Abstract: A method, system, and computer program product for abnormal data detection. According to the method, a plurality of data points collected at different time points are classified into a plurality of groups. A plurality of groups of potential abnormal data points are determined from the plurality of groups. Correlations between a first group of the plurality of groups of potential abnormal data points with other groups of potential abnormal data points are determined. In response to the first group of the plurality of groups of potential abnormal data points being uncorrelated to a majority of the other groups of potential abnormal data points based on the correlations, data points in the first group are identified as abnormal data points.

Abstract: A method, apparatus, and computer program product for communicating real-time updates among web clients. A structured data object is composed from a set of data nodes according to a domain specific language in response to requesting access to a data within a human resources database. The structured data object is interpreted within a context of a record ID/form to identify content for the web page and to determine whether real-time updates are enabled for the web page. Interpreting the structured data object, displays the form with the content. An event queue is created and a connection to the server hub is established when real-time updates are enabled. In response to receiving an update event in the event queue, a notification of the update event is displayed when the update event matches the record ID/form that a first user has opened.

Abstract: Configuration datasets from multiple, distinct computing systems can be managed. For example, a computing device can receive multiple configuration datasets describing multiple system configurations for at least two distinct computing systems. The computing device can determine key-value pairs based on the configuration datasets. The computing device store relationships between the key-value pairs in a database. The computing device can then receive a configuration dataset from a computing system of a user that can describe a system configuration for the computing system. The computing device can determine a set of key-value pairs based on the configuration dataset. The computing device can analyze the set of key-value pairs based on the relationships stored in the database to determine configuration feedback for the system configuration. The configuration manager can transmit the configuration feedback to the computing system to allow the user to implement the configuration feedback.

Abstract: The technology disclosed relates to maintaining a cache of effective properties in an identity management system employing a graph. In particular, it relates to handling vertex/edge and/or graph topology updates in accordance with update notification requirements configured from a schema and, in conjunction with detecting updating of vertex/edge attributes and/or graph topology, recalculating effective attributes in accordance with the configured notification requirements.

Abstract: This disclosure involves stitching event data using identity mappings. An event analytics system generates and stores an event dataset including first event data for a first set of events associated with a user. The first event data includes timestamps and a device identifier. The system identifies second event data for a second event associated with the user. The second event data includes a timestamp, the device identifier, and a user identifier. The system appends the second event data to the event dataset. Based on the second event data, the system generates and stores an identity mapping that maps the device identifier to the user identifier. Asynchronously to generating and storing the event dataset, based on the identity mapping and a predetermined look-back window, the system updates the first event data to include the user identifier.

Abstract: Embodiments of the present disclosure provide apparatuses, methods, computer program products, and systems for data perspective generation and visualization. Some example embodiments provide advantages of exploring various ideas, represented by and/or associated with one or more perspective data objects, without utilizing a complex re-configuration stage for one or more machine learning models, and/or without utilizing one or more team members conventionally required to ensure proper implementation of the idea(s). Similar advantages are obtained when desiring to change an existing idea, change the level of granularity associated with the processing, and/or the like. Some embodiments are configured to cause rendering of interfaces associated with the processing, and/or enable a user to user interaction for inputting the desired level of granularity of the generated perspectives and/or desired granularity for the analysis of the data set.

Abstract: A method includes receiving a set of documents associated with data discovery. The method further includes receiving, for each document in a subset of the set of documents, an indication of relevancy or non-relevancy of the document for an issue. The method further includes modifying one or more parameters for a machine-learning model based on the indication of relevancy or non-relevancy. The method further includes outputting, for each document in the set of documents, by a machine-learning model, a prediction probability of relevancy to an issue associated with the data discovery, and a ranking of the set of documents based on the prediction probability of relevancy. The method further includes generating a user interface that includes a sampling of the documents for review by a user, where each document is associated with a predicted relevancy tag or a predicted non-relevancy tag.

Abstract: A method includes obtaining a first snapshot of a source system and deriving a set of key-value pairs reflecting the first snapshot. A mirror operation of the source system is performed to obtain CDC change operations. The method obtains a first CDC log as a first sequence of key-value pairs. A second snapshot of the source system is obtained and a set of key-value pairs is derived, which reflects the second snapshot. The first sequence of key-value pairs is compared with the set of key-value pairs to derive corrective CDC operations, captured as a set of key-value pairs. The corrective CDC operations represent corrections to be performed with respect to the first sequence of key-value pairs. A second CDC log is obtained as a second sequence of key-value pairs. The corrective CDC operations ensure that the second sequence of key-value pairs are coherent with the set of key-value pairs.

Abstract: A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Abstract: The present disclosure discloses a chatbot with a stance taking. Opinions may be extracted from candidate replies during a processing of conversation, and the extracted opinions may be compared with the stance taken by the chatbot so as to perform selection on the candidate replies and remove the candidate replies which have conflicts with the stance taken by the chatbot. With such technical solutions, the stance taken by the chatbot may be exhibited.

Abstract: User behaviors are monitored, by machine logic, during a visit to a venue by a user, the user behaviors associated with user interactions with items in the venue, a location of the items being tracked. In real-time, based, at least in part, on the user behaviors and the items, a subsequent behavior of the user is predicted, by machine logic, the predicting resulting in predicted behavior(s). Cognitive recommendations are provided, by machine logic, to the user in real-time during the visit, the cognitive recommendations corresponding to additional item(s) based, at least in part, on the predicted behaviors and the items. Machine learning is used to train a system for facilitating the noted aspects, as well as to update training.

Abstract: A method for synchronizing a local file system (LFS) and a remote (cloud) file system (RFS) includes the step of storing events indicative of differences between the LFS and the RFS, where the events are associated with file system objects of the LFS and RFS. The method further includes the steps of prioritizing the events, generating file system operations for the events based at least in part on the prioritization of the events, and performing at least some of the file system operations to synchronize the LFS and RFS. Prioritizing the events includes defining a plurality of service classes (e.g., based on type of event, event attribute, etc.), assigning a priority to each of the service classes, and assigning each event to one of said service classes. Systems are also described. The invention allocates limited synchronization resources based on a predefined policy to synchronize important file system objects sooner.

Abstract: A computer-implemented method of tagging a text, comprises: determining a value for each of a plurality of locations in a first vector; processing (402), by a trained first neural network component, the first vector to generate a second vector; processing (404), at a trained second neural network component, the second vector to generate a probability score for each of at least ten predetermined tags; determining (406) if each probability score meets a criterion; if the criterion is met, assigning (408) the tag corresponding to the probability score to the text. Each of the locations may correspond to a respective predetermined word, each value relating to existence and/or frequency of the corresponding word in the text, and the number of locations may be between 600 and 20000. The number of locations in the second vector may be fewer than the number of locations in the first vector and is from 100 to 5000.

Abstract: Various embodiments, methods and systems for implementing distributed data synchronization in a distributed computing system, are provided. In operation, a data record of a first data set is accessed. The data record is encoded to generate, for a first distributed invertible bloom filter (“DIBF”) data structure, a first DIBF record. The first DIBF record comprises a data field and a quantifier field that includes a quantifier value, which represents a reference count for the first DIBF record. The first and second DIBF data structures are accessed and decoded based at least in part on computing a difference between a quantifier value in the first DIBF data structure and a quantifier value in the second DIBF data structure. A determination whether a match exists between the first DIBF data structure and second DIBF data structure is made based on computing the difference between the first and second DIBF data structures.

Abstract: A method may include intercepting a database query from a client to a database. The database query may include a first identifier associated with a first database table. A second database table stored at the database may be determined to correspond to the first database table. The database query may be reconstructed by at least replacing the first identifier with a second identifier associated with the second database table. The reconstructed database query may be executed by at least sending, to the database, the reconstructed database query. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: Various embodiments, methods and systems for implementing a distributed computing system asset assembly engine are provided. Initially a first source asset is received from a first distributed Synthetic Data as a Service (SDaaS) upload interface. A second source asset is received from a second a distributed SDaaS upload interface. The first source asset and the second source asset are ingested. Ingesting a source asset comprises automatically computing values for asset-variation parameters of the source asset, where the asset-variation parameters are programmable for machine-learning. A first synthetic data asset comprising a first set of values for the asset-variation parameters is generated. A second synthetic data asset comprising a second set of values for the asset-variation parameters is generated. The first synthetic data asset and the second synthetic data asset in a synthetic data asset are stored.

Abstract: The disclosure herein describes generating test data based on a set of linked data nodes and associated data value rules. A plurality of data fields of a data structure is identified. A plurality of linked data nodes is generated based on the identified data fields, wherein each data node comprises a probability value and data value rules for generating data values for the data fields. Parent nodes of the linked data nodes are collapsed into associated child nodes to form combined data nodes. Test data values for the plurality of data fields are generated based on the combined data nodes and a data structure instance is populated with the generated test data values. The populated data structure instance is provided for use in executing tests. The generation of test data based on the data value rules of linked data nodes provides a realistic data environment for testing.

Abstract: A system can provide a visual representation of an inventory of data entities for a distributed computing system. Inventory data including cost and operational data for data entities such as data centers, servers, and virtual machines, can be converted into a format file. The format file can be used to create a tree of nodes and node summaries corresponding to the data entities. A user interface can display hierarchical and isolated views of the tree revealing parent child relationships between data entities within a computing system infrastructure. Node summaries including cost and utilization data can be displayed to reveal how specific sub-costs such as labor and licensing, are driven by data entities in one level of the infrastructure and pushed to respective parent or child data entities in other levels. Views of the tree can be used to determine areas of inefficiency or reduced value within the computing system.

Abstract: Drivers in different functional paths can use different types of identifiers for the same hardware device, such that the drivers may not be able to natively coordinate their actions related to the hardware device due to incompatible identifier types. However, a driver at a file system layer of one functional path can obtain a volume Physical Device Object (PDO) identifier at a volume layer and find a disk PDO identifier at a disk layer that is associated with the same device number. The driver can also find a parent device instance identifier from the disk PDO identifier, and use the parent device instance identifier as a plug-and-play (PnP) identifier for the hardware device during communications with a second driver in a PnP functional path.

Abstract: A data platform may receive data files from an electronic data interchange (EDI). The data files may be received in multiple different data formats. The data platform may convert the data files to a common data format, extract data elements from the data files converted to the common data format, and assign the data elements extracted from the data files to file identifiers. The data platform may assign the data elements extracted from the data files to attribute identifiers that identify types of data represented by the data elements, aggregate the data elements to create a standardized data set, and map the data elements in the standardized data set to functions. The data platform may generate values based on mapping the data elements to the functions, determine a metric based on combining the values according to a metric definition, and post the metric to the EDI for consumption.