Abstract: Implementations of the disclosure provide for anticipatory pre-execution of queries. In an implementation of the disclosure, a method for anticipatory pre-execution of queries includes the computation of an execution cost of each of a multiplicity of different queries to a data source scheduled at a specified time on a specified date. The method also includes monitoring a querying processing schedule and detecting from the monitoring, unscheduled time on a particular date prior to the specified time on the specified date. Finally, the method includes responding to the detection by first selecting a most expensive one of the different queries in terms of execution cost, for instance an amount of computing resources consumed in executing a corresponding one of the different queries, and then executing the selected most expensive one of the different queries during the unscheduled time on the particular date prior to the specified time on the specified date.

Abstract: Disclosed herein is a method, system, or non-transitory computer readable medium for evaluating a query on a columnar dataset comprising one or more dictionaries associated with columns in the dataset. The method includes receiving a request to perform a query comprising at least a operator and a request to return information about a value of interest in a columnar dataset stored on cloud storage. At least one column in the columnar dataset is based on a dictionary. The dictionary maps one or more values for a column to one or more respective identifiers. The method determines whether to perform dictionary filtering for the query by calculating a metric based on one or more factors. Responsive to the metric being below a threshold, which may be predetermined, the method performs the dictionary filtering.

Abstract: Systems and methods are described for a query conversion system to convert a first query string from a first version of a query language to a second version of the query language. The query conversion system may be associated with a tokenizer and parser, code converter, compatibility library, and a query formatter. The tokenizer and parser may tokenize and parse a query string to create a first node tree with commands. The code converter may parse the first node tree while using the compatibility library to convert the commands and generate a second node tree. The query formatter may create a second query string executable by the second version of the query language.

Abstract: Methods for operation fragmentation with metadata serialization in query processing pushdowns are performed by systems and devices. A compute node receives a query directed to database data, and generates query text fragments. Portions of metadata of the database are read from different page servers, and are serialized by the compute node. Page identities of data pages in a page server that stores the data are determined from a page index at the compute node, and the compute node provides the text fragments, the serialized metadata, and the page identities to the page server storing the data. The page server compiles the text fragments based on the serialized metadata to generate an executable query plan for the query. The page server initializes and performs execution of the executable query plan against the data as a pushdown query operation, and a result of pushdown query operation is provided to the compute node.

Abstract: Techniques and solutions are provided for improving query execution. Data models can be complex, which is often reflected in queries against such data models. The present disclosure provides a query refactoring technique where a complex query, such as a query expressed as a single select statement, can be formulated as a series of less complex queries. The workload of a database can be reduced by combining results of the less complex queries outside of the database. The present disclosure provides a framework for implementing these techniques, where the framework includes a virtual cube, a calculation engine, and one or more operations, which can all be implemented as classes in a programming language, and where a generic class or interface can help guide users in developing subclasses that provide a reformulation or refactoring of a complex query.

Abstract: A system for bulk updating resource data of parameters included in a view is provided. The system includes at least one processor configured to: receive a user request to bulk import data in a file including rows of the data, each row corresponding to a different instance of the view; make an import data API call to bulk import the data into one or more tables to which the data of the parameters is to be saved; make a save data API call to update existing resource data of the parameters, stored in the one or more tables, with the data included in the file; and update, in the one or more tables, the existing resource data of the parameters for the plurality of instances with the data included in the file, wherein the import data save data APIs are generic across a plurality of views and tables.

Abstract: According to an aspect, a computer-implemented method for improving query performance in a databased management system (DBMS) includes receiving, by a query optimizer of the DMBS, a query for execution. The method also includes creating, by the query optimizer, an initial access path for the query based on state and executing the query based on the initial access path. The method further includes observing, by a query agent of the DBMS, the execution of the query and modifying at least one of the state based on a determination by the query agent that a change to the initial access path would improve the execution of the query.

Abstract: A method and one or more non-transitory storage media are provided to train and implement a one-hot encoder. During a training phase, computation of an encoder state is performed by executing a set of relational statements to extract unique categories in a first training data set, associate each unique category with a unique index, and generate a one-hot encoding for each unique category. The set of relational statements are executed by a query optimization engine. Execution of the set of relational statements is postponed until a result of each relational statement is needed, and the query optimization engine implements one or more optimizations when executing the set of relational statements. During an encoding phase, a set of categorical features in a second training data set are encoded based on the encoder state to form a set of encoded categorical features.

Abstract: Dynamic plan interpretation may be implemented to perform queries. A query is received at a database system. A plan to perform the query is generated. For at least a portion of the plan to perform the query, a domain-specific language may be applied to interpret and execute the portion of the plan to perform the query using one or more previously compiled executors instead of generating and compiling code to execute the portion of the plan. Returning a result of the query according to the execution of the plan.

Abstract: Systems and methods for platform independent secure customer data migration from one data storage to another data storage. A synchronization server includes a translation layer and an authentication layer. The translation layer encodes data from one data storage access language and schema to another distinct data storage access language and schema. Before the full transition to the second data storage, the translation layer also encodes, updates and inserts data into the first data storage. Existing references to the first data storage as well as new references to the second data storage are supported using an authentication layer. After a request for secure customer data, a determination is made which data storage is accessed from an obtained authentication factor. When the second data storage is accessed, the translation layer is employed to encode the first data storage object references to the corresponding second data storage object.

Abstract: A data processing system implements obtaining first textual content in a first language from a first client device; determining that the first language is supported by a first machine learning model; obtaining a guard list of prohibited terms associated with the first language; determining that the textual content does not include one or more prohibited terms associated based on the guard list; providing the first textual content as an input to the first machine learning model responsive to the textual content not including the one or more prohibited terms; analyzing the first textual content with the first machine learning model to obtain a first content recommendation; obtaining a first content recommendation policy that identifies content associated with the first language that may not be provided as a content recommendation; determining that the first content recommendation is not prohibited; and providing the first content recommendation to the first client device.

Abstract: Example solutions for executing a query in a declarative graph query language include receiving the query for data in a database and determining if one or both of i) a pattern in the query, and ii) the data in the database render the query suitable for being executed, at least in part, in parallel. If either condition indicates that the query is suitable for being executed, at least in part, in parallel, one or more fork operations and join operations are injected into a query plan, and the query is executed according to the query plan. Some examples further determine whether executing the query in parallel is computing resource-efficient, and only executes computing resource-efficient queries in parallel.

Abstract: Disclosed is an approach for applying fine-grained hints to obtain optimal control over error handling during query compilation. Using fine-grained hints causes less disruption to the query plans generated by the optimizer as they constrain specific features while retaining scope for the rest of the optimizer functionality. This approach provides a query plan of better quality that avoid errors, particularly in comparison to coarse-grained approaches that restrict processing to earlier database versions or which disables cost-based processing.

Abstract: Embodiments herein describe techniques for preparing and executing tasks related to a database query in a database accelerator. In one embodiment, the database accelerator is separate from a host CPU. A database management system (DBMS) can offload tasks corresponding to a database query to the database accelerator. The DBMS can request data from the database relevant to the query and then convert that data into one or more data blocks that are suitable for processing by the database accelerator. In one embodiment, the database accelerator contains individual hardware processing units (PUs) that can process data in parallel or concurrently. In order to process the data concurrently, the data block includes individual PU data blocks that are each intended for a respective PU in the database accelerator.

Abstract: A training process produces a machine-learned model that, once trained, can be applied to process different types of data items. The training process accomplishes this result by combining data items in a training set with type-specific parameter information, to produce supplemented data items. The training process then trains a model based on the supplemented data items. Training involves adjusting model weights together with the type-specific parameter information. In an inference stage of processing, the technology combines a new data item with an appropriate type of trained parameter information, and then maps the resultant supplemented data item to an output data item. The technology is particularly effective in adapting an initial model to a new subject matter domain in those situations in which a robust set of data items that pertain to the subject matter domain and which have a desired type is lacking.

Abstract: Systems and methods for managing access data are disclosed. One method can comprise receiving prediction information relating to one or more content options and requesting access information associated with the prediction information. At least a portion of the received access information can be processed to provide a preliminary access decision. A request for access relating to the one or more data options can be received and an access decision based at least in part on the preliminary access decision can be provided.

Abstract: Various embodiments herein each include at least one of systems, methods, devices, and software voice-device aided operation, such as operation of a store, elements therein, and providing customer assistance, among other voice device capabilities. One embodiment, in the form of a method, includes receiving voice input at a Point-Of-Sale (POS) location, the voice-input including a command utterance. This method may then transmit the voice input via a network to a network service for processing and then receive at least one response to the transmitted voice command upon which a data processing action may be performed, such as providing a voice output based on a received text, manipulating a computing device or an output device thereof, and the like.

Abstract: Certain aspects of the present disclosure provide techniques for configuring a software application through a remote configuration service. An example method generally includes receiving, from a remote configuration service, a declarative construct. Generally, the declarative construct includes a definition of a workflow in an application to be executed within a player application deployed on a client device. Information associated with the definition of the workflow is extracted by parsing the declarative construct according to a schema defining a format of the declarative construct. The workflow is executed in the player application based on the extracted information defining functionality of the workflow.

Abstract: A data processing method and system for a cloud platform, and an electronic apparatus and a storage medium. The data processing method includes: an acquisition rate of performance data of each data type in a cloud platform is determined (S101); according to the acquisition rate, a shard corresponding to each data type is determined (S102); a database mapping table according to correspondence relationships between shards and databases is generated (S103); and when a data operation request is received, according to the database mapping table, a target database corresponding to the data operation request is determined, and a corresponding operation in the target database is executed (S104). By using the above method, the load balancing of a cloud platform can be realized, and the data processing efficiency can be improved.

Abstract: Dynamic partitioning of a search space of queries is implemented for flexible, heuristic database querying. Search space partitioning refers to dividing the search space for a submitted query into smaller parts by augmenting the queries to append thereto an additional predicate comprising a dynamic partition key and a value(s) selected based on heuristics (e.g., recency and/or relevancy of the value(s)). A plurality of candidate augmentations of the query and corresponding query plans are generated and evaluated based on additional heuristics to determine which can be executed to yield the best results in terms of result quality and latency. This query plan is selected and executed for retrieval of results that satisfy the query, with pagination utilized for presentation of the results. The procedure of generating candidate query plans, selecting one of the candidates for execution, and paginating results is repeated until a search termination criterion is satisfied.

Abstract: Techniques and systems can analyze information associated with instructions to sort data to determine an identifier common to at least a plurality of individual instructions to sort the data. A correspondence of the identifier to a sort identifier used to sort the data can be determined. Based on the determined correspondence, the techniques and systems can sort the data based on the identifier.

Abstract: A system and method to generate and manage clusters of search terms. The system and method generate a cluster including a set of search terms. A size of the cluster is compared to a cluster size threshold level to determine that the cluster comprises a valid-sized cluster. A noise rate of the cluster is compared to a noise rate threshold level to determine that the noise rate is less than the noise rate threshold level, wherein the noise rate relates to a distance of the cluster from a set of other clusters. Performance data associated with the cluster is generated based on a comparison with one or more cluster performance metric thresholds. A user interface is generated to display information associated with the cluster comprising the performance data. Input is received via the user interface comprising an adjustment of the one or more cluster performance metrics thresholds.

Abstract: A computer implemented method for automatically customizing electronic commerce includes analyzing information relevant to the user's purchasing preferences, determining the user's purchasing preferences based on the analysis of the relevant information, determining a shopping list, wherein the shopping list comprises one or more products the user wants to purchase and one or more indications of the user's purchasing preferences for each of the one or more products, generating one or more digital shopping carts based on the shopping list, comparing a product in at least one of the one or more digital shopping carts to a corresponding product and a corresponding purchasing preference on the shopping list, determining one or more preferred digital shopping carts based on the comparison, and displaying to the user the one or more preferred digital shopping carts.

Abstract: A system, apparatus and methods for generating database entries and tools for accessing and searching a database from an Ontology. Starting with an Ontology used to represent data and relationships between data, the system and methods described enable that data to be stored in a desired type of database and accessed using an API and search query generated from the Ontology. Embodiments provide a structure and process to implement a data access system or framework that can be used to unify and better understand information across an organization's entire set of data. Such a framework can help enable and improve the organization and discovery of knowledge, increase the value of existing data, and reduce complexity when developing next-generation applications.

Abstract: Techniques may include receiving a plurality of spans of trace data at a computing system during a first time period. The techniques may include storing the plurality of spans in a span partition of a data store. The data store can contain a plurality of span partitions with spans that are grouped in the partition by trace identifier. The device may include generating a timestamp partition, with an index of timestamps by trace identifiers, for the first time period. The techniques may include storing the timestamp partition in the data store. Also, the techniques may include identifying at least two timestamp partitions that correspond to a second time period that preceded the first time period. The techniques may include generating and storing a primary compacted timestamp partition by combining the at least two timestamp partitions.

Abstract: An enterprise database server may store enterprise information. An application layer server, coupled to the enterprise database server, may use an application programming language (e.g., ABAP) to access the enterprise information. The application layer server may execute a plurality of domain runtimes, each associated with a domain service (e.g., transactional, SQL, analytical, search, etc.). The application layer server may also access, by a first domain runtime, a first data service entity in an infrastructure for data modelling (e.g., CDS) that enables data models to be defined and consumed on the enterprise database server. Similarly, the application layer server may access, by a second domain runtime, a second data service entity in the infrastructure for data modelling. According to some embodiments, the first data service entity includes a first provider contract and the second data service entity includes a second provider contract (different than the first provider contract).

Abstract: An example method of tuning a machine learning operation can include receiving a data query comprising a reference to an input data set of a database, generating a plurality of unique sets of hyperparameters by varying a hyperparameter value of each set of hyperparameters of the plurality of unique sets of hyperparameters based on the input data set, in response to receiving the data query, training a plurality of machine learning models using the input data set of the data query, each of the plurality of machine learning models configured according to a respective one of a plurality of unique sets of hyperparameters, selecting a first machine learning model of the plurality of machine learning models based on an accuracy of an output of the first machine learning model, and returning the output of the first machine learning model in response to the data query.

Abstract: Methods, systems, devices and/or apparatus related to finding and sharing information on a large-scale or global computer network such as the Internet, and, specifically, to allow for collaboratively searching for, organizing and/or accessing information.

Abstract: In a set of data analytics queries, at least a one of the queries comprising more than one operator, and each query being at least one of i) a producer of data for an other query in the set, and ii) a consumer of data from an other query in the set. In such examples, one or more computing devices identify each producer/consumer relationship between the queries. The one or more computing devices identify one or more optimizations among the queries based on the identified relationships. The one or more computing devices then apply at least one identified optimization to at least one of the queries.

Abstract: A location-aware search assist capability identifies location-aware search query suggestions using location information associated with the location-aware search query suggestions. A user's search query input and location and a location associated with each location-aware search query suggestion candidates may be used to identify a set of search query suggestions for presentation to the user. Location-aware search query suggestion candidates may be ranked in accordance with a closeness of each one's location to the user's location. The ranking may be performed using a score, such as a popularity score associated with each search query suggestion candidate. The location-aware search query suggestion candidates having a location closer to the user's location may be promoted by adjusting each candidate's popularity score upward, and the search query suggestion candidates that are farther away from the user's location may be demoted by adjusting each such candidate's popularity score downward.

Abstract: Systems and methods for processing loops in computational graphs representing machine learning models are disclosed. An example method begins with obtaining data representing a computational graph. Data identifying an allocation of the computational graph across devices is obtained. Additionally, one or more nodes in the computational graph that represent a respective control flow statement are identified. For each identified node, a structure of nodes and edges that represents an operation that provides a current state of recursion or iteration in the respective control flow statement is generated. This structure is inserted into the computational graph and the allocation of nodes to devices is modified to assign the structure to a device.

Abstract: Systems and methods for recommending content to an online service user are presented. In response to a request from a user, a set of n-grams of the request are generated, with each n-gram comprising one or more terms from the request and each n-gram of the set of n-grams being unique. Embedding vectors projecting the n-grams into a content item embedding space are generated, and the embedding vectors are combined into a representative embedding vector for the request. The nearest content items are identified according to a distance measure between a projection of the representative embedding vector and embedding vectors of content items of a corpus of content items in the content item embedding space. At least some of the nearest content items are returned as recommended content in response to the request from the user.

Abstract: A method for execution by a database system includes determining physical network topology data of a plurality of nodes of the database system utilized to facilitate query executions. Initial route selection data indicating a set of initial relay nodes for each of a plurality of pairs of the plurality of nodes is based on the physical network topology data by selecting one of a plurality of possible initial route options for the each of the plurality of pairs of the plurality of nodes. The initial route selection data is sent to the plurality of nodes. A query resultant is generated by facilitating execution of a query, via the plurality of nodes, by facilitating routing of data blocks between at least one of the plurality of pairs of the plurality of nodes via a set of intermediate nodes selected based on the initial route selection data in accordance with a dynamic data block routing scheme.

Abstract: This disclosure describes, in part, techniques for estimating frequency values of top contributors of a high cardinality data set in a computationally efficient manner. In one example, the techniques utilize a probabilistic data structure, such as a count-min-sketch, for identifying which contributors are the largest contributors to a particular metric. After identifying these largest contributors using the probabilistic data structure, the techniques may store the contributor IDs and the their respective estimated cumulative value (e.g., frequency counts) in a top-N list.

Abstract: Embodiments of the present invention relate to a method, system, and computer program product for multiplexing data operation. In some embodiments, a method is disclosed. A query for at least one table comprising a plurality of data records is received. The query indicating a plurality of data operations to be performed on the plurality of data records. The plurality of data operations are combined into a target data operation. An intermediate result of the query is generated by performing the target data operation on the plurality of data records. A final result of the query is determined based on the intermediate result. In other embodiments, a system and a computer program product are disclosed.

Abstract: One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: providing, on a display device, a communications window, wherein the communications window comprises a request for user input to start a conversation with an online assistant; receiving a user input identifying a request by the user to be completed by the online assistant; updating, based upon the request, the communications window, wherein the updated communications window comprises a summary of the conversation including prepopulated variable terms and allows user interaction to adjust the summary including adjustment of the prepopulated variable terms; and iteratively updating the communications windows based upon user input adjusting the summary. Other aspects are described and claimed.

Abstract: One embodiment of the invention is directed to a method for performing efficient data searches in a distributed computing system. The method may comprise, receiving a search request including a key. The key may be provided to a block-based table manager via a programming interface external to a virtual machine executing on a computer system. The programming interface may provide a translation between a first programming framework of the virtual machine and a second programming framework of the block-based table manager. Providing the key may cause the block-based table manager to conduct a search for a value corresponding to the key. The value may be provided in response to the search request. Utilizing such block-based tables may enable a data search to be performed using on-board memory of computing node operating within a distributed computing system.

Abstract: Aggregation in a computing system can include receiving, at a service node of the computing system, a first query specifying aggregation and translating the first query into a second query having a first canonical format and specifying the aggregation. The method can include forwarding the second query to a first subset of a plurality of endpoint nodes and translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node. The third query can specify a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source. The endpoint nodes can initiate execution of the third query by the data sources and provide an aggregated result including a result from the data source(s) to the service node.

Abstract: In one embodiment, the present disclosure includes a cloud based management system controlling a plurality of automated food production systems residing at different locations. A machine learning algorithm may be used to modify recipe data to customize recipes.

Abstract: Methods, systems, and computing platforms for data communication are disclosed. The processor(s) may be configured to electronically receive a SQL query request for a relational computer readable database having plurality of computer readable records. The processor(s) may be configured to electronically process the SQL query request with a machine learning controller based on a set of machine learning training data so as to output at least one probable smart execution plan for executing the SQL query request. The processor(s) may be configured to electronically output the at least one probable smart execution plan for executing the SQL query request to a relational database management processing module.

Abstract: A user may submit a search query to a search engine, which may process the search query and generate a set of results. Previous actions of the user or of other users may be used to identify recommended search queries. The recommended search queries may be presented to the user along with the search results for the query. The location of the recommended search queries may be fixed, set by the user, or determined on a query-by-query basis. A user interface element operable to cause the presentation of one or more recommended search queries may be presented after a delay. After an additional or independent delay, the recommended search queries may be presented automatically. The delays may be fixed, set by the user, or determined on a query-by-query basis.

Abstract: Methods and systems for querying application metadata objects are provided. The method may comprise receiving a query identifying an application; executing the query against a metadata model for the application to obtain a result, the result identifying a first metadata object satisfying the query, wherein the metadata model identifies a plurality of metadata objects and hierarchical relationships between the plurality of metadata objects, and wherein the metadata model comprises information about structure and semantics of data used by the application; determining, from the plurality of metadata objects, a second metadata object referenced by the first metadata object; determining, from the plurality of metadata objects, a third metadata object that is a parent object of the first metadata object; building a directed graph including the first metadata object, the second metadata object, and the third metadata object; and providing a response including the directed graph to the query.

Abstract: Systems and methods that offer significant improvements to current content processing management systems for heterogeneous and widely distributed content sources are disclosed. The proposed systems and methods are configured to provide a framework and libraries of extensible components that together are designed enable creation of solutions to acquire data from one or more content repositories, possibly distributed around the world across a wide range of operating systems and hardware, process said content, and publish the resulting processed information to a search engine or other target application. The proposed embodiments offer an improved architecture that incorporates manager nodes and worker (processing) nodes, where worker nodes are configured to scan and process data, while manager nodes are configured to handle all allocation of work (including throttling) and control state and failover. Such an arrangement enables the system to perform with greater scalability and reliability.

Abstract: Embodiments of the disclosure provide systems and methods for identifying hierarchically related files stored in a database. According to one embodiment, this can be accomplished using a pipeline of worker applications which each use a specific table stored in the database 35 and a sequence or series of queues. Generally speaking, each worker application can read a message identifying tasks to be performed from one queue in the sequence of queues, perform those tasks using the appropriate table from the database to identify a set of files, and write one or more messages to a subsequent queue in the sequence or series of queues to pass results and/or further tasks to be performed to a subsequent worker application in the pipeline of worker applications.

Abstract: Described are methods, systems and computer readable media for computer data distribution architecture connecting an update propagation graph through multiple remote query processors.

Abstract: This document describes techniques and devices for a refined search with machine learning. These techniques improve computer-aided searches through enabling selection of search criteria used in a prior search and providing a refined search result based on that selection. Furthermore, a machine-learning component of a search engine can be altered to improve future search results based on the selection and an indication of the desirability of the refined search result.

Abstract: Techniques are described for responding to aggregate queries using optimizer statistics already available in the data dictionary of the database in which the database object targeting by the aggregate query resides, without the user creating any additional objects (e.g. materialized views) and without requiring the objects to be loaded into volatile memory in a columnar fashion. The user query is rewritten to produce a transformed query that targets the dictionary tables to form the aggregate result without scanning the user tables. “Accuracy indicators” may be maintained to indicate whether those statistics are accurate. Only accurate statistics are used to answer queries that require accurate answers. The accuracy check can be made during runtime, allowing the query plan of the transformed query to be used regardless of the accuracy of the statistics. For queries that request approximations, inaccurate statistics may be used so long as the statistics are “accurate enough”.

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for executing and tuning a machine learning operation within a database. An example method can include receiving a data query referencing an input data set of a database, executing a plurality of machine learning operations to generate, in view of the input data set, a plurality of output data sets each having a respective accuracy value, wherein each of the plurality of machine learning operations is executed by a processing device according to one of a plurality of unique sets of hyperparameters, selecting a first output data set of the plurality of output data sets in view of the accuracy values, and returning the first output data set in response to the data query.

Abstract: According to various embodiments, the Query Context Translation Engine identifies a topic of a search query history received during a current user session. The search query history in a first language. The Query Context Translation Engine identifies, in a translation table, target text that corresponds with a query in the search query history, the target text comprising at least one word. The Query Context Translation Engine obtains at least one search result based on a translation of the target text in a second language.

Abstract: Operations include halting a runaway query in response to determining that a performance metric of the query exceeds a performance threshold. The runaway query halting system receives a query execution plan associated with a query and divides the received execution plan into one or more components. For each component, the system determines a predicted resource usage associated with executing the component. The system further determines a predicted resource usage associated with the query execution plan based on the predicted resource usage associated with each component. The system executes the query associated with the received query execution plan and compares the predicted resource usage associated with the query to a resource usage threshold. In response to determining that the predicted resource usage of the query execution plan exceeds the resource usage threshold, the system halts execution of the query associated with the query execution plan.