Adobe, Inc.

Vibhor Porwal

Iftikhar Ahamath Burhanuddin

Subrata Mitra

Ryan A Rossi

Anup Bandigadi Rao

Fan Du

Koyel Mukherjee

Ayush Chauhan

Gaurav Sinha

Yeuk-Yin Chan

Aurghya Maiti

Anil Malkani

Nischay

Aaron Jerry Ninan

Aryan Yadav

Syam Manoj Kumar Paila

Manav Ketan Doshi

Ghazi Shazan Ahmad

Adit Akarsh

Himanshu Panday

Shaddy Garg

Sergey N Kazarin

Sameeksha Arora

Gautam Pratap Kowshik

Vidit Bhatia

Ruchi Sandeep Pandya

Meenakshi Chembil Shaji

Gadikoyila Satya Vamsi

Jonathan Ve Vance

Rebin Silva V

Shubham Agarwal

Systems and methods for data narration are provided. One aspect of the systems and methods includes obtaining a dataset including a plurality of data elements, wherein each of the data elements includes a plurality of attributes; clustering the plurality of data elements to obtain a plurality of data segments; extracting a plurality of facts from the dataset based on the plurality of data segments; generating a graph including a plurality of nodes corresponding to the plurality of facts, respectively; computing an ordering of the plurality of facts based on the graph; and generating a description of the dataset based on the ordering of the plurality of facts.

Systems and methods for data narration

Methods and systems are provided for using reinforcement learning to recommend data visualizations. In embodiments described herein, statistical features for each sample of corresponding samples of a dataset are determined by applying each sample of the dataset to a data visualization recommendation model. The computational cost of each of the statistical features for each of the samples is determined based via a regression model. Recommended statistical features are determined by sequentially applying each sample to a reinforcement learning model with a computational budget and with the corresponding computational costs of the statistical features of each sample. A data visualization is then displayed that is generated by applying the dataset and the recommended statistical features to the data visualization recommendation model.

Using reinforcement learning to recommend data visualizations

To retrieve information derived from a plurality of separately stored datasets, join structures are identified within the plurality of separately stored datasets. Join structures can include datasets joined by a central dataset, datasets joined by a single key, and datasets joined across a plurality of keys. Each of the join structures corresponds to a query processing schema that defines a sampling technique. When a join query is received as a SQL query, the join query identifies a portion of the plurality of separately stored datasets, from which a join structure is selected and a corresponding query processing schema is identified. The join query is reconstructed to form a reconstructed join query that comprises query processing schema instructions to derive the requested information using the sampling technique defined by the identified query processing schema.

Approximate query processing for join queries

In implementations of systems for estimating terminal event likelihood, a computing device implements a termination system to receive observed data describing values of a treatment metric and indications of a terminal event. Values of the treatment metric are grouped into groups using a mixture model that represents the treatment metric as a mixture of distributions. Parameters of a distribution are estimated for each of the groups and mixing proportions are also estimated for each of the groups. In response to receiving a user input requesting an estimate of a likelihood of the terminal event for a particular value of the treatment metric, the termination system generates an indication of the estimate of the likelihood of the terminal event for the particular value based on a distribution density at the particular value for each of the groups and a probability of including the particular value in each of the groups.

Systems for estimating terminal event likelihood

Methods, systems, and non-transitory computer readable media are disclosed for intelligently generating comprehensive and relevant visual data stories from tabular data. The disclosed system can create data stories from a given dataset guided by some user selections to set a theme for the story. In particular, the disclosed system evaluates facts of a dataset to identify significant facts. The disclosed system can order significant facts to create a backbone for the data story based on user selections by utilizing a beam search. Furthermore, the disclosed system can expand the backbone of the data story by generating coherence facts. The disclosed system may present the generated story via a graphical user interface.

Generating visual stories from data sets

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Auraiya, India

Vibhor Porwal