Cleversafe, Inc.

International Business Machines Corporation

Pure Storage, Inc.

Ocient Holdings LLC

Nec Corporation

Ocient Inc.

Packard Bell Nec

Other

Zenith Data Systems Corporation

Warren & Lewis Investment Corporation

S Christopher Gladwin

Jason K Resch

Gary W Grube

Greg R Dhuse

Andrew D Baptist

Timothy W Markison

Wesley B Leggette

Thomas Franklin Shirley, Jr

Ilya Volvovski

George Kondiles

Manish Motwani

Jason Arnold

Joseph Jablonski

Kumar Abhijeet

Vance T Thornton

Zachary J Mark

James Y Wilson

Rhett Colin Starr

Daniel Coombs

Matthew Michael England

Depeng Bi

Jamie Bellanca

John Quigley

Bart Richard Cilfone

Alan Joseph Soucy

Jose T George

Asimuddin Kazi

Niall John McShane

Srinivas Palthepu

Derick W Voegeli

Daniel A Boals

Sarah Toledano

Alan E Holmes

Dustin M Hendrickson

Jesse Louis Young

Robert Clark McCammon

Russell Paul Kennedy

Michael Cortopassi

Peng Lim

Scott Merkle

Gregory Palinckx

James Sherer

Ahmad Alnafoosi

Arif Maskatia

Steven Mark Hoffman

Lynn Foster

Dennis A Kalaf

John Allan Parham

Harry Hsiung

Gary Steven Cohen

Jerri Lynn Foster

Joshua J Mullin

Sejal Kumarbhai Modi

Dhanvi Gopala Krishna Kapila Lakshmana Harsha

Dhavi Gopala Krishna Kapila Lakshmana Harsha

Biswa R Banerjee

Chuck Wilson Templeton

Mainsh Motwani

Ravi V Khadiwala

Kenneth Douglas Wood

Trevor J Vossberg

Gil Y Wong

Bart Richard Clifone

Jeffrey Schindler

Jeff Smith

Adam D Eggum

Guoming Frank Wu

Bradley J Staff

J Richard Terrell, Ii

Paul H McCann

Ryan Joseph Kuester

John M Hauck

Kyle Swen

Ilya Volvolvski

Vance T Thomton

Anush Gopalan

A database system operates by: determining a query request corresponding to a requesting entity; generating potential query cost data based on query cost data for the query request including power consumption and further based on running query cost data for at least one previous query request corresponding to the requesting entity; generating minimum query cost compliance data for the query request based on determining whether the potential query cost data complies with a minimum query cost rule; when the minimum query cost compliance data indicates the potential query cost data complies with the minimum query cost rule: determining a query result for the query request based on execution of the query request against a database system.

Generating query cost data for query requests based on power

A method for execution by one or more computing devices of a massive data ingestion system includes receiving data for storage in the massive data ingestion system. The method further includes storing a data segment of the data in a cache memory of the massive data ingestion system. The method further includes retrieving, based on one or more retrieval parameters, the data segment from the cache memory. The method further includes error encoding, in accordance with error encoding parameters, the data segment to produce a set of encoded data slices, wherein the error encoding parameters include a pillar width number and a decode threshold number, and wherein the decode threshold number is a minimum number of encoded data slices needed to reconstruct the data segment. The method further includes storing the set of encoded data slices in a set of storage units of the massive data ingestion system.

Ingesting and storing data in a massive data ingestion system

A database system operates by: determining a query pricing scheme, wherein the query pricing scheme indicates a record valuation scheme; receiving a query request from a requesting entity that indicates access to a subset of a plurality of records in a database system; generating query cost data based on the subset of the plurality of records by utilizing the query pricing scheme and a power consumption, wherein generating the query cost data includes: calculating plurality of record valuations by calculating a record valuation for each of the plurality of records in the subset of the plurality of records, and aggregating over the plurality of record valuations to generate a query price total indicated in the query cost data; and transmitting the query cost data to the requesting entity.

Power based query processing system and methods for use therewith

A query processing system is operable to determine a set of query rules that includes a minimum result set size rule. A query is received from a requesting entity. A size of a result set of the query is determined. Compliance data is determined based on determining whether the size of the result set complies with the minimum result set size rule. When the compliance data indicates the size of the result set complies with the minimum result set size rule, a query result for the query is generated based on the result set, and the query result is generated to the requesting entity. When the compliance data indicates the size of the result set does not comply with the minimum result set size rule: transmission of the query result to the requesting entity is foregone.

Enforcement of a minimum result set size rule for queries requested for execution against a database system

A data processing system is operable to determining a query for execution and generate a query operator execution flow for the query that includes a first at least one operator serially before a second at least one operator. The first at least one operator of the query operator execution flow is executed based on generating a request for rows in accordance with an object storage communication protocol indicating filtering parameter data parameter, sending the request to an object storage system, and receiving a response indicating a filtered row set from the object storage system. The second at least one operator of the query operator execution flow is executed based on processing the filtered row set indicated in the response in accordance with the second at least one operator to produce the query resultant.

Query execution via communication with an object storage system via an object storage communication protocol

A database system is operable to determine a request to implement a K Nearest Neighbors (KNN) algorithm to generate classification data for a set of new records. A query operator execution flow is determined for the request that includes a KNN-join operator. A query resultant that indicates classification data for the set of new records is generated by performing a plurality of operator executions in accordance with the query operator execution flow based on, for each record of the set of new records, generating a plurality of similarity measures by performing a similarity function on the each record and each of a set of previously-classified records; identifying a proper subset of the set of previously-classified records that includes exactly a predefined number of records; and joining the each record with the proper subset of the set of previously-classified records.

Generating classification data via a query processing system

A user device is operable to determine a query request based on first user input to a graphical user interface displayed by the user device. Query cost data is determined based on the query request. A prompt is displayed via the graphical user interface that indicates the query cost data. Query approval data is determined based on second user input to the graphical user interface in response to the prompt. The query request is transmitted to an analytics system for execution against a database system when the query approval data indicates approval to transmit the query request.

Utilizing query approval data determined based on query cost data for a query request

A method for execution by one or more computing devices of a storage network includes error encoding data into a set of encoded data slices. The method further includes storing the set of encoded data slices in first storage units of a first vault in accordance with first error encoding parameters. The method further includes initiating storage of the set of encoded data slices in second storage units of a second vault in accordance with second error encoding parameters. When a decode threshold number of encoded data slices of the set of encoded data slices are stored in the second vault, the method further includes deleting one or more encoded data slices of the set of encoded data slices stored in the first vault.

Dynamic storage of encoded data slices in multiple vaults

A processing system of a storage network operates by receiving a write request associated with a data object; identifying dispersed storage error encoding parameters; and determining a number of available storage units. When the number of available storage units is determined to exceed a first number, the processing system operates by: determining a first write threshold number in accordance with the dispersed storage error encoding parameters; dispersed error encoding the data object to produce a first write threshold number of encoded data slices; and writing, to available storage units, the first write threshold number of encoded data slices. When the number of available storage units is determined to not exceed the first number, the processing system operates by: determining a second write threshold number in accordance with the dispersed storage error encoding parameters associated, wherein the second write threshold number is less than the first write threshold number; dispersed error encoding the data object to produce a second write threshold number of encoded data slices; and writing, to the available storage units, the second write threshold number of encoded data slices.

Facilitating write requests in a storage network

A node of a database system is operable to receive a plurality of data blocks from a set of source nodes via a set of relay nodes. At least one feedback data block to be sent to at least one of the set of source nodes is generated. Feedback relay node selection data selecting at least one of a plurality of nodes in the database system for facilitating routing of the at least one feedback data block from the node to the at least one of the set of source nodes via a prioritized feedback communication mechanism is generated. The at least one feedback data block is sent to the at least one of the plurality of nodes selected in the feedback relay node selection data. The at least one feedback data block is routed from the node to the at least one of the set of source nodes via the prioritized feedback communication mechanism.

Routing feedback data blocks via a prioritized feedback communication mechanism

A method for execution by one or more computing devices includes selecting a first routing path from a plurality of routing paths to a set of storage units based on routing path performance information, where the first routing path has a performance level greater than a first performance threshold. The method further includes selecting a second routing path from the plurality of routing paths based on the routing path performance information, where the second routing path has a performance level less than or equal to the first performance threshold. The method further includes sending a first subset of encoded data slices to the set of storage units via the first routing path for storage therein. The method further includes sending a second subset of encoded data slices to the set of storage units via the second routing path for storage therein.

Sending encoded data slices via multiple routing paths

A data segment is encrypted to produce an encrypted data segment, and a data tag associated with the data segment is generated. The encrypted data segment is encoded to generate a set of encoded data slices. At least a read-threshold number of encoded data slices are required to reconstruct the encrypted data segment. A set of write slice requests, which includes the set of encoded data slices and the data tag, is transmitted to a DSN memory. A determination is made, based on the data tag, whether a first encoded data slice of the set of encoded data slices is a duplicate of a second encoded data slice already stored within the DSN memory. If it is a duplicate, rather of storing the first encoded data slice, a reference to a location of the second encoded data slice is stored.

Efficient storage of data in a dispersed storage network

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 method includes obtaining input encoded data slices from memory of the storage network, where the input encoded data slices include a set of encoded data slices interspersed with a set of auxiliary data slices, where a data segment was error encoded into the set of encoded data slices, and where auxiliary data was error encoded into the set of auxiliary data slices. The method further includes obtaining de-selection information associated with the input encoded data slices and de-selecting the sequence of input encoded data slices based on the de-selection information to produce deselected encoded data slices. The method further includes error decoding at least a decode threshold number of encoded data slices of the deselected encoded data slices in accordance with error decoding parameters to reproduce the data segment. The method further includes outputting the data segment to a requesting computing device of the storage network.

Recovering data from encoded data slices interspersed with auxiliary data

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Chicago, IL, United States of America

S Christopher Gladwin