Ocient Inc.

Other

Rhett Colin Starr

George Kondiles

Joseph Jablonski

S Christopher Gladwin

A method for execution by one or more database storage nodes of a database management system includes obtaining a row of data that includes a plurality of columns. The method further includes generating a data confidence value for data within one or more columns of the plurality of columns. The method further includes storing the data confidence value in a memory of the database management system.

Confidence-based database management systems and methods for use therewith

A networked database management system (DBMS) is disclosed. In particular, the disclosed DBMS includes a plurality of nodes, one of which is elected as a designated leader. The designated leader is elected using a consensus algorithm, such as tabulated random votes, RAFT or PAXOS. The designated leader is responsible for managing open coding lines, and determining when to close an open coding line.

Database system with coding cluster and methods for use therewith

A computing device includes a plurality of nodes, where a first node operates in accordance with a computing device operation system (OS) and remaining nodes operate in accordance with a custom OS. The remaining nodes include a plurality of sets of processing core resources that process a plurality of sets of threads of an application. The computing device also includes a main memory divided into a computing device memory section and a custom memory section that includes portions logically allocated as a plurality of buffers. The computing device also includes a memory access control module operable to coordinate access to the plurality of buffers by at least some of the plurality of sets of threads in accordance with the custom OS. The computing device also includes disk memory and a disk memory access control module operable to coordinate access to the disk memory in accordance with the computing device OS.

Coordinating main memory access of a plurality of sets of threads

A payload store within a database management system includes a first set of nodes that include a first node that includes a memory, a network interface, a storage device, and a processing unit. The processing unit receives, via the network interface, a first portion of data to be stored in a plurality of storage devices of the first set of nodes, and splits the first portion into a first set of data rows that includes a plurality of subsets of data rows and a last subset of data rows. The processing unit further randomly assigns the plurality of subsets of data rows and the last subset of data rows between the first set of nodes and stores a first number of data rows in the storage device. The processing unit further sends, via the network interface, a second number of data rows to the second node for storage therein.

Randomized data distribution in highly parallel database management system

A method for execution by a computer of a database management system includes obtaining a dataset that includes a set of data records, where the data set is associated with a set of data characteristics. The method further includes ranking analytical calculations of a list of analytical calculations that are able to be executed on the dataset based on the set of data characteristics and a set of analytical calculation characteristics associated with the list of analytical calculations to produce a ranked list of analytical calculations. The method further includes selecting an analytical calculation from the ranked list of analytical calculations based on the ranking. The method further includes executing the selected analytical calculation on the dataset to produce an analytical calculation result.

Efficient database management system and method for use therewith

A method by one or more processing units of a database management system includes determining to transition a collection of data from storage in a set of first temperature storage nodes to storage in a set of second temperature nodes based on one or more criteria associated with the collection of data, where each first temperature storage node of the set of first temperature storage nodes is paired with a corresponding second temperature storage node of the set of second temperature storage nodes to form a set of parallel node pairs. The method further includes transitioning storage of the collection of data in parallel, by each parallel node pair of the set of parallel node pairs, from storage in the set of first temperature storage nodes to storage in the set of second temperature storage nodes.

Data transition in highly parallel database management system

Database management system with coding cluster and methods for use therewith

A method for managing data storage and retrieval and operated within a database management system includes retrieving a set of confidence data records from memory of the database management system, where a confidence data record of the set of confidence data records includes a set of data columns and one or more confidence columns populated with one or more data confidence values associated with data within one or more data columns of the set of data columns. The method further includes analyzing the set of confidence data records to produce a result, where the result includes a confidence level that is based on corresponding ones of the one or more data confidence values associated with the data within one or more data columns of the set of data columns.

Database management systems for managing data with data confidence

Database system with designated leader and methods for use therewith

A massively parallel database management system includes an index store and a payload store including a set of storage systems of different temperatures. Both the index store and the storage system each include a list of clusters. Each cluster includes a set of nodes with storage devices forming a group of segments. Nodes and clusters are connected over high speed links. Each cluster receives data and splits the data into data rows based on a predetermined size. The data rows are randomly and evenly distributed between all nodes of the cluster.

Database management system and methods for use therewith

A method for managing data storage and retrieval and operated within a database management system includes determining whether a data confidence value of a data record that is stored or is to be stored in memory of the database management system is less than a data confidence threshold, where the data confidence value includes one or more of an estimate of an accuracy of data within the data record, an estimate of the accuracy of the data record, and an estimate of a reliability level of the data. When the data confidence value is less than the data confidence threshold, the method continues by generating a confidence data record based on the data record and the data confidence value. The method continues by storing the confidence data record in memory of the database management system.

A massively parallel database management system includes an index store and a payload store including a set of storage systems of different temperatures. Both the stores each include a list of clusters. Each cluster includes a set of nodes with storage devices forming a group of segments. Nodes and clusters are connected over high speed links. The list of clusters within the payload store includes clusters of different temperatures. The payload store transitions data of a segment group from a higher temperature to a segment group in a lower temperature cluster in parallel. A node moves data of a segment in the higher temperature cluster to a corresponding node's segment in the lower temperature cluster. Once the data is written in the destination segment in the lower temperature cluster, the source segment is freed to store other data. The temperatures include blazing, hot, warm and cold.

A database management system operates by: obtaining a dataset from a memory of the database management system, wherein the dataset includes a set of data records; determining a set of data characteristics of the dataset; determining a list of analytical calculations that are able to be executed on the dataset; determining an analytical calculation characteristic for each analytical calculation of the list of analytical calculations to produce a set of analytical calculation characteristics, wherein the analytical calculation characteristic indicates an estimated execution time to perform the analytical calculation; ranking each analytical calculation of the list of analytical calculations based on the set of data characteristics and the set of analytical calculation characteristics to produce a ranked list of analytical calculations; selecting an analytical calculation from the ranked list of analytical calculations based on the ranking; and executing the selected analytical calculation on the dataset to produce an analytical calculation result.

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Long Grove, IL, United States of America

Rhett Colin Starr