Abstract: Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.

Abstract: Described are examples for managing event data from multiple event data sources including executing, by a processor, a query for event data in a discoverable event stream over a time window that includes a delay tolerance window specific to the query, wherein the event data is stored as timeseries data in the discoverable event stream such that at least one property corresponding to at least one object has multiple different values at multiple different time instances, and wherein the delay tolerance window is configured to capture potentially out-of-order event data or late arriving event data, and returning a subset of multiple different values for the at least one property in instances of the event data corresponding to the time window with the delay tolerance window applied for the query.

Abstract: Described are examples for managing event data from multiple event data sources including executing, by a processor, a query for event data in a discoverable event stream, wherein the event data is stored as timeseries data in the discoverable event stream such that a property corresponding to an object has multiple different values at multiple different time instances, determining, based on a timeseries function, a subset of the multiple different values for the property in instances of the event data that correspond to the multiple different time instances to return for the query, and returning the subset of the multiple different values for the property for the query.

Abstract: Described are examples for displaying event data from multiple event data sources including obtaining, by a processor based on a query over a time window of event data in an discoverable event stream, instances of the event data related to an object and based on a value of a property indicated in the instances of the event data, and displaying, via a user interface, multiple stacked visual representations related to the instances of the event data, wherein at least one of the multiple stacked visual representations of the event data is derived from at least another one of the multiple stacked visual representations of the event data using a time-series function.

Abstract: Aspects of the present disclosure relate to data management of event representations within a discoverable event stream. These techniques may include determining historical query information based upon historical execution of a query over an event stream, and identifying a storage attribute of data associated with the historical execution of the query over the event stream, the storage attribute indicating at least one of a size of the data or an assigned storage location of the data in a first storage location or a second storage location. In addition, the techniques may include identifying an execution attribute of a future execution the query over the event stream, and modifying the assigned storage location of the data based on the historical query information, the storage attribute, and the execution attribute.

Abstract: Aspects of the present disclosure relate to monitoring for an activation result within a discoverable event stream. These techniques may include generating, based on an activation trigger event, by an activation component, a first activation instruction event to trigger a first action at a first actor, and transmitting the first activation instruction event to the first actor. In addition, the techniques may include monitoring for receipt of an actor status event of a discoverable event stream, the actor status event indicating performance or non-performance of the first action by the first actor in response to the first activation instruction event, and generating, based on the monitoring, by the activation component, a second activation instruction event to trigger a second action at a second actor. Further, the techniques may include transmitting the second activation instruction event to the second actor.

Abstract: Aspects of the present disclosure relate to view management in a discoverable event streams. These techniques may include receiving a first event of a discoverable event stream, the first event including a plurality of event data fields representing an occurrence of the first event, and identifying a discoverable event stream view associated with an event data field of the plurality of event data field. The techniques may further include generating, based on application of a modification to or computation on the event data field, a second event of the discoverable event stream view.

Abstract: Aspects of the present disclosure relate to ingesting event data into a discoverable event stream using a common schema to assign timeseries properties to the event data. These techniques may include receiving a first event of a pre-processed event stream, the first event including a plurality of event data fields representing an occurrence of the first event, and determining timing information based on the plurality of event data fields representing the occurrence of the first event. In addition, the techniques may include generating, based on combining the plurality of event data fields and the timing information, a second event of a discoverable event stream corresponding to the pre-processed event stream, and generating, within a time series of the discoverable event stream, based on the timing information and an event field of the plurality of event data fields, a time series entry corresponding to the second event.

Abstract: Aspects of the present disclosure relate to detecting occurrence of triggers related to properties of objects indicated in event data in a discoverable event stream. These techniques may include identifying a namespace associated with an activation trigger, and identifying a plurality of time-series based objects of a discoverable event stream associated with the namespace. The techniques may further include determining that an activation rule is met over the plurality of time-series based objects. Additionally, the techniques may include transmitting an activation trigger event to an activation component, the activation trigger event including a generic activation identifier identifying an action to perform, and an actor identifier identifying an actor to perform the action.

Abstract: Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.

Abstract: Shuffling of into partitions by first grouping input vertices of a limited number. Each group of input vertices may then be simply shuffled into a corresponding group of intermediate vertices, such as by broadcasting. A second grouping occurs in which the intermediate vertices are grouped by partition. The intermediate vertices then shuffle into corresponding output vertices for the respective partitions of that group. If the intermediate vertices are still too large, then this shuffling may involve recursively performing the shuffling just described, until ultimately the number of intermediate vertices shuffling into the output vertices is likewise limited. Thus, the final shuffling into the output vertices might also be simply performed by broadcasting.

Abstract: Shuffling of into partitions by first grouping input vertices of a limited number. Each group of input vertices may then be simply shuffled into a corresponding group of intermediate vertices, such as by broadcasting. A second grouping occurs in which the intermediate vertices are grouped by partition. The intermediate vertices then shuffle into corresponding output vertices for the respective partitions of that group. If the intermediate vertices are still too large, then this shuffling may involve recursively performing the shuffling just described, until ultimately the number of intermediate vertices shuffling into the output vertices is likewise limited. Thus, the final shuffling into the output vertices might also be simply performed by broadcasting.

Abstract: Detection and use of relationships in tabular data is enhanced. A foreign-key-based relationship between two tables in memory is automatically discovered by a module. A view over table data is computationally augmented by adding an expandable hierarchical column. The expandable hierarchical column includes second table data that is related to rows of a first table by the discovered foreign-key-based relationship. A naming scheme for the added column is described. Sometimes the augmentation places an AddJoinColumn operator in a query expression tree; sometimes the tree is rewritten by moving a query operator or replacing a specified pattern of operators by a flat Join operation between two tables. Column expansion may be followed by reshaping data into a flat table, by getting a homogeneous query, by performing a join, or other operations.

Abstract: Providing for read/write access to captured free variables expressed as environment data objects is disclosed herein. By way of example, free variables can be rewritten as a field of a private class, and accessed via an instance of the private class captured within the environment data object. In such a manner, no need of a placeholder or proxy function is required to access the free variable. Consequently, the subject innovation provides a simplified method for capturing free variables within expression trees and providing read/write access to such variables.

Abstract: A method and system for mapping between constructs in a domain modeling language and a relational storage language is provided. A source code authored in a source language is received and a set of constructs in the source code are identified. The set of constructs in the source code are mapped to a set of constructs in a target language. The source code is then compiled into a target code authored in the target language such that one of the source code or target code include a declarative constraint-based and/or order-independent execution model.

Abstract: A method, system, and computer product for processing information embedded in a text file with a grammar programming language is provided. A text file is parsed according to a set of rules and candidate textual shapes corresponding to potential interpretations of the text file are provided by compiling a script. An output is provided, which may include either a processed value corresponding to a particular textual shape, or a textual representation of the text file that includes generic data structures that facilitate providing any of the candidate textual shapes, where the generic data structures are a function of the set of rules.

Abstract: Providing for read/write access to captured free variables expressed as environment data objects is disclosed herein. By way of example, free variables can be rewritten as a field of a private class, and accessed via an instance of the private class captured within the environment data object. In such a manner, no need of a placeholder or proxy function is required to access the free variable. Consequently, the subject innovation provides a simplified method for capturing free variables within expression trees and providing read/write access to such variables.