公开(公告)号：US20150142727A1

公开(公告)日：2015-05-21

申请号：US14547080

申请日：2014-11-18

Applicant: salesforce.com, inc.

Inventor： Patrick Yu Pang Louie ,  Arunkumaran Varadharajan ,  Daniel Scott Perry ,  Aditya Kochhar ,  Jan Michael Timmerman ,  Paul James McArthur ,  Santanu Sinha ,  Smita S. Kulkarni ,  Sushanth Ramakrishna ,  Twisha Mehta

IPC: G06F17/30

CPC classification number: G06F16/248 ,  G06F16/26

Abstract: Embodiments regard analytic operations for data services. An embodiment of a method includes: receiving a request at a database system from a user to embed a representation of analytics data on a first page generating by a processing unit of the database system an embed area in the page in which to display the analytics data on the page; storing in a memory of the database system metadata associated with the page describing the embed area; and upon the database system detecting an access to the first page, generating by the database system the first page with the analytics data located in the embed area based upon the metadata.

Abstract translation: 实施例涉及数据服务的分析操作。 一种方法的实施例包括：从数据库系统接收来自用户的请求，以将分析数据的表示嵌入由数据库系统的处理单元生成的第一页面上，以在其中显示分析数据的页面中的嵌入区域 在页面上 存储与描述嵌入区域的页面相关联的数据库系统元数据的存储器; 并且在所述数据库系统检测到对所述第一页面的访问时，基于所述元数据，由所述数据库系统生成具有位于所述嵌入区域中的所述分析数据的所述第一页面。

公开(公告)号：US20220121685A1

公开(公告)日：2022-04-21

申请号：US17072597

申请日：2020-10-16

Applicant: salesforce.com, inc.

Inventor： Zuye Zheng ,  Scott Musser ,  Raghav Ravichandran ,  Asra Yousuf ,  Meghana Ashok ,  Andrew Ouyang ,  Aditya Kochhar

IPC: G06F16/28 ,  G06F16/242 ,  G06F16/9032 ,  G06K9/62 ,  G06N20/20

Abstract: Methods, systems, and devices for generating a query using training observations are described. According to the techniques described herein, a device (e.g., an application server) may receive a set of queries including a set of fields in a tenant-specific dataset associated with the query. The device may generate a set of training observations for the queries based on the set of fields. The device then trains a first machine learning model to determine grouping hierarchies and a second machine learning model to determine aggregation predictions. The device then builds a combined machine learning model based on the determined grouping hierarchies and the aggregation predictions. According to techniques described herein, the device uses the determined grouping hierarchies and the aggregation predictions to rank a set of suggested queries determined in response to an input query and selects a suggested query for display based on the ranking.