Abstract: A method for use in an issue creation process, before an issue is submitted, in an issue tracking system (ITS), comprises receiving an unstructured user input expressing an issue description and comprising one or more query keywords; querying a database of historic issue data to determine historic issues from the historic issue data that are relevant to the one or more query keywords and, in response to the determination, extracting request types from the historic issues; generating, from the extracted request types, a list of possible request types for allocation to the unstructured user input; receiving a user input of a particular request type selected from the list of possible request types and, in response, allocating the particular request type to the issue. The issue is submitted to the ITS for routing the issue to an appropriate service desk according to the particular request type.

Abstract: A method for performing recursive searching of items of a data structure having a data mode includes creating an instance of a query definition, the instance of the query definition comprising a unique identifier, specifying one or more elements of the query definition, providing the query definition as an input to a query engine. The method further includes the operations of determining, by the query engine, query execution instructions based on the query definition, the query instructions specifying a recursive level-by-level search until a terminal node of the data structure is reached, obtaining results of a query executed based on the query execution instructions; and outputting query results.

Abstract: Data stores that are to be configured as members of a storage group on behalf of a client are identified. The client indicates one or more types of cross-data-store operations to be performed. A transaction manager that makes acceptance decisions for write requests directed to the members based at least partly on contents of a log is instantiated, and respective write transformers are set up for at least some of the types of cross-data-store operations. During a particular time period, the workload at the storage group is monitored, and the number and types of cross-data-store operations is determined. The results of the monitoring are used to determine a billing amount for the client based on a selected pricing policy.

Abstract: Machine data is collected from multiple sources of an operating environment such as an information technology system, factory floor, or the like, into a data intake and query system, in one embodiment. Metrics representative of the environment are included in or derived from the data. Users may interact with an interface to depict a representation of various metrics and interdependencies and that depiction is reflected in a computer storage model. Changes to the computer storage model based on the user interaction may also result in training of a machine learning model according to the user interaction, the machine learning model configured to determine a prediction, classification or clustering of a result of a first search query by utilizing a result of at least a second search query as input to the machine learning model.

Abstract: The present disclosure involves systems, software, and computer implemented methods for providing user interface (UI)-based modifications to adjust and interact with data exchange splits. An example client device can include can identify a data exchange (DE) associated with at least three entities, the DE associated with a total value. A visualization representing the DE can be presented via a UI and can include a chart comprising the total value of the DE exchange divided into value areas that are each associated with a particular entity. In response to a detection of input associated with a selection of an edge between a first and second value area, the combined value area associated with the first and second value areas can be locked. In response to detected movement input associated with the edge, the relative values of the first and second inside the combined value area can be adjusted in the visualization.

Abstract: Systems and methods for assigning personalities to autonomous vehicles are disclosed. In one embodiment, a method is disclosed comprising receiving data from an autonomous vehicle; generating a vector representing the autonomous vehicle based on the data; classifying the vector into one or more personalities; receiving a search query from a user; identifying one or more autonomous vehicles responsive to the search query based on personalities assigned to the one or more autonomous vehicles, the one or more autonomous vehicles including the autonomous vehicle; and transmitting the one or more autonomous vehicles to the user.

Abstract: Methods and systems for improving memory utilization and access speed of database indexes are disclosed. A representation of attributes of items listed on a network-based publication system is identified. The representation includes a plurality of lists of the attributes as a sequence of serialized attributes, each of which stores an attribute identifier (ID) and data corresponding to the attribute identifier. A compact or compressed structure is used to represent the attribute IDs. This representation allows a reader application to quickly determine whether an attribute ID is present in the lists or not. Directly-addressable variable-length codes (DACs) are used, in an unconventional manner, to represent the data. By representing the data in this way, the index takes up less memory for certain types of data and the reader application is able to gain faster access to the index.

Abstract: Systems and techniques that facilitate automated generation of relevant and adequate test datasets based on guarded commands are provided. In various embodiments, a query generation component can generate a query language query based on a first guarded command. In various aspects, an execution component can execute the query language query on a data table to return one or more datasets for testing the first guarded command. In various embodiments, the query generation component can comprise an initialization component that can initialize conditions of a WHERE clause of the query language query based on the first guarded command. In various instances, the query generation component can further comprise a transformation component that can transform the conditions of the WHERE clause of the query language query based on a sequence of guarded commands on which the first guarded command depends.

Abstract: Methods and apparatuses are described for normalizing digital content across databases and generating personalized content recommendations. A server normalizes structured text for each content item to generate unstructured text. The server converts the unstructured text into a multidimensional content item feature set. The server trains a model based upon user profile information, historical content consumption information, historical content recommendation information, and the feature sets. The server receives a request including a vector associated with a user of a client device. The server executes the model using the vector as input to generate interaction prediction scores. The server selects scores above a threshold and identifies content items associated with each score.

Abstract: Virtual storage arrays consolidate branch data storage at data centers connected via wide area networks. Virtual storage arrays appear to storage clients as local data storage; however, virtual storage arrays actually store data at the data center. The virtual storage arrays overcomes bandwidth and latency limitations of the wide area network by predicting and prefetching storage blocks, which are then cached at the branch location. Virtual storage arrays leverage an understanding of the semantics and structure of high-level data structures associated with storage blocks to predict which storage blocks are likely to be requested by a storage client in the near future. Virtual storage arrays determine the association between requested storage blocks and corresponding high-level data structure entities to predict additional high-level data structure entities that are likely to be accessed. From this, the virtual storage array identifies the additional storage blocks for prefetching.

Abstract: A computer-implemented method, a computer program product, and a computer system for detecting an inappropriate data type of a column in a database and correcting an encoding for the column. The computer system detects in a table a candidate column that has a mismatching type definition, using database usage statistics. The computer system determines whether conversion of the candidate column is possible. In response to determining that the conversion of the candidate column is possible, the computer system converts values in the candidate column with a first data type to values in a new column with a second data type. The computer system appends the new column in the table. The computer system registers the new column and the second data type in a metadata catalog. The computer system generates a query plan operator for processing a query for the new column.

Abstract: An empirical approach to providing differential privacy includes applying a common statistical query to a set of databases to produce sample values, both with and without any particular entity's data. The probability density is empirically estimated by sorting the sample values to generate an empirical cumulative distribution function. The cumulative distribution function is differenced across approximately the square root of the number of sample points to get an empirical density function. The statistical query is empirically (?,?)-private if the empirical densities with and without any particular individual differ by a factor of no more than exp(?), with the exception of a set for which the densities exceed that bound by a total of no more than ?.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine-readable instructions that when executed by the processor, may cause the processor to receive a request for result data from a requestor and determine queries to create the result data. The processor may determine a subset of a data set based on the queries. The subset of the data set may be displayed in small multiples by the requestor. The processor may output the subset of the data set as the result data to the requestor. In some examples, the processor may receive a request for additional result data from the requestor. The processor may determine a second subset of the data set to be displayed in the small multiples and output the second subset of the data set as the additional result data to the requestor.

Abstract: An in-context cognitive information assistant is provided by: obtaining a context for a user, wherein the context comprises a calendar activity with one or more other users; supplementing the context by obtaining one or more conversations with the user related to the context; extracting cognitive data for the context and the conversations; and finding relevant materials in a corpus using the cognitive data. The relevant materials are used to prepare the user for interactions with other users.

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining summary content for resources in a domain. In one aspect, a method includes accessing a first resource belonging to a particular domain, selecting an anchor in the first resource linking to a second resource belonging to the particular domain, identifying particular text content in the first resource that is subordinate to the anchor that the second resource includes the particular text content that is subordinate to the anchor, based on determining that the second resource includes the particular text content that is subordinate to the anchor, generating a domain template for the particular domain, the domain template specifying a location of the particular text content in the second resource, and determining, for each respective resource belonging to the particular domain having a structure matching the domain template, respective text content for the respective resource.

Abstract: A system, method, and computer-readable medium for proving feedback on database instructions, identifying, for example, existing patterns and providing suggested replacement instructions. This may have the effect of improving the efficiency of instructions used to create and/or manipulate databases. According to some aspects, these and other benefits may be achieved by parsing received instructions into an organizational structure, traversing the organizational structure for known patterns, and suggesting replacement patterns. In implementation, this may be effected by receiving one or more sets of known patterns and corresponding replacement patterns, parsing received instructions, comparing the known patterns with the parsed instructions, and providing suggested replacement patterns based on one or more known patterns matching the parsed instructions. A benefit of may include reducing Cartesian products during the merging of tables.

Abstract: A method may include inserting, into a hash trie, data records from a database table. The inserting may include traversing the hash trie to identify, for each data record included in the database table, a corresponding node at which to insert the data record. The hash trie may be traversed based on a hash of a key value associated with each data record. The node at which to insert a data record may be identified based on an offset forming a binary representation of the hash of a key value associated with that data record. The offset may include a portion of a plurality of binary digits forming the binary representation. A data record may be inserted at a corresponding node by updating a data structure included at the node. A database operation may be performed based on the hash trie filled with the data records from the database table.

Abstract: A tenant separation architecture is provided to adopt tenant separation for remote integration of various OEM systems with a cloud-based analytics application. The tenant separation architecture provided herein allows for tenant separation in a wide range of modules in different OEM integration scenarios through a token-base authentication. Application server tenants and database tenants may be accessed for application of the tenant separation. Moreover, the tenant separation implementations disclosed herein may be applied to different modules for metadata, tenant configuration, authorization concepts, and general data protection regulation (GDPR) adoption.

Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.