International Business Machines Corporation

Mark David Feblowitz

Anton Viktorovich Riabov

Zhen Liu

Anand Ranganathan

Nagui Halim

Eric P Bouillet

Kay Schwendimann Anderson

Octavian Udrea

Shirin Sohrabi Araghi

Genady Grabarnik

Grant W Wagner

Angela Marie Schuett

Yuan-Chi Chang

Deepak Srinivas Turaga

Pankaj Rohatgi

Shai Halevi

Joseph Phillip Bigus

Ronald Perez

Paul Ashley Karger

Pau-Chen Cheng

Michael Steiner

Trent Ray Jaeger

Edward John Pring

Peter Zimmer

Fang Yuan

Edward W Shay

Stuart S Horn

Parijat Dube

Kavitha Srinivas

Ramakrishnan Srikant

Oktie Hassanzadeh

David Alson George

Ralf Rantzau

Alexandre Valentinovich Evfimievski

Hanhua Feng

Genady Ya Grabarnik

Michael Katz

Manik Bhandari

Embodiments are provided that relate to a computer system, a computer program product, and a computer-implemented method for automating scenario planning. Embodiments involve machine learning (ML) and an artificial intelligence (AI) planner to capture a general scenario planning (GSP) problem and provide a solution to the GSP problem in the form of trajectories.

Method and system for automating scenario planning

A computer-implemented method is provided that includes accessing candidate text and a candidate pair including first and second phrases, substituting the first and second phrases into cause-effect patterns to generate variant sentences. An artificial intelligence model is leveraged to determine respective probabilities that the variant sentences are inferred from the candidate text, calculate a statistical measure of the respective probabilities, and assess the calculated statistical measure to ascertain whether the first and second phrases possess a causal relationship or non-causal relationship to one another. A knowledge base including one or more pairs of cause-effect phrase pairs is populated with the first and second phrases possessing the causal relationship. A computer system and a computer program product are also provided.

Causal knowledge identification and extraction

Techniques for scenario planning are provided. In one example, a computer-implemented method can comprise analyzing, by a device operatively coupled to a processor, content using a topic model. The content can be associated with a defined source and is related to one or more current events. The computer-implemented method can also comprise determining, by the device, one or more portions of the analyzed content that are relevant to one or more key risk drivers using a risk driver model. The computer-implemented method can also comprise aggregating, by the device, the determined one or more portions into one or more emerging storylines based on values of one or more attributes of the topic model.

Scenario planning and management

Scenario planning and risk management

A method for automated composition of an application including: receiving a customizable template for application composition and a composition goal, wherein the goal comprises a plurality of tags and the goal is incomplete such that more than one possible composition matches the goal; refining the goal by automatically adding refinement tags to the goal; and generating an application flow that matches the customizable template and the refined goal, wherein the application flow comprises data sources, data processing operators, and outputs of the application flow.

Automated software composition

A method and computer program product for assembling and deploying multi-platform flow based applications. An information processing flow that produces a result is assembled, the information processing flow includes components connected by data links, a component includes software code that describes at least one of an input constraint or an output constraint of the component, and at least two of the components are deployable on different computing platforms. The information processing flow is partitioned into sub-flows, such that for each sub-flow every component in the sub-flow is deployable on the same computing platform. The sub-flows are deployed on their respective computing platforms.

Assembly and deployment of multi-platform flow-based applications

A method for assembling an information processing application, includes: inputting a plurality of component descriptions, wherein each of the component descriptions includes a graph pattern that semantically describes an applicability condition of a component and a graph pattern that semantically describes an inclusion effect of the component; inputting a processing request, wherein the processing request includes a goal that is represented by a graph pattern that semantically describes a desired processing outcome; assembling a processing graph, wherein the processing graph includes at least one component that satisfies the desired processing outcome; and outputting the processing graph.

Method and system for assembling information processing applications based on declarative semantic specifications

A stream processing system for processing a routing request specifying a first location and a destination location in a network of interconnected locations includes a plurality of data processing elements, a memory connected to the plurality of data processing elements for storing a plurality of maps having different scopes, wherein each of the data processing elements is associated with one of the plurality of maps and determines a section of a path from the first location to the destination location in the network of interconnected locations, and a routing selection element for merging the sections of the path as a solution to the routing request.

Stream processing based intelligent transport systems

A system and method for processing an information unit/packet (IU) in a stream processing system includes decomposing an IU into sub-information units persisted other than in the IU. An index or reference is generated in the IU for retrieving the persisted sub-information units during processing.

Method and system for secure lightweight stream processing

A method, including: providing a computer menu from which a processing goal can be created, wherein the menu includes a plurality of facets, and wherein each facet includes at least one tag; receiving a processing goal, wherein the processing goal includes a plurality of tags selected from the menu; executing at least one application that includes a plurality of components arranged in a processing graph to produce information that satisfies the processing goal; and outputting the information.

Describing formal end-user requirements in information processing systems using a faceted, tag-based model

A method, including: receiving a software requirement; and constructing a workflow template that can satisfy the software requirement, wherein the workflow template comprises a plurality of processing stages, wherein each processing stage includes at least one component class and each component class includes at least one component, and wherein an output of each processing stage is described by a processing goal pattern that is described by a set of tags and facets.

Faceted, tag-based approach for the design and composition of components and applications in component-based systems

A method for assembling processing graphs in an information processing system, includes: performing, in an offline manner, translating a plurality of component descriptions into a planning language and performing reasoning on the plurality of component descriptions during the translation; and performing, in an online manner, receiving a processing request that specifies a desired processing outcome; translating the processing request into a planning goal; and assembling a plurality of processing graphs, each of the processing graphs including a plurality of the translated and reasoned components that satisfy the desired processing outcome.

Method and system for automatically assembling processing graphs in information processing systems

A method for constructing a processing request so that an information processing application satisfying the processing request can be assembled, includes: inputting a processing request, wherein the processing request includes a goal that is represented by a graph pattern that semantically describes a desired processing outcome; and assembling a processing graph that includes at least one component that satisfies the desired processing outcome, wherein the processing graph is assembled by associating the graph pattern that semantically describes the desired processing outcome with a graph pattern that semantically describes an applicability condition of the at least one component or a graph pattern that semantically describes an inclusion effect of the at least one component.

Method for declarative semantic expression of user intent to enable goal-driven information processing

A method for assembling a stream processing application in which data source descriptions, component descriptions and a stream processing request are input and used to assemble a stream processing graph. Each of the data source descriptions includes a graph pattern that semantically describes an output of a data source, each of the component descriptions includes a graph pattern that semantically describes an input of a component and a graph pattern that semantically describes an output of the component, the stream processing request includes a goal that is represented by a graph pattern that semantically describes a desired stream processing outcome and the stream processing graph includes at least one data source or at least one component that satisfies the desired processing outcome.

Method and system for composing stream processing applications according to a semantic description of a processing goal

An automatic planning system is provided for stream processing workflow composition. End users provide requests to the automatic planning system. The requests are goal-based problems to be solved by the automatic planning system, which then generates plan graphs to form stream processing applications. A scheduler deploys and schedules the stream processing applications for execution within an operating environment. The operating environment then returns the results to the end users.

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Winchester, MA, United States of America

Mark David Feblowitz