Generating and executing automatic suggestions to modify data of ingested data collections without additional data ingestion

Patent metadata

Field	Value
Publication number	US-12182086-B2
Application number	US-202117347133-A
Country	US
Kind code	B2
Filing date	Jun 14, 2021
Priority date	Jun 14, 2021
Publication date	Dec 31, 2024
Grant date	Dec 31, 2024

Abstract

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating automatic suggestions to effectively modify the organization of an ingested data collection without destruction of the underlying raw data. In particular, in one or more embodiments, the disclosed systems utilize multiple machine learning models in sequence to determine likelihoods that the organizational structure of an ingested data collection should be modified in various ways. In response to generating these likelihoods, the disclosed systems generate corresponding automatic suggestions to modify the organization of the ingested data collection. In response to a detected selection of one or more of the automatic suggestions, the disclosed systems read data out of the ingested data collection in accordance with the selected automatic suggestions to effectively modify the organization of the ingested data collection.

First claim

What is claimed is:

1. A computer-implemented method comprising:
accessing, by a digital data analytics management system, an ingested data collection categorized into a plurality of dimensions, wherein the ingested data collection includes raw analytics data associated with touchpoints of a plurality of client devices with a third-party computing system ingested into a schema and each of the plurality of dimensions comprises dimension items that indicate pre-defined inputs for interactions between the plurality of client devices and the third-party computing system;
generating a dimension report for the plurality of dimensions comprising the dimension items based on the ingested data collection to display to a client device of the digital data analytics management system, the dimension report showing the raw analytics data organized according to the pre-defined inputs for interactions between the plurality of client devices and the third-party computing system;
utilizing dimension items from the plurality of dimensions to select one or more machine learning models from a plurality of machine learning models;
in response to a query to modify an organization of the ingested data collection, generating one or more automatic suggestions to modify one or more dimensions or dimension items utilizing the selected one or more machine learning models;
generating a display of the one or more automatic suggestions for the client device of the digital data analytics management system; and
in response to a detected selection of an automatic suggestion from the display of the client device of the digital data analytics management system, modifying the one or more dimensions or dimension items when retrieving data from the ingested data collection by:
generating a fallback regular expression incorporating the automatic suggestion;
identifying a dimension item or dimension of the one or more dimensions or dimension items referred to by the fallback regular expression;
generating a meta-field according to the fallback regular expression for the dimension item or dimension of the one or more dimensions or dimension items; and
reading data from the identified dimension item or dimension of the one or more dimensions or dimension items into the meta-field according to instructions within the fallback regular expression without destroying the data ingested into the schema; and

generating an updated dimension report for the identified dimension item or dimension of the one or more dimensions or dimension items by reading data out of the meta-field, the meta-field indicating at least one of an indication to disregard, an indication to merge, or an indication to rename as referred to by the fallback regular expression.

2. The computer-implemented method as recited in claim 1 , wherein generating the one or more automatic suggestions to modify the one or more dimensions or dimension items comprises:
generating, without user input and utilizing a first machine learning model trained for determining a likelihood that dimensions or dimension items in the ingested data collection should be merged an automatic suggestion to merge two or more dimensions or dimension items;
generating, without user input and utilizing a second machine learning model trained for determining a likelihood that dimension items in the ingested data collection should be renamed, an automatic suggestion to remove the one or more dimensions or dimension items; or
generating, without user input and utilizing a third machine learning model trained for determining a likelihood that dimension items in the ingested data collection should be removed, an automatic suggestion to rename the one or more dimensions or dimension items.

3. The computer-implemented method as recited in claim 2 , wherein generating the automatic suggestion to merge the two or more dimensions or dimension items comprises:
determining pairwise distances between pairs of names of the one or more dimensions or dimension items;
generating a first cluster comprising a first subset of pairs with pairwise distances that are less than a predetermined threshold distance; and
generating a first automatic suggestion to merge the one or more dimensions or dimension items within the first cluster.

4. The computer-implemented method as recited in claim 3 , wherein generating the first automatic suggestion to merge the two or more dimensions or dimension items within the first cluster further comprises:
determining a dimension or dimension item with a corresponding highest number of associated values; and
further generating the first automatic suggestion to merge remaining dimension or dimension items within the first cluster with the dimension or dimension item with the corresponding highest number of associated values.

5. The computer-implemented method as recited in claim 3 , further comprising:
generating a second cluster comprising a second subset of pairs with pairwise distances that are less than the predetermined threshold distance; and
generating a second automatic suggestion to merge two or more additional dimensions or dimension items within the second cluster.

6. The computer-implemented method as recited in claim 5 , further comprising:
determining inter-cluster distances for the first cluster and the second cluster; and
ranking the first automatic suggestion and the second automatic suggestion based on the inter-cluster distances.

7. The computer-implemented method as recited in claim 2 , wherein generating the automatic suggestion to remove the one or more dimensions or dimension items comprises:
generating embeddings for the one or more dimensions or dimension items within a common space;
determining an average similarity score for each embedding, wherein the average similarity score represents an average distance between each embedding and other embeddings within the common space;
determining one or more embeddings with average similarity scores that do not satisfy a predetermined cut-off similarity score; and
generating the automatic suggestion to remove the one or more dimensions or dimension items corresponding to the determined one or more embeddings with the average similarity scores that do not satisfy the predetermined cut-off similarity score.

8. The computer-implemented method as recited in claim 2 , wherein generating the automatic suggestion to rename the one or more dimensions or dimension items comprises:
determining names for the one or more dimensions or dimension items;
generating embeddings of the names within a common space; and
for each embedding of the embeddings, iteratively:
mask a portion of each embedding;
predict a string corresponding to the masked portion of each embedding;
compare the predicted string to the portion of a name corresponding to the masked portion of each embedding; and
generating the one or more automatic suggestions to rename the one or more dimensions or dimension items based on the comparison.

9. The computer-implemented method as recited in claim 1 , further comprising:
in response to the query to modify the organization of the ingested data collection, generating, the one or more automatic suggestions to modify two or more dimensions or dimension items; and
generating a first meta-field and a second meta-field for the two or more dimensions or dimension items, wherein the first meta-field differs from the second meta-field.

10. The computer-implemented method as recited in claim 9 , further comprising:
modifying the organization of the ingested data collection by reading data from the ingested data collection according to the first meta-field and the second meta-field,
wherein modifying the organization

Assignees

• Adobe Inc

Inventors

• Sinha Ritwik
• Mitra Saayan
• Zhao Handong
• Sarkhel Somdeb
• Paulsen Trevor
• George William Brandon

Classifications

• G06F16/2423

  Interactive query statement specification based on a database schema · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06N3/09

  Supervised learning · CPC title