Tamr, Inc.

Nikolaus Bates-Haus

Ihab Francis Ilyas

George Beskales

Daniel Meir Bruckner

George Anwar Dany Beskales

Michael Ralph Stonebraker

Alexander Richter Pagan

Alexandra V Batchelor

Brian A Long

Pedro Giesemann Cattori

Jason Liu

Stan Zdonik

Andrew H Palmer

Aizana Turmukhametova

Tianyu Zhu

Vladimir Gluzman Peregrine

Kanak Kshetri

Record clustering is performed by learning from verified clusters which are used as the source of training data in a deduplication workflow utilizing supervised machine learning.

Method of using clusters to train supervised entity resolution in big data

Record clustering is performed for a collection of records using training rules, training-rule labels, training data created from a sample of pairs of records, a pair-wise classifier, and a clustering algorithm. Record clustering is also performed for a collection of records using prediction rules, prediction-rule labels, a pair-wise classifier, and a clustering algorithm.

Methods and computer program products for clustering records using imperfect rules

An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions send to the experts. The system solves the problem of schema mapping and record deduplication a holistic way by unifying these problems into a unified linkage problem.

Method and system for large scale data curation

A data curation system is provided that includes various methods to enable efficient reuse of human and machine effort. To reuse effort, various facilities are presented that model, save, and allow for querying of provenance and state information of a curation workflow and allow for incremental, stateful transitions of the data and metadata thereof.

Data curation system with version control for workflow states and provenance

An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions sent to the experts. The system solves the problem of schema mapping and record deduplication in a holistic way by unifying these problems into a unified linkage problem.

Given a local distance metric for geospatial features, a binning is produced that is guaranteed to label features within a given distance threshold with the same bin, while labeling a minimum number of features separated by a distance that is greater than the threshold with the same bin.

Growing community of inventors

Littleton, MA, United States of America

Nikolaus Bates-Haus