A computer-implemented method interfaces with a remote attachment from a spreadsheet program on a client device. A first area in the spreadsheet associates with a top-level object in a web server, and a second area associates with a descendant object. On a first user interaction with a cell in the second area, a user interface item is shown, allowing uploading and/or downloading the attachment. When the user interacts to upload or download, the program uploads (or queues for upload) a selected attachment and modifies attachment metadata in the second area, or downloads the attachment based on attachment metadata in the second area. Communication between the spreadsheet program and the web server may be REST compliant.

A computer-implemented method interfaces with a remote attachment from a spreadsheet program on a client device. A first area in the spreadsheet associates with a top-level object in a web server, and a second area associates with a descendant object. On a first user interaction with a cell in the second area, a user interface item is shown, allowing uploading and/or downloading the attachment. When the user interacts to upload or download, the program uploads (or queues for upload) a selected attachment and modifies attachment metadata in the second area, or downloads the attachment based on attachment metadata in the second area. Communication between the spreadsheet program and the web server may be REST compliant.

A method for generating a chart suggestion list using data present in a data table includes providing the data table, one or more data types of a plurality of cells of the data table and one or more statistics pertaining to the plurality of cells as input to a machine learning model to obtain one or more outputs indicating one or more chart types for the data table, and creating one or more suggested charts based on the indicated chart types. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.

A method for generating a chart suggestion list using data present in a data table includes providing the data table, one or more data types of a plurality of cells of the data table and one or more statistics pertaining to the plurality of cells as input to a machine learning model to obtain one or more outputs indicating one or more chart types for the data table, and creating one or more suggested charts based on the indicated chart types. Each of the one or more suggested charts represents at least a subset of the data in the data table. The method also includes generating a chart suggestion list including at least one of the one or more suggested charts.

The disclosed technology creates a family of spreadsheet functions (predefined formulas) which allows users to create programming loop equivalents in their regular spreadsheet cells combining familiar range or array evaluating functions (e.g., SUM, COUNT, MIN, MAX, etc.) with data filtering and output selection. The input data to these new functions can be sourced from multiple cells within the spreadsheet or a broad spectrum of numeric, date and text data not stored in a spreadsheet, including data not discretely defined. The technology disclosed can use as inputs cell ranges or Non-spreadsheet Cell (NSC) data formulas. The technology supports functions with predefined combinations and user specified combinations all capable of automating, millions of looped calculations.

The disclosed technology creates a family of spreadsheet functions (predefined formulas) which allows users to create programming loop equivalents in their regular spreadsheet cells combining familiar range or array evaluating functions (e.g., SUM, COUNT, MIN, MAX, etc.) with data filtering and output selection. The input data to these new functions can be sourced from multiple cells within the spreadsheet or a broad spectrum of numeric, date and text data not stored in a spreadsheet, including data not discretely defined. The technology disclosed can use as inputs cell ranges or Non-spreadsheet Cell (NSC) data formulas. The technology supports functions with predefined combinations and user specified combinations all capable of automating, millions of looped calculations.

The present disclosure is directed to a universal data language (UDL) translator. Specifically, the systems and methods disclosed enable input data from a variety of sources to be translated into a UDL that can be consistently analyzed and compared against other sources of data. For example, an entity may upload input data that has a plurality of data terms and definitions (e.g., header column in a spreadsheet). These terms may be duplicative and/or inaccurate with respect to the underlying data. If the entity wishes to compare and transact data within a data marketplace, the entity may not fully comprehend what data it is missing and/or what data another entity may have to offer for trade. To remedy this problem of business semantic management, the present invention discloses steps for creating a UDL and a UDL translator so that any input data can be translated to UDL.

A whitelisting approach to redaction may include receiving a normalization request containing a binary file with complex structured data, such as a workbook or spreadsheet file, from an application on a user device through a redaction service provided by an e-discovery platform. In response to the normalization request, a normalization process starts and runs asynchronously to apply a whitelist to the binary file. The whitelist is configurable and specifies features (e.g., data types, workbook object types, etc.) of the binary file that are considered safe by a party to a lawsuit or investigation. The normalization process includes a pre-processing stage, a transferring stage, and a post-processing stage to push all the data to a visible level and produce a normalized document with the whitelisted features. An artifact containing the normalized document can then be generated and used to render a normalized representation for interactive redaction.

A whitelisting approach to redaction may include receiving a normalization request containing a binary file with complex structured data, such as a workbook or spreadsheet file, from an application on a user device through a redaction service provided by an e-discovery platform. In response to the normalization request, a normalization process starts and runs asynchronously to apply a whitelist to the binary file. The whitelist is configurable and specifies features (e.g., data types, workbook object types, etc.) of the binary file that are considered safe by a party to a lawsuit or investigation. The normalization process includes a pre-processing stage, a transferring stage, and a post-processing stage to push all the data to a visible level and produce a normalized document with the whitelisted features. An artifact containing the normalized document can then be generated and used to render a normalized representation for interactive redaction.

Disclosed are database systems, methods, systems, and computer program products for providing tabular data in a social network feed. In some implementations, a server of a database system stores, in a database, social network feed data comprising a plurality of feed items as data objects. The server also stores, in a database, tabular data as data objects. The server shares the social network feed data and the tabular data in a social network feed displayable to present the feed items and the tabular data in a user interface, the tabular data being presentable in the user interface in a tabular format. The user interface may receive shareable user commentary regarding the tabular data. The stored tabular data may be editable by users of the database system. The social network feed may be updated to comprise notifications of edits to the tabular data.