The present disclosure includes a computing device, a system, and method for performing a spreadsheet recalculation algorithm. In one embodiment, the computing device includes an electronic processor, and a memory coupled to the electronic processor. The memory includes Directed Acyclic Graph (DAG) data having a plurality of nodes, each node of the plurality of nodes having at least one of a constant value or one or more inputs, and program instructions. The program instructions, when executed by the electronic processor, cause the electronic processor to change the plurality of nodes, and update one or more affected nodes of the plurality of nodes based on the change to the plurality of nodes, the one or more affected nodes being less than all of the plurality of nodes.

Dynamically normalizing intervals in a table including receiving, from a client computing system, a request to normalize intervals for a data set on a cloud-based data warehouse, wherein the request comprises a reference to the data set and a data range; generating, on the cloud-based data warehouse, an interval table using the data range; joining, into a joined table on the cloud-based data warehouse, the interval table and the data set; receiving the joined table from the cloud-based data warehouse; and presenting, via a graphical user interface on the client computing system, the joined table as a worksheet.

Pasting content from a clipboard buffer as structured tabular data. A computer system determines a data type of content within a clipboard buffer. Based on the data type of the content, the computer system identifies a tabular pattern analysis technique to apply to the content. Based on applying the tabular pattern analysis technique to the content, the computer system identifies a portion of tabular content within the content. Using a clipboard application programming interface, the computer system presents the portion of tabular content to an application as paste data that is structured as a set of rows and a set of columns.

Pasting content from a clipboard buffer as structured tabular data. A computer system determines a data type of content within a clipboard buffer. Based on the data type of the content, the computer system identifies a tabular pattern analysis technique to apply to the content. Based on applying the tabular pattern analysis technique to the content, the computer system identifies a portion of tabular content within the content. Using a clipboard application programming interface, the computer system presents the portion of tabular content to an application as paste data that is structured as a set of rows and a set of columns.

An attribution query pertaining to a selected portion of a client model of a collaborative electronic document is received from a client device. The selected portion of the client model corresponds to a first coordinate location within a first coordinate structure of the client model of the collaborative electronic document. A first revision identifier associated with a first change at the first coordinate location of the client model is identified. The first coordinate location corresponds to a second coordinate location within a second coordinate structure of a server model of the collaborative electronic document. Attribution information that is associated with the first revision identifier is retrieved. The attribution information is provided to the client device in response to the attribution query.

A method to generated a chart recommendation based on machine understanding of spreadsheet data, including determining a set of data that each include content of a cell of one or more cells in a column of a spreadsheet presented to a user. The method further determines an entity type associated with the column based on the set of data. The entity type represents a semantic meaning of the set of data in the column of the spreadsheet. The method further identifies at least one of a plurality of charts that is relevant to the entity type associated with the column. The method then provides the identified chart for presentation to the user.

A method to generated a chart recommendation based on machine understanding of spreadsheet data, including determining a set of data that each include content of a cell of one or more cells in a column of a spreadsheet presented to a user. The method further determines an entity type associated with the column based on the set of data. The entity type represents a semantic meaning of the set of data in the column of the spreadsheet. The method further identifies at least one of a plurality of charts that is relevant to the entity type associated with the column. The method then provides the identified chart for presentation to the user.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

Systems, devices, and methods for determining color ambiguity of images or videos. A system includes a color ambiguity score generator, which analyzes an image and determines a color ambiguity score that quantitively indicates a level of color ambiguity that the image is estimated to cause when viewed by a user having color vision deficiency. A local color ambiguity score is generated to quantitively indicate a level of local color ambiguity between (i) an in-image object and (ii) an in-image foreground of that in-image object. A global color ambiguity score is generated to quantitively indicate a level of global color ambiguity between (I) a first in-image object within the image and (II) a second in-image object within that image. The color ambiguity score generator generates the color ambiguity score by utilizing a formula that uses both (A) the local color ambiguity score and (B) the global color ambiguity score.

With the availability of huge amount of data, it has becoming difficult to identify and manage duplicate data, especially when the data is in a plurality of columns. A method and system for identifying duplicate columns using statistical, semantics and machine learning techniques have been provided. The system provides a design framework to compare huge datasets at column level and identify potential duplicate columns, not based on the column title, but based on all of its values. The disclosure has ability to compare values in multiple columns and identify potential duplicate columns wherein comparison of values is not only for the exact match, but for semantic match, smart match, fuzzy match, and match after UOM conversion etc. using Statistical, semantics and machine learning techniques.