Oracle International Corporation

Robert James Oberbreckling

Luis E Rivas

Michael Malak

Glenn Allen Murray

Mark L Kreider

Jean-Baptiste Frederic George Tristan

Philip Ogren

Daniel Peterson

The present disclosure related to techniques for analyzing data from multiple different data sources to determine a relationship between the data (also referred to herein a 'data relationship discovery'). The relationships between any two compared datasets may be used to determine one or more recommendations for merging (e.g., joining), or 'blending,' the data sets together. Relationship discovery may include determining a relationship between a subset of data, such as a relationship between a pair of columns, or column pair, each column in a different dataset of the datasets that are compared. Given two datasets to process for relationship discovery, relationship discovery may identify and recommends a ranked subset of column pairs between two compared datasets. The ranked column pairs identified as a relationship may be useful for blending the datasets with respect to those column pairs.

Techniques for relationship discovery between datasets

The present disclosure relates to techniques for analysis of data from multiple different data sources to determine similarity amongst the datasets. Determining a similarity between datasets may be useful for downstream processing of those datasets for different uses. A graphical interface may be provided to display detailed results including: a similarity prediction, data similarity prediction, column order similarity prediction, document type similarity prediction, prediction of overlapping or related columns, orphaned column prediction (e.g., a left orphaned column or a right orphaned column).

Techniques for dataset similarity discovery

Systems and methods are disclosed to improve a topic modeling system that tunes a topic model for a set of topics from a corpus of documents, by allowing users to pre-inform the tuning process with bias parameters for desired associations in the topic model. In embodiments, the topic model may be a Latent Dirichlet Allocation (LDA) model. In embodiments, the bias parameter may indicate a fixed association where a particular word in a particular document is associated with a particular topic. In embodiments, the bias parameter may specify a weight value that biases the inference process with regard to a particular association. Advantageously, the disclosed features allow users to specify a small number of parameters to steer the tuning process towards a set of desired topics. As a result, the topic model may be generated more quickly and with more useful topics.

Bias parameters for topic modeling

Techniques are disclosed for standardization of data. According to a first technique, standard representation terms are determined for to-be-standardized data using the to-be-standardized data itself and without using any external reference data. According to a second technique, a combination of the to-be-standardized data and an external reference is used to determine standard representation terms for the to-be-standardized data.

Data standardization techniques

The present disclosure relates to techniques for analysis of data from multiple different data sources to determine similarity amongst the datasets. Determining a similarity between datasets may be useful for downstream processing of those datasets for different uses. A graphical interface may be provided to display detailed results including: a similarity prediction, data similarity prediction, column order similarity prediction, document type similarity prediction, prediction of overlapping or related columns, orphaned column prediction (e.g., a left orphaned column or a right orphaned column). Detecting similarities may be useful for leveraging prior data transformations generated for the datasets that are analyzed.

Growing community of inventors

Boulder, CO, United States of America

Robert James Oberbreckling