Abstract: Embodiments of the invention are directed a computer-implemented method for assessing data quality. A non-limiting example of the computer-implemented method includes using a processor to receive a plurality of updates to data points in a data stream. The processor is further used to compute instances of a data quality metric (DQM) from the data points in the data stream. The instances of the DQM are configured to differentiate the data points in the data stream by time and assign a higher weight to the instances of the DQM computed from more recent data points in the data stream. The instances of the DQM are continuously updated as more of the data points are received by the processor while limiting cycles of the processor consumed by updating the instances of the DQM to a threshold.

Abstract: A hybrid sensor can be generated by training a machine learning model, such as a neural network, based on a training data set. The training data set can include a first time series of upstream sensor data having forward dependence to a target variable, a second time series of downstream sensor data having backward dependence to the target variable and a time series of measured target variable data associated with the target variable. The target variable has measuring frequency which is lower than the measuring frequencies associated with the upstream sensor data and the downstream sensor data. The hybrid sensor can estimate a value of the target variable at a given time, for example, during which no actual measured target variable value is available.

Abstract: Context-awareness in preventative maintenance is provided by receiving sensor data from a plurality of monitored systems; extracting a first plurality of features from a set of work orders for the monitored systems, wherein individual work orders include a root cause analysis for a context in which a nonconformance in an indicated monitored system occurred; predicting, via a machine learning model, a nonconformance likelihood for each monitored system based on the first plurality of features; selecting a subset of alerts based on predicted nonconformance likelihoods for the monitored systems; in response to receiving a user selection from the first set of alerts and a reason for the user selection, recording the reason as a modifier for the machine learning model; and updating the machine learning model to predict the subsequent nonconformance likelihoods using a second plurality of features that excludes the additional feature identified from the first plurality of features.

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: 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 system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.

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: An apparatus and method for optimizing a process, comprising: receiving live operational data associated with a plurality of sub-processes of a process; selecting a pre-trained regression model from a plurality of pre-trained regression models for each sub-process of the plurality of sub-processes; generating a system-wide optimization model comprising a multi-period mathematical program model, including: one or more decision variables; a plurality of constraints, wherein: a first constraint of the plurality of constraints comprises one of the pre-trained regression models, and a second constraint of the plurality of constraints comprises an operational constraint; and an objective function; generating, via the optimization model, an operating mode trajectory comprising a plurality of intermediate operating modes at a plurality of intermediate times during a planning interval; and displaying a set-point trajectory recommendation in a graphical user interface based on the operating mode trajectory.

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.