Emc Corporation

Pivotal Software, Inc.

Emc IP Holding Company LLC

Caleb E Welton

Jeffrey Ira Cohen

Hitoshi Harada

Radhika Reddy

Tao Ma

Lei Chang

Zhanwei Wang

Shengwen Yang

Gavin Sherry

Guangxin Yang

Yi Yang Ren

Hai Qian

Rahul Iyer

Jianwang Ao

Florian Schoppmann

King Lun Choi

Gavin John Sherry

Milena Bergant

Luke Lonergan

Xixuan Feng

Alon Goldshuv

Kurt Harriman

Brian C Hagenbuch

Kenneth A Sell

Charles F McDevitt

Kenneth E Sell

A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, and the master node may distribute this metadata with query plans to worker nodes. The worker nodes may request additional metadata from the master node when necessary.

Parallel processing database system with a shared metadata store

A method and system for executing database queries in parallel using a shared metadata store. The metadata store may reside on a master node, where the master node is the root node in a tree. The master node may distribute query plans and query metadata to other nodes in the cluster. These additional nodes may request additional metadata from each other or the master nodes as necessary.

Parallel processing database system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for parallel time series analysis. One of the methods includes receiving a plurality of data elements. The plurality of data elements are partitioned into a plurality of chunks, wherein the plurality of chunks, including a first chunk and a plurality of additional chunks, have an ordering according to the data elements included in each chunk. Each chunk is assigned to a particular segment of a plurality of segments. A first iteration of an autoregressive integrated moving average is computed for each chunk assigned to each segment. A second iteration of the autoregressive integrated moving average is computed for each chunk assigned to each segment, wherein computing uses the result data for a corresponding preceding chunk in the first iteration. One or more additional iterations of the autoregressive integrated moving average are computed until stopping criteria has been satisfied.

Parallel time series analysis

A method and system for executing a query in parallel is disclosed. A master node may receive a query from a client and develop query plans from that query. The query plans may be forwarded to worker nodes for execution, and each query plan may be accompanied by query metadata. The metadata may be stored in a catalog on the master node.

Processing a database query using a shared metadata store

Method of providing access controls and permissions over relational data stored in a hadoop file system

A random number generation process generated uncorrelated random numbers from identical random number sequences on parallel processing database segments of an MPP database without communications between the segments by establishing a different starting position in the sequence on each segment using an identifier that is unique to each segment, query slice information and the number of segments. A master node dispatches a seed value to initialize the random number sequence generation on all segments, and dispatches the query slice information and information as to the number of segments during a normal query plan dispatch process.

Random number generator in a parallel processing database

Performing data analytics processing in the context of a large scale distributed system that includes a massively parallel processing (MPP) database and a distributed storage layer is disclosed. In various embodiments, a data analytics request is received. A plan is created to generate a response to the request. A corresponding portion of the plan is assigned to each of a plurality of distributed processing segments, including by invoking as indicated in the assignment one or more data analytical functions embedded in the processing segment.

Data analytics platform over parallel databases and distributed file systems

A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.

General framework for cross-validation of machine learning algorithms using SQL on distributed systems

A random number generation process generated uncorrelated random numbers from identical random number sequences on parallel processing database segments of an MPP database without communications between the segments by establishing a different starting position in the sequence on each segment using an identifier that is unique to each segment, query slice information and the number of segments. A master node dispatches a seed value to initialize the random number sequence generation on all segments, and dispatches the query slice information and information as to the number of segments, during a normal query plan dispatch process.

A specified data type and pre-storage operation are receive. In response, an association between the two is created in storage metadata. After the association is created, data to be stored is received where the data has a data type. In response to receiving the data, the storage metadata, including the association, is accessed and it is determined if the data type of the data is the same as the specified one. If so, the specified pre-storage operation is automatically performed on the data in order to obtain an output and the output is stored.

Declaring directives in a data store

Growing community of inventors

Foster City, CA, United States of America

Caleb E Welton