Abstract: Examples disclosed herein relate to distributed computing in R. Some examples disclosed herein may include identifying a distributed multivariate apply (dmapply) operation and an invocation of a distributed computing backend and determining a function referenced in the dmapply operation. A distributed backend driver associated with the invoked distributed computing backend may translate the determined function to a function native to an R application programming interface (API) of the invoked distributed computing backend and may provide the translated function to the invoked distributed computing backend to perform the translated function on a distributed data set referenced in the dmapply operation.

Abstract: 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.

Abstract: 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.

Abstract: Page feed for efficient dataflow between distributed query engines is disclosed. An example method comprises storing a query result relation by a producer query as pages in a query engine buffer pool, and then sending these pages to a distributed caching platform (DCP) as the data source of another query. The example method also includes reading the result relation by a consumer query from the page stored in the DCP.

Abstract: An event occurring in a particular geographic region is identified based on disseminated information containing public commentary in the particular geographic region. Attributes that are related to the event are identified, and sentiment words relating to the identified event are extracted from the disseminated information, where the extracted sentiment words are in a local language of the particular geographic region. A sentiment trend visualization is generated that depicts a trend of sentiments of at least a particular one of the identified attributes, wherein the sentiments are based on the sentiment words for at least the particular attribute.

Abstract: Using a contingency calculation based on a number of events sharing a collection of values of plural attributes, a discriminative metric is computed representing a statistical significance of the events that share the collection of values of the plural attributes. A visualization is generated that includes cells representing respective events, the visualization including a region containing a subset of the cells corresponding to the collection of values of the plural attributes, and the visualization including a significance visual indicator associated with the region to indicate the statistical significance of the events sharing the collection of values of the plural attributes.

Abstract: A method for combining data values through associative operations. The method includes, with a processor, arranging any number of data values into a plurality of columns according to natural parallelism of the associative operations and reading each column to a register of an individual processor. The processors are directed to combine the data values in the columns in parallel using a first associative operation. The results of the first associative operation for each column are stored in a register of each processor.

Abstract: Multiple tuples are combined (202) by a processing node into a message that has a key portion including a value of a key, and a payload including a relation containing content of the combined plurality of tuple. The message is sent (204) to a receiving node.

Abstract: A technique includes in a relational database query engine, receiving a query associated with a relational data structure. The received query includes a database graph query. The technique includes using the relational database query engine to integrate a result acquired from the graph database engine into a result provided by the relational database query engine to the received query.

Abstract: 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.

Abstract: Systems and methods of block computation are disclosed. An example of a method includes buffering a plurality of tuples in a build phase. The method also includes, after buffering a last of the plurality of tuples, applying a computation to all of the buffered tuples in a compute phase. The method also includes returning results of the computation one tuple at a time to a query processing pipeline in a stream-out phase.

Abstract: Example embodiments relate to providing visual analytics of spatial time series data. In example embodiments, sensors may be located at regions within a building for collecting sensor data at regular time intervals. A sensor hierarchy can be generated including sensor nodes that are hierarchically arranged according to a physical infrastructure of the building, where each of the sensor nodes corresponds to a sensor. Sensor data can be obtained from the sensors, and a pixel calendar tree can be generated based on the sensor data and the sensor hierarchy, where the pixel calendar tree is recursively subdivided into tree portions according to a proportion of the sensor data attributable to each of the sensors. The pixel calendar tree can be displayed, where each of the tree portions includes time series sensor data of a corresponding region that is generated based on the sensor data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to manage a cache. An example method includes in response to receiving a request to retrieve received data, retrieving first data from a cache, the first data received during a first time period, and retrieving, second data from a storage device, the second data received during a second time period prior to the first time period; and providing the first data and second data as combined data the, combined data being combined based on the first time period and the second period.

Abstract: 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.

Abstract: Selected topics are identified from records based on scoring candidate terms in the records according to a user-specified metric and at least one further metric selected from among frequencies of occurrence of records pertaining to the respective candidate terms, and negativity of sentiment expressed with respect to the candidate terms in the records. A visualization is generated that includes bubbles representing the respective topics, the bubbles including pixels representing corresponding records, where a given one of the bubbles has a shape dependent upon a number of records represented by the given bubble and a time interval represented by the given bubble. Visual indicators are assigned to the pixels in the given bubble according to values of an attribute expressed in the corresponding records for the topic represented by the given bubble.

Abstract: Customizing functions performed by data flow operators when processing data streams. An open-executor(s) is provided as part of the data stream analytics platform, wherein such open-executor allows for both of: 1) customizing user plug-ins for the operators, to accommodate changes in user requirements; and 2) predefining templates that are based on specific meta-properties of various operators and that are common therebetween.

Abstract: 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.

Abstract: According to an example, fluctuations of a metric for events are determined. The fluctuations are for multiple time intervals. A pixel-based visualization of the fluctuations is generated, and the pixels represent amounts of the fluctuations.

Abstract: A data stream is stored in a database. An event pipe stores a sliding window of events from the data stream. A query is responded to by utilizing the sliding window of events stored in the event pipe.

Abstract: A system includes a distributed file system to control storage of data across storage nodes and a database query engine to receive a database query for access of data, the database query engine to process the database query using an index, and using a buffer pool to cache data retrieved in response to the database query and to store updated data. An abstraction layer is provided between the database query engine and the distributed file system, the abstraction layer to read and write data of the distributed file system in response to the database query.