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: 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 can comprise accessing streaming data; monitoring the parsed streaming data to determine whether the parsed streaming data satisfies a condition; accessing historical data comprising information relating to: a historical condition previously detected and how the historical condition was resolved; determining one or more system operations performable on the one or more technical systems based on at least the information relating to how the historical condition was resolved; in response to user selection, performing at least one of the one or more system operations on the one or more technical systems.

Abstract: Data analysis system with highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. The system enables the user to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the user over the course of an investigation, as captured via the user's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on. The data analysis system enables a user to refresh or insert a node in a workflow. The data analysis system further provides data editing, reporting, enrichment, expression language, and charting functionality.

Abstract: A method may comprise receiving a query for performing one or more computational operations on one or more multi-dimensional data sets representing multi-dimensional time series data collected in real-time from one or more sensors associated with one or more technical systems. The method may also comprise identifying the location of the one or more multi-dimensional time series data sets in one or more databases, retrieving the one or more multi-dimensional time series data sets from the identified one or more databases, and performing the one or more computational operations on the retrieved one or more multi-dimensional time series data sets. The method may also comprise generating output based on the result of the one or more computational operations indicative of one or more states of the one or more technical systems with respect to time.

Abstract: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. The data analysis system enables the user to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the user over the course of an investigation, as captured via the user's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on. The data analysis system enables a user to refresh or insert a node in a workflow.

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 may comprise receiving a query for performing one or more computational operations on one or more multi-dimensional data sets representing multi-dimensional time series data collected in real-time from one or more sensors associated with one or more technical systems. The method may also comprise identifying the location of the one or more multi-dimensional time series data sets in one or more databases, retrieving the one or more multi-dimensional time series data sets from the identified one or more databases, and performing the one or more computational operations on the retrieved one or more multi-dimensional time series data sets. The method may also comprise generating output based on the result of the one or more computational operations indicative of one or more states of the one or more technical systems with respect to time.

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, performed by one or more processors, may comprise receiving a query for performing one or more computational operations on one or more multi-dimensional data sets representing multi-dimensional time series data collected in real-time from one or more sensors associated with one or more technical systems. The method may also comprise identifying the location of the one or more multi-dimensional time series data sets in one or more databases, retrieving the one or more multi-dimensional time series data sets from the identified one or more databases, and performing the one or more computational operations on the retrieved one or more multi-dimensional time series data sets. The method may also comprise generating output based on the result of the one or more computational operations indicative of one or more states of the one or more technical systems with respect to time.

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: 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: 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: 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: 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.