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:
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:
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:
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:
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, from one of the network applications, to access a particular data resource. The request includes an identifier of a requesting user. The method further includes accessing a policy object associated with the data resource that includes policy information specifying operations the user is authorized to perform with respect to the data resource based on satisfaction of one or more conditions. The method further includes evaluating the 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:
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. In particular, a data analysis system, such as described herein, may provide features to enable the data analyst to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the data analyst over the course of an investigation, as captured via the data analyst'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.
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. In particular, a data analysis system, such as described herein, may provide features to enable the data analyst to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the data analyst over the course of an investigation, as captured via the data analyst'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.
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.