on the respective relative risks, generating a test order indicating an order in which the microservices in the group will be tested.

Abstract: One example method includes accessing an appearance history of a person, the appearance history including information concerning an appearance of a person at a particular time, generating, based on the appearance history, a forecast that comprises a probability that the person will appear again at some future point in time, determining that the probability meets or exceeds a threshold, and updating a high probability group database to include a facial image of a face of the person.

Abstract: Techniques are provided for facial recognition using a high probability group database and a facial network of related persons.

Abstract: Embodiments for dynamically allocating journal space for Do streams across multiple applications. A shared Do stream process has a dynamic block allocation component that provides a certain amount of buffering of a data flush for an application, using space that would normally be allocated for, but unused by, other applications, thus preventing the need for one or more of the applications to move to fast-forward mode when possible. Certain machine learning techniques are used in order to predict the required Do stream for each application according to past experience with the application, and this prediction is used to intelligently allocate Do Streams between the different applications.

Abstract: One example method includes collecting telemetry data for each of a group of virtual machines (VM), and each of the VMs is associated with a user, collecting usage data for each of the VMs, creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user, creating, for each user, a user profile that is based on the user profile definition for that user, clustering the users based on similarity of their respective user profiles, and generating a recommended VM hardware configuration for a VM of one of the users.

Abstract: One example method includes monitoring performance of an element of a runtime environment, where the monitoring includes collecting performance information concerning the element, analyzing the collected information, detecting, based on the analysis of the collected information, an anomaly in the performance of the element and, in response to detection of the anomaly, automatically marking a snapshot of the runtime environment element, and the marking of the snapshot overrides a retention policy applicable to the snapshot.

Abstract: Techniques are provided for generating workspace recommendations based on prior user ratings of selected workspaces, as well as other similar selections. One method comprises obtaining user workspace ratings provided by a user; calculating a first workspace recommendation score for workspaces that the user previously rated based on the obtained user workspaces ratings; calculating a second workspace recommendation score for additional workspaces that are: (i) similar to workspaces previously rated by the user based on a predefined workspace similarity metric, and/or (ii) selected by similar users, based on a predefined user similarity metric; and recommending workspaces for the user based on the first workspace recommendation score and the second workspace recommendation score.

Abstract: Embodiments include facilitating DNA storage of digital data including a plurality of data assets in a network by building a causal graph of the network and the relationship of the data assets; computing a value of each data asset; computing, using the causal graph and data values, a radius of recovery for each data asset; classifying each data asset as appropriate DNA stored by assigning a numerical ranking of each data asset; defining manual constraints and a DNA storage configuration; and generating a ranked list of recommended data assets for storing in the DNA storage using the classification, manual constraints and DNA storage configuration.