Christopher Cuong T. Nguyen

Sequence data, such as time series data is analyzed using neural networks, for example, recurrent neural networks. The sequence data is obtained from a source. For example, a sequence data may represent time series data obtained from a sensor. As another example, the sequence of data may represent a sequence of user interactions performed by a user with an online system. The sequences of data are provided as input to a neural network. A feature vector representation of each input sequence data… Show more

Sequence data, such as time series data is analyzed using neural networks, for example, recurrent neural networks. The sequence data is obtained from a source. For example, a sequence data may represent time series data obtained from a sensor. As another example, the sequence of data may represent a sequence of user interactions performed by a user with an online system. The sequences of data are provided as input to a neural network. A feature vector representation of each input sequence data is extracted from the neural network. The feature vector representation is used for clustering the sequence data. Salient features of clusters of sequence data are determined. The salient features of clusters of sequence data are provided for display via a user interface. Show less

Training datasets are determined for training neural networks. An input dataset comprising a plurality of samples is provided as training dataset to the neural network. Vector representations of samples of the input dataset are obtained from a hidden layer of the neural network. The samples are clustered using the vector representation. The samples are scored based on a metric that indicates the similarity of the sample to its cluster. A subset of samples is determined by excluding samples that… Show more

Training datasets are determined for training neural networks. An input dataset comprising a plurality of samples is provided as training dataset to the neural network. Vector representations of samples of the input dataset are obtained from a hidden layer of the neural network. The samples are clustered using the vector representation. The samples are scored based on a metric that indicates the similarity of the sample to its cluster. A subset of samples is determined by excluding samples that have high similarity with their clusters. The subset of samples is labelled and used for training the neural network. Show less

A system represents data as visual distributed data frames (VDDFs) that comprise a dataset, metadata describing the data, and metadata describing visualization of the dataset. A VDDF may be extracted from charts displayed in markup language documents. A VDDF may be generated from different data sources including big data analysis systems. A VDDF workspace allows interaction with multiple VDDF objects extracted from multiple data sources and stored locally within the storage of the device. The… Show more

A system represents data as visual distributed data frames (VDDFs) that comprise a dataset, metadata describing the data, and metadata describing visualization of the dataset. A VDDF may be extracted from charts displayed in markup language documents. A VDDF may be generated from different data sources including big data analysis systems. A VDDF workspace allows interaction with multiple VDDF objects extracted from multiple data sources and stored locally within the storage of the device. The VDDF workspace allows the user to interact with the VDDF objects, for example, by inspecting the metadata, modifying the data, adding new columns, changing the visualization, joining data from multiple charts, and sharing the VDDF objects with other documents. The processing of data of a VDDF is performed locally within a computing device, for example, in a client device. Show less

A predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score. Responsive to the anomaly score exceeding a threshold value, the server may issue an alert. The machine learning model may be supervised or unsupervised. In one embodiment, the machine learning model use several sensor channels to predict the values of one or more vitals of the equipment and compare the predicted values to the actual measured… Show more

A predictive maintenance server receives data from sensors of equipment. The server uses one or more machine learning models to assign an anomaly score. Responsive to the anomaly score exceeding a threshold value, the server may issue an alert. The machine learning model may be supervised or unsupervised. In one embodiment, the machine learning model use several sensor channels to predict the values of one or more vitals of the equipment and compare the predicted values to the actual measured values of the vitals. The server may assign an anomaly score based on the differences between the predicted values and the measured values. In one embodiment, the machine learning model may be an autoencoder that generates a distribution of the measurement values to determine the likelihood of observing the actual measured values in a normal operation. In one embodiment, the server may use a histogram approach to predict anomaly. Show less

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis… Show more

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis system stores natural language query templates that represent natural language queries associated a particular intent. For example, a natural language query template may represent queries that compare two columns of a dataset. The data analysis system compares an input prefix of natural language with the natural language query templates to determine the suggestions. The data analysis system receives user defined metrics or attributes that can be used in the natural language queries. Show less

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis… Show more

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis system stores natural language query templates that represent natural language queries associated a particular intent. For example, a natural language query template may represent queries that compare two columns of a dataset. The data analysis system compares an input prefix of natural language with the natural language query templates to determine the suggestions. The data analysis system receives user defined metrics or attributes that can be used in the natural language queries. Show less

A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be… Show more

A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be communicated from one client application to another application using communication mechanisms outside the data analysis system, for example, by email, thereby allowing other client devices to interact with a particular in-memory distributed data structure. The in-memory distributed data structure can be a machine learning model that is trained by one client device and executed by another client device. A client application can interact with the in-memory distributed data structure using different programming languages.
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A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be… Show more

A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be communicated from one client application to another application using communication mechanisms outside the data analysis system, for example, by email, thereby allowing other client devices to interact with a particular in-memory distributed data structure. The in-memory distributed data structure can be a machine learning model that is trained by one client device and executed by another client device. A client application can interact with the in-memory distributed data structure using different programming languages.
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A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be… Show more

A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be communicated from one client application to another application using communication mechanisms outside the data analysis system, for example, by email, thereby allowing other client devices to interact with a particular in-memory distributed data structure. The in-memory distributed data structure can be a machine learning model that is trained by one client device and executed by another client device. A client application can interact with the in-memory distributed data structure using different programming languages.
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A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be… Show more

A data analysis system stores in-memory representation of a distributed data structure across a plurality of processors of a parallel or distributed system. Client applications interact with the in-memory distributed data structure to process queries using the in-memory distributed data structure and to modify the in-memory distributed data structure. The data analysis system creates uniform resource identifier (URI) to identify each in-memory distributed data structure. The URI can be communicated from one client application to another application using communication mechanisms outside the data analysis system, for example, by email, thereby allowing other client devices to interact with a particular in-memory distributed data structure. The in-memory distributed data structure can be a machine learning model that is trained by one client device and executed by another client device. A client application can interact with the in-memory distributed data structure using different programming languages. Show less

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis… Show more

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis system stores natural language query templates that represent natural language queries associated a particular intent. For example, a natural language query template may represent queries that compare two columns of a dataset. The data analysis system compares an input prefix of natural language with the natural language query templates to determine the suggestions. The data analysis system receives user defined metrics or attributes that can be used in the natural language queries. Show less

A magnetometer or magnetic field sensor includes semiconductor material deposited laterally on an insulating substrate. The semiconductor material is alternating regions of n- and p-type silicon provided with two cathodes, an anode and a triggering node. Upon application of a triggering pulse to a switch on the sensor, a carrier domain is formed. In the presence of a magnetic field this carrier domain is deflected to one side thus causing an imbalance in the current collected at the two… Show more

A magnetometer or magnetic field sensor includes semiconductor material deposited laterally on an insulating substrate. The semiconductor material is alternating regions of n- and p-type silicon provided with two cathodes, an anode and a triggering node. Upon application of a triggering pulse to a switch on the sensor, a carrier domain is formed. In the presence of a magnetic field this carrier domain is deflected to one side thus causing an imbalance in the current collected at the two cathodes.
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