Abstract: Methods, systems, and apparatuses, including computer programs for generating and using a hash table configured to improve mapping of reads are disclosed that include obtaining a first seed of K nucleotides from a reference sequence, generating a seed extension tree having a nodes, wherein each node of the nodes corresponds to (i) an extended seed that is an extension of the first seed and has a nucleotide length of K* and (ii) one or more locations, in a seed extension table, that include data describing reference sequence locations that match the extended seed, and for each node: storing interval information at a location of the hash table that corresponds to an index key for the extended seed, wherein the interval information references one or more locations in the seed extension table that include reference sequence locations that match the extended seed associated with the node.

Abstract: Methods, systems, and devices supporting primitive-based query generation are described. A database system may receive a natural language query associated with a subset of data sets stored at the database system, determine a set of tokens included in the natural language query, and label one or more tokens of the set of tokens with one or more primitives from a set of supported primitives associated with the subset of data sets. The database system may determine a set of higher order concepts corresponding to query operations, query conditions, or both based on the one or more labeled tokens. The database system may train a neural network to learn a grammatical structure of the natural language query and generate a database query for querying the database system based on the one or more labeled tokens, the set of higher order concepts, and the neural network.

Abstract: The disclosure includes aspects of a system and/or method including a hash multi-table joining implementation method based on a grouping vector, which includes the following steps: 1) rewriting an SQL query command, and dividing a complete OLAP query command into a subtask of selecting-projecting-grouping-joining operation and an subtask of aggregating operation; 2) creating and generating grouping vector metadata corresponding to a GROUP-BY statement in an SQL command through the subtask of selecting-projecting-grouping-joining operation, and creating a vector index as an output result of the subtask of selecting-projecting-grouping-joining operation; 3) executing aggregation computation based on the vector index through the subtask of aggregating operation, and storing an aggregation computation result in a corresponding unit of a grouping vector aggregator with the same length as the grouping vector; and 4) merging the aggregation computation result in the grouping vector aggregator with the grouping vecto

Abstract: Methods for cascade elimination of candidates in spatial relation operations are performed by systems, devices, and apparatuses. Spatial relation operations include counts of geometric objects against geographies/geometries, and data points associated with vertices of shapes representing the geographies/geometries are reduced to form smaller sets of data points that represent the initial shapes. Elimination of geometric objects for operations is performed using inner- and outer-reductions in cascade. Data points are reduced until a representation of the original shape with a specified area or number of data points is generated. Multi-polygon geometries are joined by union based on preservation of area and data points of the polygons therein prior to reducing data points. Cascade reductions are also performed at specified times during spatial operations based on costs associated therewith.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a win prediction for an opportunity. In some implementations, a server receives data representing an opportunity. The server obtains historical data from a data repository based on the received data representing the opportunity. The server generates standardized feature vectors based on non-standardized data, the non-standardized data comprising (i) the received data and (ii) the obtained historical data. The server filters the standardized feature vectors. The server generates a win percentage based on the filtered feature vectors using classifiers. In response to generating the win percentage for the opportunity, the server generates a plurality of data insights using the classifiers and the generated win percentage, wherein the plurality of data insights describe one or more data predictions for pursuing the opportunity.

Abstract: Disclosed are various approaches for providing a natural language interface for searching databases. A natural language query is parsed to identify a plurality of tokens. At least one operator is identified with a machine-learning model based at least in part on the plurality of tokens. Next, at least one attribute and at least one respective attribute value are identified with a machine-learning model based at least in part on the plurality of tokens. Then, at least one constraint is identified with a machine-learning model based at least in part on the plurality of tokens. Finally, a machine language query is generated based at least in part on the at least one operator, the constraint(s), the attribute(s), and the respective attribute value(s).

Abstract: A method and apparatus that recommends a feature from a data set. The method includes performing a first conversion on the data set including one target variable and a plurality of feature variables by converting a missing value for each of the feature variables into a preset constant, executing a first algorithm to determine a level of redundancy of the plurality of feature variables, using the data set converted by the first conversion and producing first recommendation information that includes a predetermined number of feature variables selected based on a result of the executing the first algorithm.

Abstract: A computer-implemented method according to one embodiment includes compiling log data from all nodes of a cluster, retrieving a timestamp table stored within the cluster, optimizing the timestamp table, and adjusting the log data from all the nodes of a cluster, utilizing the timestamp table.

Abstract: Systems and techniques are generally described for multi-stage text-based clustering. In various examples, a first data set comprising a first set of elements may be received. Each element of the first set of elements may include text data. A clustering algorithm may be used to determine a first set of clusters of the first set of elements based at least in part on similarities in the text data. A second set of clusters, for each cluster of the first set of clusters, may be determined using the clustering algorithm. A number of resources for labeling the first set of elements may be determined based at least in part on the first set of clusters. The number of resources may be assigned to label clusters of the second set of clusters based at least in part on a number of elements in each cluster of the second set of clusters.

Abstract: A method for execution by a query processing system includes receiving a query expression that includes a call to a computing window function. The computing window function is executed in accordance with execution of the query expression against a database. Execution of the query expression includes accessing an ordered set of rows of the database indicated in the call to the computing window function, and applying a recursive definition indicated in the call to the computing window function to each row in the ordered set of rows to generate output for each row in the ordered set of rows. A query resultant for the query expression is generated based on the output for each row in the ordered set of rows.

Abstract: Methods and systems for data management are described, particularly for processing global queries. Each global query includes a user-defined query constraint value, such as laxity or query response time limit. The query receiving node maintains a copy of the previously updated data from all of its children node. The query receiving node first searches for the requested query data in its local data storage to minimize children node query. If any portion of the requested data in the local data storage fails to meet the query constraint value, then the child node from which the data came from is tasked with recursively executing the global query.

Abstract: In one example, a method involves performing an initial discovery process that includes querying a storage array, and identifying, based on the query, one or more hosts that are registered with the storage array. This initial discovery process is performed automatically without requiring user action to identify the one or more hosts. The method additionally includes presenting a list of discovered hosts, receiving a selection input from a user specifying one or more of the hosts in the list, retrieving, from the storage array, information associated with each of the respective hosts, and making the information available to a user.

Abstract: Techniques for implementing a multi-wire protocol and multi-dialect database engine are described. A database engine exposes multiple interfaces in the form of ports that support different database wire protocols. The database engine supports multiple query dialects that can be passed over any one of the supported wire protocols. The database engine can support multiple different query dialects within a single database session.

Abstract: Provided are methods and systems for traversing a data model with a plurality of workers to generate a plurality of virtual records for use in evaluating an expression by a plurality of aggregators. Each aggregator may receive a respective virtual record and evaluate a hypercube function and determine a respective hash based on the respective virtual record. The virtual records based on the respective hash values of the respective virtual records may be stored by the plurality of aggregators.

Abstract: Protecting consumer data is a key responsibility of an organization. The method of protection need to compare the data with policy documents. Ontology and a threshold to select an optimal match plays a key role in such comparison. The conventional automatic threshold computation methods are complex and not based on semantic similarity. The present disclosure generates an Entity Relationship (ER) model from an input document and is converted into a first ontology. The first ontology and a second ontology obtained from a relational database are compared by an ontology matching algorithm. Further, the plurality of many to many correspondences are optimized to one to one correspondence by an optimization method. Further, a plurality of optimal one to one correspondence is generated based on a threshold. The threshold is computed based on symmetric and transitive property. Further, semantically similar entities are selected based on the optimal one to one correspondence.

Abstract: A question answering (QA) system comprising memory for storing instructions, and a processor configured to execute the instructions to ingest source documents that include structured data and unstructured data to create a knowledge base, wherein the unstructured data includes table data; create table annotations to represent the table data; store the ingested structured data, unstructured data, and the table annotations in the knowledge base; and determine answers to questions using the knowledge base.

Abstract: A file storage application that processes file operations is communicably connected with a block storage application that processes block operations by establishing multiple communication sessions between the file storage application and the block storage application. Multiple logical volumes provided by the block storage application are exposed to the file storage application over the multiple communication sessions established between the file storage application and the block storage application using a total number of logical paths to the logical volumes that is equivalent to the total number of the logical volumes provided by the block storage application to the file storage application.

Abstract: Provided are a feedback method and apparatus based on an online document comment, and a non-transitory computer-readable storage medium. The feedback method based on an online document comment includes receiving comment push information sent by a server, where the comment push information is generated by the server according to a comment feedback addition request sent by a terminal and includes a document identifier, a comment identifier, and a comment feedback type; determining a first target document matching the document identifier and determining a first comment matching the comment identifier in the first target document; and acquiring comment feedback matching the comment feedback type and adding the comment feedback in a comment feedback display region matching the first comment.

Abstract: In a computer-implemented method for querying a variably partitioned time series database, a query of a time series database is received, the query including a time range and a predicate comprising at least one dimension, wherein the time series database comprises a plurality of time series database schemas. At least one time series database schema of the time series database corresponding to the time range is determined. The query is divided into a plurality of sub-queries, wherein each sub-query of the plurality of sub-queries corresponds to one time series database schema of the plurality of time series database schemas. The plurality of sub-queries is executed to return a plurality of results.

Abstract: Techniques and solutions are described for copying data from a source client to a target client. It may be selected, such as by a user, whether a full copy or a partial copy of a set of source client data should be made. If a partial copy is selected, a set of file types is provided. The file types can be used to define files that should be included in, or excluded from, the copy. A data source of the source client is analyzed to determine data associated with a file type that is to be included in a copy, or is associated with a file type that is not indicated as to be excluded from the copy. The determined data is copied from the source client to the target client.