Adobe, Inc.

Iftikhar Ahamath Burhanuddin

Fan Du

Harvineet Singh

Prakhar Gupta

Ryan A Rossi

Eunyee Koh

Atanu Ranjan Sinha

Kevin Gary Smith

William Brandon George

Niyati Himanshu Chhaya

Vishwa Vinay

Shiv Kumar Saini

Gaurush Hiranandani

Zheng Wen

Abhilasha Sancheti

Md Main Uddin Rony

Anup Bandigadi Rao

Georgios Theocharous

Ritwik Sinha

Branislav Kveton

Natwar Modani

Tung Mai

John Bevil Bates

Koyel Mukherjee

Kokil Jaidka

Shriram Venkatesh Shet Revankar

Saurabh Mahapatra

Vibhor Porwal

Manoj Kilaru

Ankur Garg

Aurghya Maiti

Rachit Bansal

Gautam Choudhary

Aaron Jerry Ninan

Aryan Yadav

Adit Akarsh

Meenakshi Chembil Shaji

Gadikoyila Satya Vamsi

Jonathan Ve Vance

Ramasuri Narayanam

Nischay

Rebin Silva V

Aneesh Shetty

Titas Chakraborty

Tirupati Saketh Chandra

Aditya Prakash

Kirnesh Nandan

Kritin Kesav Sai Sathi

Biswarup Bhattacharya

Kushal Satya

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

In implementations of systems for generating node embeddings for multiple roles, a computing device implements an embeddings system to cluster nodes of a graph into clusters. An initial role membership vector is computed for each of the nodes based on the clusters. The embeddings system generates a first set of role embeddings for a particular node of the nodes based on the initial role membership vector for the particular node and nodes connected to the particular node in the graph. The embeddings system determines an indication of at least one of a node classification or a link prediction for the graph based on the first set of role embeddings and a second set of role embeddings for an additional node of the nodes.

Generating node embeddings for multiple roles

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

Facilitating generation and presentation of advanced insights

Methods and systems are provided herein for summarizing a set of anomalies corresponding to a group of metrics of interest to a monitoring system user. Initially, a set of anomalies corresponding to a group of metrics is identified as having values that are outside of a predetermined range. A correlation value is determined for at least a portion of pairs of anomalies in the set of anomalies. For each anomaly in the set of anomalies, an informativeness value is computed that indicates how informative each anomaly in the set of anomalies is to the monitoring system user. The correlation values and the informativeness values are then used to identify at least one key anomaly and a plurality of non-key anomalies from the set of anomalies. A summary is generated of the identified at least one key anomaly to provide information to the monitoring system user about the set of anomalies for a particular time period.

Providing personalized alerts and anomaly summarization

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

Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.

Determining patterns within a string sequence of user actions

A method for recommending digital content includes: determining user preferences and a time horizon of a given user; determining a group for the given user based on the determined user preferences; determining a number of users of the determined group and a similarity of the users; applying information including the number of users, the similarity, and the time horizon to a model selection classifier to select one of a personalized model of the user and a group model of the determined group; and running the selected model to determine digital content to recommend.

Method and system for recommending digital content

In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Systems for generating sequential supporting answer reports

Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Leading indicators and monitor business KPIs and metrics for preemptive action

This disclosure covers methods, non-transitory computer readable media, and systems that use an intelligent analytics interface to process natural-language and other inputs to configure an analytics task for the system. The disclosed methods, non-transitory computer readable media, and systems provide the intelligent analytics interface to facilitate an exchange between the systems and a user to determine values for the analytics task. The methods, non-transitory computer readable media, and systems then use these values to execute an analytics task.

Natural-language based intelligent analytics interface

A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.

Online diverse set generation from partial-click feedback

Methods and systems are provided for providing recommendations from a recommendation system for an analytics system. A recommendation system can be trained using user intent and context. Such user intent can be determined using a user history of interaction with an analytics system. The user history can either be that of the user accessing the recommendation system or an exemplary user history to broaden the recommendations made by the recommendation system. Such context can be determined using context features within the analytics system. The trained recommendation system generated using user intent and context can provide analytics recommendations based on a current context of a user that predict the intent of the user.

Context-aware recommendation system for analysts

In implementations of a recommendation system based on individualized privacy settings, a computing device maintains user profiles of information and recommendations associated with users of the recommendation system. The computing device includes a recommendation module that is implemented to receive a privacy level selection for a type of items corresponding to a user profile in the system. The recommendation module can determine a privacy setting for a user associated with the user profile, where the privacy setting is individualized for the user in context of the type of items with an algorithmic noise function utilized to obfuscate a proportional level of the information associated with the user and the type of items based on the received privacy level selection. The recommendation module can also generate recommendations of relevant items for the user based on the determined privacy setting as individualized for the user in context of the type of items.

Recommendation system based on individualized privacy settings

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Bengaluru, India

Iftikhar Ahamath Burhanuddin