Hewlett-packard Development Company, L.p.

Micro Focus Lcc

Hewlett Packard Enterprise Development LP

Entit Software LLC

Maria G Castellanos

Meichun Hsu

Umeshwar Dayal

Ming-Chien Shan

Fabio Casati

Qiming Chen

Riddhiman Ghosh

Rui Liu

Mehmet Oguz Sayal

James Richard Stinger

Chetan Kumar Gupta

Bin Zhang

William Kevin Wilkinson

Alkiviadis Simitsis

Hyun Duk Kim

Matthias J Sax

Jeffrey LeFevre

Sharad Singhal

Harumi Kuno

Song Wang

Mohamed E Dekhil

Michael J Lemon

Sergey Serebryakov

Yue Lu

Stefan Krompass

Eric Y Shan

Medhabi Ray

ChenXiang Zhai

Perla Ruiz

Yifan Li

Miguel Durazo

Ivo Jose Jimenez Ramos

Ivo Jimenez

Examples disclosed herein relate to accessing electronic databases. Some examples disclosed herein may include partitioning a computation task into subtasks. A processing node of a computation engine may generate a database query for retrieving an electronic data segment associated with at least one of the subtasks from a database. The database query may include pre-processing instructions for a database management system (DBMS) associated with the database to pre-process the electronic data segment before providing the electronic data segment to the processing node. The pre-processing instructions may include at least one of: filtering, projection, join, aggregation, count, and user-defined instructions. The generated query may be provided to the DBMS.

Accessing electronic databases

Example embodiments relate to parallelizing structured query language (SQL) user defined transformation functions. In example embodiments, a subquery of a query is received from a query engine, where each of the subqueries is associated with a distinct magic number in a magic table. A user defined transformation function that includes local, role-based functionality may then be executed, where the magic number triggers parallel execution of the user defined transformation function. At this stage, the results of the user defined transformation function are sent to the query engine, where the query engine unions the results with other results that are obtained from the other database nodes.

Parallelizing SQL user defined transformation functions

Optimizing window joins over data streams can include receiving an input topology and calculating costs of computing a join of data streams based on a number of model topologies, and determining an optimal topology based on the calculated costs and the input topology, wherein the input topology, the model topologies, and the optimal topology each include a number of interconnected operators.

Optimizing window joins over data streams based on model topologies

Methods, devices, and techniques for base user defined functions in a database management system are discussed herein. For example, in one aspect, a query request is received from a computer device. The query request may include a query operator representing a specialized user defined function (SUDF). The SUDF may then be executed. Executing the SUDF may include executing a base operation of a base user defined function (BUDF). The base operation may interact with an application programming interface (API) of the query engine to obtain a tuple stored in the database. Executing the SUDF may further include executing a specialized operation that processes the tuple according to an analytics function. The specialized operation may generate a result. Then, a query result may be returned to the computer device. The query result can include the result.

Base user defined functions

Example embodiments relate to parallelizing structured query language (SQL) on distributed file systems. In example embodiments, a subquery of a distributed file system is received from a query engine, where the subquery is one of multiple subqueries that are scheduled to execute on a cluster of server nodes. At this stage, a user defined function that comprises local, role-based functionality is executed, where the partitioned magic table triggers parallel execution of the user defined function. The execution of the UDF determines a sequence number based on a quantity of the cluster of server nodes and retrieve nonconsecutive chunks from a file of the distributed file system, where each of the nonconsecutive chunks is offset by the sequence number.

Parallelizing SQL on distributed file systems

Examples disclosed herein relate to parallel transfers of electronic data. Some examples disclosed herein may include executing, by a processing node of a computation engine, a computation task among a plurality of computation tasks generated by the computation engine for transferring an electronic data table to a target table. The computation task, when executed by the processing node, may cause the processing node to transfer a segment of the electronic data table to a staging table, update a task status table upon completing the transfer of the segment to the staging table, in response to determining that the plurality of computation tasks have completed, update a last committer table with a task identifier associated with the computation task, and in response to determining that the last committer table includes the task identifier associated with the computation task, transfer the staging table to the target table.

Parallel transfers of electronic data

An example technique for defining a user function can include receiving a user defined function that gathers data from a data source external to a database and returns the data to the database. The example technique for defining the user function can also include creating an instance of a programming class that extends from an application programming interface (API). The instance of the programming class including the user defined function. The example technique for defining the user function can also include executing the user defined function provided by the instance of the programming class.

User defined function, class creation for external data source access

A technique of batching tuples can include determining a plurality of key-attributes for a plurality of tuples, creating a batch tuple, and calculating a hash value for the batch tuple.

Batching tuples

A method of capturing intentions within online text comprises with a data mining device (), identifying (block) a number of statements of intention within an online forum (), and with the data mining device (), extracting (block) a number of attributes () from the statements of intention. A system () for extracting intentions expressed within an online forum comprises a data mining device (), a forum server () comprising a number of online forums () communicatively coupled to the data mining device (), in which the data mining device identifies a number of statements of intention within the online forums () and extracts a number of attributes () from the statements of intention.

Capturing intentions within online text

Causal topic mining can include incorporating non-text time series data with a number of articles based on a time relationship and analyzing the incorporated non-text time series data and the number of articles at a particular time to determine a causal relationship.

Causal topic miner

An example system includes an extraction module, an intention processing module, and an intention visualization module. The extraction module is configured to ingest textual data from a text source. The intention processing module is configured to process the textual data and identify one or more intention statements within the textual data. The intention visualization module is configured to provide an interactive interface that facilitates filtering and visualization of aspects of the one or more intention statements.

Intention statement visualization

A method for generating summaries of text is described. The method includes the step of extracting features from text of text lists from summaries. The explanatoriness of the text is then evaluated, wherein evaluating the explanatoriness of text includes evaluating the features of the text, including at least the step of evaluating the discriminativeness of the features of the text by comparing the text to a first text data set, wherein the first text data set is derived from a topic label. The evaluated text is then ranked based on the explanatoriness evaluation.

Generation of explanatory summaries

According to an example, a dataflow directed acyclic graph including nodes and edges connecting the nodes. Parameters for the nodes are calculated, and the parameters may include batch size and a degree of parallelism.

Streaming system performance optimization

An example system includes at least one memory and at least one processing device. The at least one memory comprises instructions that when executed by the at least one processing device cause the system to receive a text corpus, filter the text corpus to obtain a plurality of attributes, determine the semantic cohesion between the plurality of attributes, and create an attribute cloud including the plurality of attributes, where the placement of the plurality of attributes within the attribute cloud is based at least in part on the determined semantic cohesion between the plurality of attributes.

Attribute cloud

A system and article are disclosed for objective-function based sentiment. In one example, the system includes a set of domain information, and a computer programmed with executable instructions which operate a set of modules. The modules include a sentiment polarization module for identifying a domain-aspect opinion-word pair within a set of domain data, and assigning a sentiment polarity score to the domain-aspect opinion-word pair based on an objective function which includes sentiment data from the domain information.

Growing community of inventors

Palo Alto, CA, United States of America

Maria G Castellanos