Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: A method for simulating participation patterns in a plurality of events from a pool of qualified participants includes selecting an event whose preference rank r is equal to a predetermined preference rank R(i,j) for an event of type j for individual i; sampling a random number Z that represents a number of events in which to participate when a number N(j) of available events of type j is greater than 0 and an expected participation rate P(i, j) of individual i for events of type j is greater than 0; selecting a random subset S with Z elements that indicates which Z events to participate in; and looping over k=1 to N(j) and setting a participation array M(i,j,k,l) to 1 iff k is contained in S, where participation array M(i,j,k,l) indicates whether or not individual i participates in a k-th event of type j in an l-th simulation.

Abstract: Mechanisms are provided, in a hierarchical feedback aggregation (HFA) system implemented in one or more data processing systems, for collecting and presenting user feedback information for a composite offering. A backend engine of the HFA system, implemented in a first data processing system, registers a hierarchical feedback model for the composite offering. A frontend engine of the HFA system, implemented in a second data processing system, receives user feedback for an identified component of the composite offering. The backend engine of the HFA system generates an aggregate user feedback score for the identified component based on a combination of the user feedback for the identified component and aggregate user feedback scores for child components of the identified component in the hierarchical feedback model. The backend engine outputs a representation of the generated aggregate user feedback score for the component to a user.

Abstract: Mechanisms are provided, in a hierarchical feedback aggregation (HFA) system implemented in one or more data processing systems, for collecting and presenting user feedback information for a composite offering. A backend engine of the HFA system, implemented in a first data processing system, registers a hierarchical feedback model for the composite offering. A frontend engine of the HFA system, implemented in a second data processing system, receives user feedback for an identified component of the composite offering. The backend engine of the HFA system generates an aggregate user feedback score for the identified component based on a combination of the user feedback for the identified component and aggregate user feedback scores for child components of the identified component in the hierarchical feedback model. The backend engine outputs a representation of the generated aggregate user feedback score for the component to a user.

Abstract: The disclosure relates to extraction of rationales for studied outcome. A method comprises: grouping features as expert to align with a set of operating practices; generating interpretable features using operating rules, combining with statistical dependence analysis to bin selected features to generate favorite practice actions; grouping features as expert that combine a subset of the interpretable features to align with a set of operating practices.

Abstract: Mechanisms are provided, in a hierarchical feedback aggregation (HFA) system implemented in one or more data processing systems, for collecting and presenting user feedback information for a composite offering. A backend engine of the HFA system, implemented in a first data processing system, registers a hierarchical feedback model for the composite offering. A frontend engine of the HFA system, implemented in a second data processing system, receives user feedback for an identified component of the composite offering. The backend engine of the HFA system generates an aggregate user feedback score for the identified component based on a combination of the user feedback for the identified component and aggregate user feedback scores for child components of the identified component in the hierarchical feedback model. The backend engine outputs a representation of the generated aggregate user feedback score for the component to a user.

Abstract: Mechanisms are provided, in a hierarchical feedback aggregation (HFA) system implemented in one or more data processing systems, for collecting and presenting user feedback information for a composite offering. A backend engine of the HFA system, implemented in a first data processing system, registers a hierarchical feedback model for the composite offering. A frontend engine of the HFA system, implemented in a second data processing system, receives user feedback for an identified component of the composite offering. The backend engine of the HFA system generates an aggregate user feedback score for the identified component based on a combination of the user feedback for the identified component and aggregate user feedback scores for child components of the identified component in the hierarchical feedback model. The backend engine outputs a representation of the generated aggregate user feedback score for the component to a user.

Abstract: A computer-implemented end-to-end system for optimizing distributed development projects may include a Software as a Service (SaaS) collecting historical project metrics. A productivity factor analyzer may perform analysis of one or more productivity factors with one or more quantifiers to define an impact of a team composition and task split on a project development. A task splitter may perform identification of one or more split points that minimize negative impacts from a geographically distributed environment in communication and team collaboration. An indifference curve identifier processing device may identify trade-offs for client metrics and develop a set of contours for different development options. A development optimizer may calculate the team composition, and task splits based on the one or more split points, the set of contours, and the impact of a team composition and task split on the project development. A SaaS service automatically allocates task assignments to corresponding target workers.

Abstract: The invention provides a method, system, and program product for differentially displaying an instant messaging (IM) availability to a plurality of potential interlocutors. In one embodiment, the invention includes creating a relationship chart of potential interlocutors based on an organizational chart; defining a willingness to communicate, including a temporal component; establishing an IM availability for each potential interlocutor using the relationship chart and the willingness to communicate; sending the IM availability to a server; transmitting the IM availability from the server to a potential interlocutor's computing device capable of displaying the IM availability; receiving an IM invitation from an interlocutor; and generating an alert based on the IM availability for the interlocutor.

Abstract: A method for simulating participation patterns in a plurality of events from a pool of qualified participants includes selecting an event whose preference rank r is equal to a predetermined preference rank R(i,j) for an event of type j for individual i; sampling a random number Z that represents a number of events in which to participate when a number N(j) of available events of type j is greater than 0 and an expected participation rate P(i, j) of individual i for events of type j is greater than 0; selecting a random subset S with Z elements that indicates which Z events to participate in; and looping over k=1 to N(j) and setting a participation array M(i,j,k,l) to 1 iff k is contained in S, where participation array M(i,j,k,l) indicates whether or not individual i participates in a k-th event of type j in an l-th simulation.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.

Abstract: Similar to other Cloud Service, Solution as Services over Cloud, as single tenant technology, also requires support of agility and flexibility as a fundamental feature of Cloud computing. Different from other Cloud services, the agility and flexibility typically are not triggered by the typical performance metrics, but at the business level of metrics. A causality analysis method, system, and non-transitory computer readable medium using a causal graph depicting relationships among observable primitive metrics from infrastructure, middleware, and business metrics and latent business metrics of an application, include identifying a metric value resulting from measuring the system and application metrics, determining an impact of the measurement of the metrics on the business metrics associated with the measurable metrics in the causal graph, and determining an action to take with respect to the impact on the business metric based on the pre-defined business policies.

Abstract: A method of managing access to a physical mailing address using a virtual mailing address is presented. The method includes: setting up a proxy system as a server, assigning, by a server, a virtual mailing address identifier to natural or juristic person that registers with the server; linking, by the server, a physical mailing address entered by the user to the virtual mailing address identifier to generate the virtual mailing address; and enabling, by the server, a second natural person to manually access the physical mailing address when the server determines that the second user has permission to access the virtual mailing address; enabling, by the server, an institute or business (juristic person) to access the physical mailing address automatically using an Application programming interface (API) through a computer system when the server determines that the second user has permission to access the virtual mailing address.

Abstract: A method of managing access to a physical mailing address using a virtual mailing address is presented. The method includes: setting up a proxy system as a server, assigning, by a server, a virtual mailing address identifier to natural or juristic person that registers with the server; linking, by the server, a physical mailing address entered by the user to the virtual mailing address identifier to generate the virtual mailing address; and enabling, by the server, a second natural person to manually access the physical mailing address when the server determines that the second user has permission to access the virtual mailing address; enabling, by the server, an institute or business (juristic person) to access the physical mailing address automatically using an Application programming interface (API) through a computer system when the server determines that the second user has permission to access the virtual mailing address.

Abstract: Mechanisms are provided, in a hierarchical feedback aggregation (HFA) system implemented in one or more data processing systems, for collecting and presenting user feedback information for a composite offering. A backend engine of the HFA system, implemented in a first data processing system, registers a hierarchical feedback model for the composite offering. A frontend engine of the HFA system, implemented in a second data processing system, receives user feedback for an identified component of the composite offering. The backend engine of the HFA system generates an aggregate user feedback score for the identified component based on a combination of the user feedback for the identified component and aggregate user feedback scores for child components of the identified component in the hierarchical feedback model. The backend engine outputs a representation of the generated aggregate user feedback score for the component to a user.

Abstract: Mechanisms are provided, in a hierarchical feedback aggregation (HFA) system implemented in one or more data processing systems, for collecting and presenting user feedback information for a composite offering. A backend engine of the HFA system, implemented in a first data processing system, registers a hierarchical feedback model for the composite offering. A frontend engine of the HFA system, implemented in a second data processing system, receives user feedback for an identified component of the composite offering. The backend engine of the HFA system generates an aggregate user feedback score for the identified component based on a combination of the user feedback for the identified component and aggregate user feedback scores for child components of the identified component in the hierarchical feedback model. The backend engine outputs a representation of the generated aggregate user feedback score for the component to a user.

Abstract: Traceability management to align IT solution artifacts with business goals in a service oriented architecture environment is provided. A pattern matching framework is provided for generating patterns and transformation enablers for architectural artifacts based on specific business requirements. Patterns that are applicable to a selected set of artifacts or model are provided to an architect, who may then select a particular subset of the patterns to apply to the set of artifacts or model in the SOA solution design to speed up and simplify the design process. Providing applicable or candidate patterns for selection to the architect in the views reduces the possible ambiguity in architectural artifact-pattern matching and selection. These solution patterns may be used as a guide when configuring and linking architectural artifacts and models.

Abstract: Techniques for quantifying the quality of trend lines. A method includes defining a collection of reference data sets derived from multiple historical projects in a similar subject area as the target project, defining one or more indicators of data set applicability to a target project estimation, said defining based on multiple characteristics of the target project, evaluating applicability of each of the reference data sets based on the one or more indicators, and identifying one or more of the reference data sets as applicable for use in the target project estimation.

Abstract: Disclosed is a novel system and method to select software components. A set of available software components are accessed. Next, one or more dimensions are defined. Each dimension is an attribute to the set of available software components. A set of coherence distances between each pair of the available software components in the set of available software components is calculated for each of the dimensions that have been defined. Each of the coherence distances are combined between each pair of the available software components that has been calculated in the set of the coherence distances into an overall coherence degree for each of the available software components. Using the overall coherence degree, one or more software components are selected to be included in a software bundle.