Google Inc.

Mingge Deng

Amir H Hormati

Xi Cheng

Jiashang Liu

Lisa Yin

Zichuan Ye

Umar Syed

Gaurav Saxena

Deepak Choudhary Nettem

Kashif Yousuf

Weijie Shen

Peng Lin

Forest Elliott

Amir Hossein Hormati

A method for unstructured data analytics in data warehouses includes receiving an unstructured data query from a user, the unstructured data query requesting the data processing hardware determine one or more unstructured data files stored at a data repository that match query parameters. The method includes determining, using an object table, a set of unstructured data files stored at the data repository that matches the query parameters. The object table includes a plurality of rows, each row of the plurality of rows associated with a respective unstructured data file stored at the data repository, and a plurality of columns, each column of the plurality of columns comprising metadata associated with the respective unstructured data file of each row of the plurality of rows. The method includes returning, to the user, a structured data table including the determined set of unstructured data files.

Unstructured data analytics in traditional data warehouses

A method for forecasting time-series data, when executed by data processing hardware, causes the data processing hardware to perform operations including receiving a time series forecasting query from a user requesting a time series forecast forecasting future data based on a set of current time-series data. The operations include obtaining, from the set of current time-series data, a set of training data. The operations include training, using a first portion of the set of training data, a first sub-model of a forecasting model and training, using a second portion of the set of training data, a second sub-model of the forecasting model. The second portion is different than the first portion. The operations include forecasting, using the forecasting model, the future data based on the set of current time-series data and returning, to the user, the forecasted future data for the time series forecast.

Machine learning super large-scale time-series forecasting

A method for anomaly detection includes receiving an anomaly detection query from a user. The anomaly detection query requests data processing hardware determine one or more anomalies in a dataset including a plurality of examples. Each example in the plurality of examples is associated with one or more features. The method includes training a model using the dataset. The trained model is configured to use a local outlier factor (LOF) algorithm. For each respective example of the plurality of examples in the dataset, the method includes determining, using the trained model, a respective local deviation score based on the one or more features. The method includes determining that the respective local deviation score satisfies a deviation score threshold and, based on the location deviation score satisfying the threshold, determining that the respective example is anomalous. The method includes reporting the respective anomalous example to the user.

Anomaly detection with local outlier factor

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scalable matrix factorization. A method includes obtaining a Structured Query Language (SQL) query to create a matrix factorization model based on a set of training data, generating SQL sub-queries that don't include non-scalable functions, obtaining the set of training data, and generating a matrix factorization model based on the set of training data and the SQL sub-queries that don't include non-scalable functions.

Scalable matrix factorization in a database

A method includes receiving a point data anomaly detection query from a user. The query requests the data processing hardware to determine a quantity of anomalous point data values in a set of point data values. The method includes training a model using the set of point data values. For at least one respective point data value in the set of point data values, the method includes determining, using the trained model, a variance value for the respective point data value and determining that the variance value satisfies a threshold value. Based on the variance value satisfying the threshold value, the method includes determining that the respective point data value is an anomalous point data value. The method includes reporting the determined anomalous point data value to the user.

Point anomaly detection

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that creates a machine learning model with k-means clustering. In some implementations, an instruction to create a model is obtained. A data set including geographic data and non-geographic data is received. The data set includes multiple data entries. Geographic centroids are determined from the geographic data. The data set is analyzed to obtain statistics of the data set. Transformed data is generated from the data set, the statistics, and the geographic centroids. A model is generated with the transformed data, the model indicating multiple data groupings.

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Mingge Deng