Abstract: In some examples, a database system receives data relating to plural micro-models that apply respective analytics, and distributes a plurality of data segments of the received data across the plurality of processing engines based on values of a segmentation key included in the received data. A plurality of processing engines, performs in parallel, operations associated with the plural micro-models using respective data segments of the plurality of data segments, where different processing engines of the plurality of processing engines perform operations associated with respective micro-models of the plural micro-models.

Abstract: In some examples, a database system receives data relating to plural micro-models that apply respective analytics, and distributes a plurality of data segments of the received data across the plurality of processing engines based on values of a segmentation key included in the received data. A plurality of processing engines, performs in parallel, operations associated with the plural micro-models using respective data segments of the plurality of data segments, where different processing engines of the plurality of processing engines perform operations associated with respective micro-models of the plural micro-models.

Abstract: 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.

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: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A technique for resending messages can include determining if a particular message with a first unique identifier has been received by a recipient task among a number of potential recipient tasks in a distributed streaming system, notifying the number of potential recipient tasks to ignore the particular message with the first unique identifier, and resending the particular message with a second unique identifier.

Abstract: A technique for recovering tuples can include sending or receiving a request to reseed a tuple.

Abstract: A technique includes determining social media expressed sentiment, including processing data indicative of a plurality of social media messages to decompose each of the social media messages into a plurality of attributes; for each social media message, identifying attributes of the plurality of attributes, which are associated with user selected attribute categories and are part of the message, and for each identified attribute, assigning a sentiment to the attribute and updating statistics for the selected attribute categories based on the assigned sentiment. The technique includes visualizing the social media expressed sentiment, including displaying at least some of the statistics.

Abstract: A technique for resending messages can include determining if a particular message with a first unique identifier has been received by a recipient task among a number of potential recipient tasks in a distributed streaming system, notifying the number of potential recipient tasks to ignore the particular message with the first unique identifier, and resending the particular message with a second unique identifier.

Abstract: 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.

Abstract: The disclosed embodiments relate to a system and a method for process discovery. Embodiments of the present invention comprise extracting process data from a data source, creating a trace using the extracted process data, wherein the trace comprises a plurality of events, and detecting a process model using the plurality of events.

Abstract: A system, and a corresponding method, that allow a programmer to create and edit a data-flow employing multiple execution engines are provided. The system includes a data-flow editor and a data-flow translator. The method includes providing an illustration of the data-flow and metadata associated with the data-flow on a graphical user interface; representing the data-flow and the metadata by a first code language; dividing the data-flow illustrated on the graphical user interface into fragments; and translating the first code language into the execution code language of the execution engine corresponding to each of the fragments.

Abstract: Embodiments of the present invention relate to a system and method for process discovery. Specifically, embodiments of the present invention relate to defining a trace using process data, the trace comprising a collection of events, analyzing the trace to detect nodes and arcs between the nodes using relationships between the collection of events, and detecting interleaving in the arcs by analyzing relationships between the collection of events and the detected nodes and arcs.

Abstract: A method of process-driven analysis of operations includes defining an abstract process, defining at least one metric over the abstract process using a metric definer and computing metric values using a metric computation engine. The method further includes building an analysis model and a prediction model using an analysis and prediction engine to provide analysis on the computed metric values and optimizing the abstract process based on the computed metric values.

Abstract: Embodiments in accordance with the present invention include methods and systems for data prediction.

Abstract: The present disclosure relates to a system and method for analyzing a process. Embodiments of the present invention may include obtaining a process execution trace including at least one subtrace, the process execution trace comprising at least one loop repetition. Further, embodiments may include removing loop repetitions within the process execution trace, identifying the subtrace as a point for data collection in a process, and collecting data related to the subtrace.

Abstract: Various approaches are disclosed for loading data from a vertical database table into a horizontal database table. In one embodiment, a number of rows in a vertical database table and a number of columns in a horizontal database table are determined. Entries in a first column of the vertical table contain object identifiers, entries in a second column contain attribute names corresponding to the object identifiers, and entries in a third column contain attribute values corresponding to the attribute names. The horizontal table includes a column for the object identifiers and respective attribute columns for the attributes names. One of a plurality of methods is selected and used for reading data from the vertical database table and writing data to the horizontal database table based in part on the number of rows in the vertical database table and the number of columns in the horizontal database table.