Abstract: A stream computing method applicable to a system including a manager node and a worker node, where the manager node, obtains input channel description information, a structured query language (SQL) statement, and output channel description information, dynamically generates a data stream graph according to the input channel description information, the SQL statement, and the output channel description information, and controlling, according to the data stream graph, a worker node to execute a stream computing task.

Abstract: An approach is provided in which an information handling system identifies candidate answers from a knowledge base that correspond to a question comprising a lexical answer type (LAT). The information handling system determines sub-LATs corresponding to the LAT in a database that are child LATs of the LAT. The information handling system then identifies a set of the candidate answers that correspond to at least one of the sub-LATs and ranks the set of candidate answers based on one or more ranking criterion. In turn, the information handling system provides the ranked set of candidate answers to a user.

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: Methods, apparatus, and processor-readable storage media for automatically purging data using a deep neural network are provided herein. An example computer-implemented method includes training a neural network model using multiple types of attribute data and job-related data associated with historical workflow data maintained within multiple data structures of an enterprise system; dynamically analyzing workflows derived from the enterprise system, wherein dynamically analyzing the workflows comprises determining the multiple types of attribute data for the workflows and processing job-related data associated with the workflows into multiple data structures within one or more databases; applying the neural network model to the determined attribute data; and removing at least a portion of the multiple data structures from the one or more databases based at least in part on the application of the neural network model to the determined attribute data.

Abstract: In one embodiment, a method includes, by one or more computing systems, receiving, from a client system associated with a user, an initial portion of a user input, wherein the initial portion comprises a partial request, and wherein the initial portion is received while the user is continuing to provide further input, generating, responsive to receiving the initial portion of the user input, one or more speculative queries based on the partial request and a machine-learning predictive model, wherein each speculative query is a predicted complete request based on the partial request, calculating a confidence score for each speculative query based on the predictive model, ranking the one or more speculative queries based on their respective confidence scores and associated costs, executing one or more of the speculative queries based on their ranks, and caching one or more results of the executed one or more speculative queries.

Abstract: In pattern discovery visual analytics, a patient data table (14) is generated that tabulates, for each patient, attribute values for a set of attributes. A positive or negative prediction is generated for each patient for a target value of a target attribute using a prediction pattern (20) of attribute values for w attributes (22). The prediction is positive if at least a threshold fraction (26) of the w attributes of the patient match the prediction pattern, is negative otherwise. Patients are grouped into a selected proportion of a confusion matrix (30) in accord with the positive or negative predictions and actual values of the target attribute T in the patient data table. A display component (4) displays a representation (42) of patient statistics for the selected proportion of the confusion matrix on a per-attribute basis for attributes of the w attributes. A patient cohort (44) is identified using the representation.

Abstract: A computer-implemented method and system at a network switch provides using one or more processors to perform a pre-defined database function on query data contained in data messages received at the network switch, with the performing producing result data, and wherein the pre-defined database function is performed on the query data in a first mode of operation to a state of full completion, generating complete result data and no skipped query data, and in a second mode of operation to a state of partial completion, generating partially complete result data and skipped query data. Further, the method and system performing one or more network switch functions to route the complete result data, and/or route the partially complete result data and skipped query data, to one or more destination nodes. In addition, an application programming interface (API) is used to define the database function.

Abstract: A computer-implemented method, computer program product and system for data sampling in a storage system. The storage system includes a dataset comprising records and a buffer. The dataset is scanned record-by-record to determine whether the current record belongs to a random sample. If so, then the current record may be added to a first set of records. Otherwise, at least one storage score may be calculated or determined for the current record using attribute values of the current record. Next, it may be determined whether the buffer includes available size for storing the current record. In case the buffer comprises the available size, the current record may be stored in the buffer. Otherwise, at least part of the buffer may be free up. A subsample of the dataset may be provided as a result of merging the first set of records and at least part of the buffered records.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enforcing a mapping between entities in a database and aggregates in an application framework. One of the methods includes receiving a request to populate a program object corresponding to an entity in a first relation of a relational database, the relational database is queried to obtain values for one or more attributes of the entity in the first relation and a plurality of identifiers that each identify a respective entity in the second relation of the relational database. The program object is populated with an aggregate having a complete collection of identifiers representing an m-to-n relationship in the database.

Abstract: A method of viral escape profiling is used in association with antiviral or vaccine development. The method begins by training a language-based model against training data comprising a corpus of viral protein sequences of a given viral protein to model a viral escape profile. The viral escape profile represents, for one or more regions of the given viral protein, a relative viral escape potential of a mutation, the relative viral escape potential being derived as a function that combines both “semantic change,” representing a degree to which the mutation is recognized by the human immune system (i.e., antigenic change), and “grammaticality,” representing a degree to which the mutation affects viral infectivity (i.e. viral fitness). Using the model, a region of the given viral protein having an escape potential of interest is identified. Information regarding the region is then output to a vaccine or anti-viral therapeutic design and development workflow.

Abstract: An information processing method executed by an information processing system that executes processing for searching data to be searched by using a query condition that can be entered via a predetermined user interface and in which individual conditions constituting a query satisfy three properties consisting of reductiveness, idempotence, and commutativity, includes a pre-processing executing step of executing processing prior to processing for searching, and a search processing step of executing processing for searching. The pre-processing executing step includes a query extracting step of extracting a plurality of limited kinds of sub-queries constituting minimum units of queries and satisfying the three properties consisting of the reductiveness, the idempotence, and the commutativity by analyzing a set of statically defined kinds of queries, and a query executing step of executing each of the plurality of kinds of sub-queries extracted in the query extracting step.

Abstract: Comments are flexibly bound to keyfigures of an in-memory database, through reference to dimension dependency table(s). An in-memory database engine creates a comment bound to a first tuple via a first comment table. The first tuple comprises a first dimension (e.g., Product). A user then requests a second tuple comprising a second dimension (e.g., Product Group) related to the first dimension. In response, the in-memory database engine references a dimension dependency table to determine dimension dependency information. Based upon that information, the engine transforms the comment to also be bound to the second tuple via a second comment table. The second tuple is then returned to the user together with the comment, even though the second tuple may not explicitly share dimensions with the first tuple. Embodiments may leverage the processing power and data proximity of the in-memory database engine, to efficiently transform comments by executing aggregation (e.g., join) operations.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that involve a user profile based on clustering tiered descriptors. An example method includes grouping descriptors into a cluster of descriptors based on an association between the descriptors and each of a first item and a second item, accessing, via a user device, biometric data of a user, determining a first activity in which the user is engaged based on contextual data that correlates the cluster of descriptors with the biometric data of the user received from the user device via the network, generating a user profile based on the first activity of the user and the cluster of descriptors, and generating, in response to a second activity of the user matching the first activity associated with the cluster of descriptors within the user profile, a recommendation including a third item based on the user profile.

Abstract: Embodiments are directed to a self-cleaning token vault for managing tokens generated on behalf of a resource provider. The generated tokens may be stored at the token vault. Embodiments provide a token manager (which can be embodied in the token vault or provided as a separate module) that continuously monitor the token vault for expired or redundant tokens. The token manager may enable the resource provider to assign level flags to the tokens and assign level values to the level flags. The level value may indicate an importance or a use characteristic of the token. Upon identifying tokens that are no longer necessary, the self-cleaning token vault or the token manager may automatically remove the tokens from the token vault.

Abstract: In an example embodiment, definition information is received that includes a definition for each of one or more programmable conditions applicable to an operation. The definition information is translated into one or more objects representing the one or more programmable conditions. The objects include executable code for determining compliance of the operation with the programmable conditions. For each object, a lookup key is generated for the object based on the definition information. A database is checked to determine whether a prior version of the object is stored in the database using the generated lookup key. If stored in the database, the prior version of the object in the database is updated using the object. Otherwise, the object is stored in the database using the generated lookup key.

Abstract: According to one embodiment, a first image identifier (ID) is received identifying a first of a first set of images, where the first image is currently displayed at a mobile device of a user. A number of additional images the user likely accesses from the first image in sequence is determined based on user interactions of a current browsing session and user interactions of a prior browsing session associated with the user. A second set of images are identified based on the number of additional images the user likely accesses. A sequential order is determined based on rankings of the images in the second set in view of a set of image selection rules. The second set of images is transmitted to the mobile device to be displayed on the mobile device in sequence one at a time.

Abstract: An area monitoring system and a method of merging occurrence records in an area monitoring system whereby occurrences are monitored or recorded in external databases. The method includes receiving a first occurrence record, indicative of an event; on the basis of the applicable correlation rule, identifying a set of databases to be queried; generating a query for data according to a respective protocol; generating a correlated dataset of occurrence records from at least a subset of the set of occurrence records and the first occurrence record; and displaying to a user a visual representation of the correlated dataset of occurrence records.

Abstract: Indexing objects in a datastore, where the objects are characterized by a set of object properties. For objects from a datastore: a Bloom filter for the object properties, based on a predetermined Bloom filter configuration, is created; the Hamming value of the Bloom filter is determined; a binary logarithm of the Bloom filter is calculated; and the Bloom filter, the Hamming value, the binary logarithm, and a representation of the object is stored in the datastore.

Abstract: A distributed file system for a vehicle fire system leverages unused memory in slave modules. Such a system comprises file metadata stored on the master module, which includes information about specific files and the location of used and unused memory throughout the system, including memory on the master module and installed slave units. By maintaining the file metadata and sending instructions to slave units to save or read files at certain addresses, the master module can avail itself of unused memory throughout the entire system. This allows the master module to store more information on the system than there is available memory on the master itself.

Abstract: Example embodiments involve a system and methods for identifying valuable view item pages for search engine optimization. According to certain embodiments, the system performs operations that include predicting the probability of future traffic for a given product based on a number of product level factors as input variables, and identifying a selection of view item pages corresponding to the products with the probability of the highest future traffic in order to maximize the driving natural search traffic to a linked site of the corresponding view item page.