International Business Machines Corporation

Bitnobi, Inc.

Other

IBM Canada Limited - IBM Canada Limitee

Marin Litoiu

Mark Shtern

Bradley Simmons

Andrei Florin Solomon

Vincent Szaloky

David C Kalmuk

Jay William Benayon

Michael Starkey

Joseph Paul Wigglesworth

Shaikh Shahriar Quader

Piotr Mierzejewski

Radu Bogdan Mateescu

Emmanouil Papangelis

Margaret Storey

Derek Rayside

Mike Smit

Nicolas Andres Jaramillo Duran

Sumona Mukhopadhyay

Michael Smit

Yar Rouf

Joydeep Mukherjee

Alex Tak Lau

Methods and systems for secure, encrypted and distributed ownership and usage of big data are provided. According to one example, a server maintains a local key management data store, a data blockchain copy, an audit blockchain copy, and a metadata blockchain copy. A data operation from a user electronic device is received. The server verifies that the user electronic device has access against the local key management data store, runs the data operation and records metadata about the data operation, and writes data blocks to the data blockchain copy, the audit blockchain copy, and the metadata blockchain copy. The server broadcasts the updated blockchain copies to the peer-to-peer network for replication.

Distributed governance for sharing of big data

A cloud configuration, including public and private clouds, is organized hierarchically, with a top level and any number of lower levels. A parent level cloud receives resource workload metrics from respective child level cloud(s), makes predictions, based in part on the metrics, as to future resource needs at the child level. The parent level sets up runtime-modifiable business rules and scaling plans based on the predictions. The parent level cloud sends the scaling plans to respective child level(s). The parent level automatically triggers a scaling plan at a child level, if conditions written into the business rules are satisfied. Resources are scaled upward or downward automatically as needed to maintain optimal resource usage.

Predictive auto-scaler for a hierarchical computing infrastructure

A DBMS training subsystem trains a DBMS workload-manager model with training data identifying resources used to execute previous DBMS data-access requests. The subsystem integrates each request's high-level features and compile-time operations into a vector and clusters similar vectors into templates. The requests are divided into workloads each represented by a training histogram that describes the distribution of templates associated with the workload and identifies the total amounts and types of resources consumed when executing the entire workload. The resulting knowledge is used to train the model to predict production resource requirements by: i) organizing production queries into candidate workloads; ii) deriving for each candidate a histogram similar in form and function to the training histograms; iii) using the newly derived histograms to predict each candidate's resource requirements; iv) selecting the candidate with the greatest resource requirements capable of being satisfied with available resources; and v) executing the selected workload.

Learning-based workload resource optimization for database management systems

An ecosystem that allows fine-grained multi-party control over access to information stored in one or more data sources of a data provider. A requesting party can submit a query job to the data provider. Resellers in the chain can introduce their own query modifiers to the query job, adding additional data access, data transformation and segmentation functions to the query job. The data provider can append its own query modifier to the query job and execute the query job with all of the query modifiers. Access control for each link in the chain is checked before the query modifier for the link is executed. After execution of all query modifiers and the query job, the results can be provided to the requesting party.

Systems and methods of precision sharing of big data

A method and system for visualizing process flows for business and manufacturing processes are described that includes a first view of the process as a tree showing the process elements and their interconnections, and a second view of the process in the form of a zoomable interface that displays selected nodes at magnifications that are continuously variable at the user's option. A selected node in the tree view opens the same node in the zoomable node view. When a second node is selected in the tree view, the zoomable node view zooms from the first selected node to the second selected node through each node intermediate between the first and second selected nodes. This enables a process analyst to view the content of selected process nodes in the context of nodes with which the selected nodes are associated and thus to have a thorough understanding of the process.

System and method for visualizing process flows

In the process of hierarchical composition of software component types, the reusability of software component types is improved through the 'promotion of features'. That is, a feature of an instance of a predetermined software component type may be promoted to a software component type containing instances of the predetermined software component type. The promoted feature may then be customized when the containing software component type is instantiated.

Promotion of features in reusable software component types

Growing community of inventors

Toronto, Canada

Marin Litoiu