Emc Corporation

Oath Inc.

Verizon Media Inc.

Excalibur Ip, LLC

Guangxin Yang

Caleb E Welton

Yi Yang Ren

Jianwang Ao

Xinyu Wang

Wei Liang

Sihan Ge

Dongyue Wang

Shuo Yang

Shengwen Yang

Ji Zhou

Yan Xia

Delu Zhu

The present teaching relates to request management and data recovery in a data system. In one example, a failure in connection with first data is detected at a first node in a data system. Information associated with a most recent transaction related to the first node is obtained from the persistent storage. Each of other nodes in the data system is requested to transmit one or more transaction requests previously sent to the first node after the most recent transaction. The one or more transaction requests are received from at least one of the other nodes. A sequence of one or more transactions associated with the one or more transaction requests is determined. The one or more transactions are executed according to the sequence in order to recover the first data at the first node.

Method and system for data recovery in a data system

The present teaching relates to scheduling transactions at a node in a data system. In one example, a request is obtained for performing a first transaction at the node. A mixing condition associated with the first transaction is determined. The first transaction is initiated. The first transaction is suspended when the mixing condition is met. A second transaction is carried out at the node. The first transaction and the second transaction are directed to a same portion of data at the node.

Method and system for scheduling transactions in a data system

The present teaching relates to managing a request in a data system of a plurality of nodes in a network. In one example, a request is received for a transaction of data by a first node in the data system. A second node in the data system is determined based on the request, where the second node is to carry out the transaction. The request is stored into a first request queue at the first node. The request is sent to the second node. A notice is obtained by the first node indicating that information associated with the transaction has been persistently stored. The request is removed from the first request queue in response to the notice being obtained.

A method and apparatus for generating a signal processing pipeline based, at least in part, on manipulation of a GUI, the signal processing pipeline comprising one or more signal processing operations.

Method and/or apparatus for generating signal processing pipelines

Latent Dirichlet allocation (LDA) analysis on a dataset is performed on an MPP relational database by distributing subsets of said dataset to a plurality of segments of the MPP database, and performing LDA analysis in parallel on the respective subsets on the plurality of segments using Gibbs sampling. An object library on each segment provides executable objects of user defined functions that can be called by an SQL query when the query requires functionality provided by an object.

Highly scalable memory-efficient parallel LDA in a shared-nothing MPP database

A method, article of manufacture, and apparatus for process data. In some embodiments, this includes receiving a desired sample size from a user, increasing the desired sample size based on a policy, using the increased desired sample size to determine a number of samples to take from a database segment, wherein the database segment includes a portion of a dataset, generating a sub-dataset based on the determined number of samples to take from the database segment, wherein the sub-dataset only includes identifiers, joining the generated sub-dataset with the dataset, and storing the joined dataset in a storage device.

Policy based over sampling with replacement

A method, system, and process for representing a decision tree in a tabular format is discussed. The format may contain all the necessary information to traverse the nodes in parallel on a distributed system while consuming an efficient amount of resources. In some embodiments, the tree may be stored in a relational database as a table.

Decision tree representation for big data

A method, article of manufacture, and apparatus for process data. In some embodiments, this includes determining a dataset, wherein the dataset includes a plurality of data values and a plurality of identifiers, and wherein the plurality of identifiers are in a non-continuous order, and wherein the dataset is stored across a number of segments, determining a number of samples to take from the dataset, identifying a minimum identifier and a maximum identifier, and a total number of identifiers, calculating a density based on the plurality of identifiers, minimum identifier and maximum identifier, calculating a number of samples to take from a segment based on the calculated density, the number of samples to take from the dataset, and the number of segments, generating a sub-dataset based on the calculated number of samples to take from a segment, joining the generated sub-dataset with the dataset, and storing the joined dataset in a storage device.

Efficient sampling with replacement

A method, system, and process for generating attribute class statistics for a decision tree is discussed. In an embodiment the ACS's are calculated in parallel on a distributed architecture, and may consume a constant amount of resources.

Generating attribute-class-statistics for decision trees

A method, system, and process for classifying a dataset using a decision tree is discussed. The decision tree may be stored in a novel format on a distributed architecture, and the dataset classification may be performed in parallel on the distributed architecture.

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Beijing, China

Guangxin Yang