In some embodiments, systems and methods for visualizing one or more datasets include importing a plurality of root objects, each root object including linked data attributes and obtaining a joined dataset based on the plurality of root objects, that includes for each or the plurality of root objects, a plurality of rows of related attribute data linked to each root object as a result of a join operation. The systems and methods perform an aggregation computation on the plurality of rows of related attribute data corresponding to each of the plurality of root objects to produce a corresponding single aggregation row of consolidated data for each root object and present a user interface that shows each of the plurality of root objects with their corresponding single aggregation row of consolidated data resulting from the aggregation computation, in a one-to-one manner.

Systems and methods for rapid importation of data including temporally tracked object recognition. One of the methods includes receiving datasets each indicating information associated with one or more objects. Information indicating unique identifying information associated with the objects is accessed, and an updated dataset joining information from datasets that is associated with each object is generated. The updated dataset is maintained to include most recent versions of each of the datasets, with one or more datasets being replaced with more recent versions, and with one or more other datasets being propagated to be the most recent versions. Queries received from clients are responded to, with the queries indicating requests for specific information related to objects.

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.

Two streams of data items are received. A first estimated processing time for the first stream of data items and a second estimated processing time for the second stream of data items are determined. Data items of the first stream and data items of the second stream are dynamically maintained in a first buffer and a second buffer respectively. The data items of the second stream maintained in the second buffer have associated event times that are within a first join window based on the first estimated processing time for the first stream. A selected data item of the first stream maintained in the first buffer is joined with one or more data items of the second stream maintained in the second buffer that have associated event times that are within a second join window based on an event time associated with the selected data item of the first stream.

Disclosed herein are systems and methods for using machine learning for managing application incidents. An embodiment takes the form of a method that includes receiving extracted data pertaining to one or more applications, Model-input data is generated from the extracted data. Model-output data is generated at least in part by processing the generated model-input data with one or more machine-learning models trained to make one or more application-incident predictions. Based at least in part on the model-output data, an application-incident-likely determination is made that a likelihood of an occurrence of an application incident exceeds an application-incident-likelihood threshold, where the application incident corresponds to a given application of the one or more applications. Responsive to making the application-incident-likely determination, one or more alerts of the likelihood of the occurrence of the application incident are output.

Described approaches provide for effectively and scalably using multiple GPUs to build and probe hash tables and materialize results of probes. Random memory accesses by the GPUs to build and/or probe a hash table may be distributed across GPUs and executed concurrently using global location identifiers. A global location identifier may be computed from data of an entry and identify a global location for an insertion and/or probe using the entry. The global location identifier may be used by a GPU to determine whether to perform an insertion or probe using an entry and/or where the insertion or probe is to be performed. To coordinate GPUs in materializing results of probing a hash table a global offset to the global output buffer may be maintained in memory accessible to each of the GPUs or the GPUs may compute global offsets using an exclusive sum of the local output buffer sizes.

A method of joining a first dataset configured to store a set of data entries each identified by a respective key of a first type and a second dataset configured to store a second set of data entries identified by a respective key of a second type, the method comprising: selecting an intermediate mapping entity from a set of possible intermediate mapping entities, each mapping entity storing association between keys of the first type and keys of the second type; providing the selected intermediate mapping entity for use in joining the first data set with the second data set; wherein the step of selecting the intermediate mapping entity is based on the intersection weight between the first and second data sets via each of the intermediate mapping entities, wherein the intersection weight is the proportion of overlapping data entries between the first and second datasets.

A computer-implemented method includes representing a plurality of database tables as respective vectors in a multi-dimensional vector space, receiving an indication that a first database table represented by a first vector and a second database table represented by a second vector are related to each other, moving positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space in response to the indication, and grouping the plurality of database tables into one or more table clusters based on positions of the respective vectors representing the plurality of database tables in the multi-dimensional vector space.

A system for improving memory resource allocation efficiency for executing tasks receives a request to allocate a particular amount of memory resources to a particular database block of a database server. The system monitors the database blocks of the database server to determine whether any portion of memory resources already allocated to any of the database blocks is unutilized. If it is determined that a portion of the memory resources already allocated to any of the database blocks is unutilized, the system reallocates the unutilized memory resources to the particular database block.

A data pre-processing architecture may include an interface and a pruning logic configured to receive, via the interface, at least one filter value from a query processor; use the at least one filter value to scan rows or columns of a data table stored in a memory; generate a selection indicator identifying a set of rows or columns of the data table where the at least one filter value resides; and provide to the query processor a filtered output based on the selection indicator.