Google Inc.

Amir H Hormati

Mingge Deng

Xi Cheng

Lisa Yin

Umar Syed

Jiaxun Wu

Jiashang Liu

Zichuan Ye

Hossein Ahmadi

Forest Elliott

Peng Lin

Guang Cheng

Yannis Sismanis

Huong Thi Thu Phan

Shiyu Xie

Leo Chen

Zewen Zhang

Jing Jing Long

A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data. Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

Time series forecasting

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.

Transformation for machine learning pre-processing

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

A technology for retrieving data from a database. The technology includes receiving a search query specifying a target attribute and a target attribute value, accessing an index to determine one or more target files in which the target attribute value appears, the index including a plurality of attribute values, and for each of the attribute values, one or more files in which the attribute value appears, and retrieving data from the one or more target files.

Data warehouse indexed string token search

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.

Creating a machine learning model with k-means clustering

A method for time series forecasting includes receiving a time series forecasting query from a user requesting the data processing hardware to perform a plurality of time series forecasts. Each time series forecast is a forecast of future data based on respective current data Simultaneously, for each time series forecast of the plurality of time series forecasts requested by the time series forecasting query, the method includes training a plurality of models for the respective time series forecast. The method also includes determining which model of the plurality of models best fits the respective time series forecast and forecasting the future data based on the determined best fitting model and the respective current data. The method also includes returning, to the user, the forecasted future data for each of the plurality of time series forecasts request by the timer series forecasting query.

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Kirkland, WA, United States of America

Amir H Hormati