Abstract: 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.

Abstract: A method includes generating, by a computing device, a probe; deploying, by the computing device, the probe into a virtual machine hosting a cloud application; increasing, by the computing device, resource consumption by the probe; collecting, by the computing device, performance data from the cloud application; and training, by the computing device, a machine learning model using the performance data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems for controlled data sharing are provided. According to one example, a data provider defines one or more data policies and allows access to data to one or more data consumers. Each data consumer submits analytics tasks (jobs) that include two phases: data transformation and data mining. The data provider verifies that data is trans-formed (e.g., anonymized) according to the data policies. Upon verification, the data consumer is provided with access to the results of the data mining phase. An ecosystem of data providers and data consumers can be loosely coupled through the use of web services that permit discovery and sharing in a flexible, secure environment.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An online simulation model optimization receives data representative of a business process captured in real time to form instance metrics, aggregates the instance metrics to form aggregated instance metrics, and uses a particle filter for filtering the aggregated instance metrics to form calibrated data. The process iteratively computes an output value using the calibrated data, by a simulation model. Responsive to a determination that the output value is not within a predetermined tolerance of an error threshold, the process adjusts a weight previously assigned to an aggregated instance metric by the particle filter to form recalibrated data, whereby the recalibrated data is submitted to the simulation model for computation. Responsive to a determination that the output value is within the predetermined tolerance, the process sends a result to a correction selection process of a business process optimizer, the result comprising the output value, the calibrated data, and/or the recalibrated data.

Abstract: 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.

Abstract: A business process (BP) adaptation system (400) includes a High-Variability enriched Goal Model (HVGM) (402) that captures and refines goals of a business process (BP) while modeling alternative options where the model captures non-functional or quality attributes used in an evaluation of a performance of the BP and an estimation of how various BP alternatives affect the quality attributes. The system further includes a High-Variability workflow-level/directly Executable Model (HVEM) (404), where the system is based on goal modeling and analysis for eliciting intentions behind a BP to achieve a desired goal and the HVGM explicitly models non-functional or quality concerns. The system can further include a semi-automatic generator (604) of BP metrics based on the quality attributes specified in the HVGM and a runtime infrastructure (610) where each deployed BP instance reflects a configuration selected for that instance in a corresponding portion of the HVGM.

Abstract: 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.

Abstract: 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.

Abstract: 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.