Workday, Inc.

Vladimir Giverts

Kristy Gateley

Adeyemi Ajao

Michael K Wilson

Parag Avinash Namjoshi

Lucian Vlad Lita

Efraim David Feinstein

Jonathan Goldman

Darius Kasad

Phil Monroe

Jan Prach

Barzel Segal

David Vieira

Tarek Rabbani

Anuranjita Tewary

Philip Monroe

Tejas Mandke

Mohammad Sabah

Daniel Walter Beck

Pavan Boob

Xiao Fan

Michael Au

James Fan

Kevin Mun Joun Tham

A system for indexing and searching includes an input interface and a processor. The interface is to receive a request to search for a term. The processor is to determine a search response based at least in part on a security associated with an index field and the term.

Indexing structured data with security information

A system for generating a database of labeled foreign canonical titles includes an interface and a processor. The interface is to receive a title in a second language. The processor is to 1) store a set of n-grams in a first language in a first database; 2) sanitize the title into a sanitize title in the second language; 3) translate the sanitized title into a translated title in the first language; 4) break the translated title into n-grams; 5) determine labels for the n-grams using the first database; and 6) determine label to associate with the title.

Systematic mass normalization of international titles

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

Vladimir Giverts