Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.

Abstract: A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Abstract: Systems and methods are provided for enhanced processing of time series data via parallelization of instructions. An example method includes receiving a query indicating time series datasets and operations to be performed on the time series datasets. Nodes associated with the query are identified, with each node associated with a time series dataset. Nodes associated with operations to be performed are generated. The nodes are assembled into query tree, with parent nodes of the query tree indicating operations that are to be applied to children nodes. Instructions for processing the query tree are generated. At least a subset of the instructions is provided to one or more compute systems for processing in parallel. Results are received, and presented in a user interface.

Abstract: Computing systems methods, and non-transitory storage media are provided for receiving a monitoring request. The monitoring request includes one or more entities or attributes to be monitored, one or more rules to be evaluated with respect to the entities or attributes, and one or more downstream actions to be selectively triggered based on the evaluation. Next, data regarding the entities or the attributes is obtained. Next, a log is generated. The log includes changes or updates, relative to a previous iteration, of the entities or the attributes. The changes or updates correspond to the rules. Next, the changes or the updates are evaluated against the one or more rules and based on the log. Next, one or more actions are selectively implemented based on the evaluation of the changes or the updates.

Abstract: Various systems and methods are described herein for an improved spreadsheet application that allows a user to generate, manipulate, and replicate data visualizations (e.g., sparklines, graphs, charts, etc.) using functions without importing data into cells of the application. For example, data is stored in one or more remote or local data stores accessible to the improved spreadsheet application. A user enters a function into a cell of the improved spreadsheet application. The improved spreadsheet application generates a query using the function, the query identifying a portion of a dataset to retrieve from the data store(s). The improved spreadsheet application then transmits the query to the data store(s) and retrieves the requested data. A renderer of the improved spreadsheet application then renders a sparkline using the retrieved data. The improved spreadsheet application displays the rendered sparkline in the cell in which the function was entered, or at another designated location.

Abstract: Systems and methods are provided for enhanced processing of time series data via parallelization of instructions. An example method includes receiving a query indicating time series datasets and operations to be performed on the time series datasets. Nodes associated with the query are identified, with each node associated with a time series dataset. Nodes associated with operations to be performed are generated. The nodes are assembled into query tree, with parent nodes of the query tree indicating operations that are to be applied to children nodes. Instructions for processing the query tree are generated. At least a subset of the instructions is provided to one or more compute systems for processing in parallel. Results are received, and presented in a user interface.

Abstract: A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and projection datasets. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The projection datasets are indexes of the canonical dataset and/or the buffer that may have single or multiple column sort-orders and/or particular data formats. The writes to the canonical dataset, projection datasets, and buffer may be asynchronous and therefore the database system is advantageously less resource constrained.

Abstract: Various systems and methods are described herein for an improved spreadsheet application that allows a user to generate, manipulate, and replicate data visualizations (e.g., sparklines, graphs, charts, etc.) using functions without importing data into cells of the application. For example, data is stored in one or more remote or local data stores accessible to the improved spreadsheet application. A user enters a function into a cell of the improved spreadsheet application. The improved spreadsheet application generates a query using the function, the query identifying a portion of a dataset to retrieve from the data store(s). The improved spreadsheet application then transmits the query to the data store(s) and retrieves the requested data. A renderer of the improved spreadsheet application then renders a sparkline using the retrieved data. The improved spreadsheet application displays the rendered sparkline in the cell in which the function was entered, or at another designated location.

Abstract: A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Abstract: A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and projection datasets. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The projection datasets are indexes of the canonical dataset and/or the buffer that may have single or multiple column sort-orders and/or particular data formats. The writes to the canonical dataset, projection datasets, and buffer may be asynchronous and therefore the database system is advantageously less resource constrained.

Abstract: Systems and methods are provided for improved time series databases and time series operations. A time series service responds to requests from external devices. The external devices request time series data and submit time series queries. The time series service generates planned and efficient time series queries from the initial queries. The time series service performs operations such as unit conversion, interpolation, and performing operations on time series data. The time series service can identify which time series database to query from and/or cause data to be populated into a time series database from a data pipeline system.

Abstract: A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Abstract: Aspects of the present disclosure include a system comprising a computer-readable storage medium storing at least one program and a method for managing access permissions associated with data resources. Example embodiments involve evaluating user access permissions with respect to shared data resources of a group of network applications. The method includes receiving a request to access a data resource. The method further includes accessing a policy object linked to the data resource that includes an effective policy for the data resource. The method further includes evaluating a user's access permissions with respect to the data resource based on the policy object and communicating a response to the network application that includes the access permission of the user.

Abstract: Systems and methods are provided for improved time series databases and time series operations. A time series service responds to requests from external devices. The external devices request time series data and submit time series queries. The time series service generates planned and efficient time series queries from the initial queries. The time series service performs operations such as unit conversion, interpolation, and performing operations on time series data. The time series service can identify which time series database to query from and/or cause data to be populated into a time series database from a data pipeline system.

Abstract: A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Abstract: A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and projection datasets. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The projection datasets are indexes of the canonical dataset and/or the buffer that may have single or multiple column sort-orders and/or particular data formats. The writes to the canonical dataset, projection datasets, and buffer may be asynchronous and therefore the database system is advantageously less resource constrained.

Abstract: Systems and methods are provided for enhanced processing of time series data via parallelization of instructions. An example method includes receiving a query indicating time series datasets and operations to be performed on the time series datasets. Nodes associated with the query are identified, with each node associated with a time series dataset. Nodes associated with operations to be performed are generated. The nodes are assembled into query tree, with parent nodes of the query tree indicating operations that are to be applied to children nodes. Instructions for processing the query tree are generated. At least a subset of the instructions is provided to one or more compute systems for processing in parallel. Results are received, and presented in a user interface.

Abstract: A method and system for serving assets is disclosed, comprising receiving an asset request to serve an asset, wherein the asset request originates at an application, and wherein the asset request comprises an advertisement of an asset to be served and a request for the network address of an asset server configured to serve the requested asset. The method further comprises causing a service discovery server to identify an asset server configured to serve the requested asset, and causing the requested asset to be served to the application.

Abstract: Systems and methods are provided for enhanced processing of time series data via parallelization of instructions. An example method includes receiving a query indicating time series datasets and operations to be performed on the time series datasets. Nodes associated with the query are identified, with each node associated with a time series dataset. Nodes associated with operations to be performed are generated. The nodes are assembled into query tree, with parent nodes of the query tree indicating operations that are to be applied to children nodes. Instructions for processing the query tree are generated. At least a subset of the instructions is provided to one or more compute systems for processing in parallel. Results are received, and presented in a user interface.

Abstract: In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.