Workday, Inc.

Adeyemi Ajao

Vladimir Giverts

Lucian Vlad Lita

Efraim David Feinstein

Jonathan Goldman

Kristy Gateley

Barzel Segal

Phil Monroe

Jan Prach

Tarek Rabbani

Anuranjita Tewary

Parag Avinash Namjoshi

Mohammad Sabah

Daniel Walter Beck

James Fan

Kevin Mun Joun Tham

A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset of time series. The feature vector extractor is for determining a set of feature vectors determined from a time series of the model set of time series. The model builder is for determining one or more models based at least in part on the set of feature vectors. The predictor is for predicting retention risk for a given employee using the one or more models.

Retention risk determiner

A system for determining a canonical title includes an interface and a processor. The interface is to receive a new title. The processor is to generate a set of n-grams based on the new title; look up n-grams in canonical database to assign one or more labels; and map the new title to a canonical title based on the one or more labels.

Method for systematic mass normalization of titles

A method for normalizing raw titles to canonical titles is described. The method includes designating a set of canonical titles, generating a set of n-grams for each canonical title, assigning a set of attributes to each n-gram, assigning a set of labels to each of the attributes, and storing the labeled canonical title and labeled n-grams in a database. In some examples, a new title may be mapped to an existing canonical title in the database by generating a set of n-grams for the new title, looking up the n-grams in the database of canonical titles, retrieving the set of labels assigned to n-grams in the database that match n-grams from the new title, and assigning those labels to the corresponding attributes of the new title. The new title may then be mapped to a canonical title on the basis of similarly labeled attributes.

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San Francisco, CA, United States of America

Adeyemi Ajao