International Business Machines Corporation

Richard Thomas Goodwin

Pietro Mazzoleni

Biplav Srivastava

Juhnyoung Lee

Rakesh Mohan

Sweefen Goh

Anca Andreea Ivan

Aubrey J Rembert

Anca-Andreea Ivan

Ying Li

Pu Huang

Thomas Dean Rosinski

Igor Anatolyevich Naumov

Vibha Singhal Sinha

Julian Timothy Dolby

Pankaj Dhoolia

Debdoot Mukherjee

John George Vergo

Senthil K Mani

Manisha Dattatraya Bhandar

Rajesh Chopra

Stephen K Kibby

Manas R Kumar Singh

Maharshi H Desai

George A Schroeder

Kathleen Byrnes

Jenny S Li

Ashish Jagmohan

Krishna Chaitanya Ratakonda

Sara H Basson

Hui Lei

Youngja Park

Rama Kalyani T Akkiraju

Hui Guo

Anand Ranganathan

Rema Ananthanarayanan

Aditya Vempaty

Richard B Segal

Petar Ristoski

Fenno F Heath, Iii

Kaan Kudsi Katircioglu

Vijay Sourirajan Iyengar

Flavio Du Pin Calmon

Anil Nigam

David B Flaxer

Mark William Hubbard

Rama K Akkiraju

Gerhard Sigl

Meir M Laker

HuaFang Tan

Jacob T Vandergoot

Robin Lougee

Nitinchandra R Nayak

Rama Kalyani Tirumala Akkiraju

Mangala Gowri, Iii

Hazem Elmeleegy

Charbak Chatterjee

Kimberly C Lang

Jing Fu

Christian Harris

Tenzin Yeshi

Kember A-r Forcke

Dennis A Conrad

SweeFen Goh

RamaKalyani T Akkiraju

Janette S J Wong

Balasingham Balakumaran

Yi (Andy) Zhuang

A method, system, and computer program product for representational learning of product formulas are provided. The method accesses a set of product formulas. Each product formula includes a set of ingredient tuples. A directed graph is generated from the set of product formulas. The directed graph including a node for each ingredient of the sets of ingredient tuples of the set of formulas. The method generates a weighted graph from the directed graph. The weighted graph has a weight assigned to each edge in the directed graph. The method generates an embedding model based on the directed graph. A set of embeddings is determined for the weighted graph where each node is represented with low-dimensional numerical vectors.

Method, system, and computer program product for representational machine learning for product formulation

Techniques for improving products based on data-driven models are provided. In one example, a system comprises a receiving component that receives product data representing information about a set of products, wherein a first product of the set of products comprises a first combination of a first set of ingredients, and wherein the product data comprises product composition data representing a composition of the first product. The system further comprises a learning component that generates product space data representing a product space that characterizes the set of products and respective degrees of similarity between members of the set of products, wherein a degree of similarity between the first product and a second product of the set of products is determined based on product distance data representing a determined distance metric resulting from a comparison of the first set of ingredients to a second set of ingredients combined to produce the second product.

Data-driven models for improving products

Receiving a first model associated with a user, a generic model of a generic domain, and a specific domain having an associated domain-specific corpus. A first set of query terms based on elements of the first model, and a second set of query terms based on elements of the generic model, are determined. A third set of query terms is generated based on the first and second sets of query terms. The domain specific corpus is queried using the third set of query terms, and a domain specific model is generated based on results of the querying.

Platform for consulting solution

A method includes obtaining from an online social media site a plurality of instances of images of objects associated with a person; analyzing with a data processor the plurality of instances of the images with a plurality of predetermined style classifiers to obtain a score for each image for each style classifier; and determining with the data processor, based on the obtained scores, a likely preference of the person for a particular style of object. The plurality of instances of images of objects associated with the person can be images that were posted, shared or pinned by person, and images that the person expressed a preference for. In a non-limiting embodiment the object is clothing, and the style can include a fashion style or fashion genre including color preferences. A system and a computer program product to perform the method are also disclosed.

System and method for creating a preference profile from shared images

Embodiments relate to a method, system, and computer program product for a process model. The method includes extracting data associated with a process execution trace of a running process and extracting any prior knowledge data relating to the running process. The method also includes calculating at least one transition confidence parameter for the prior knowledge data; and identifying any existing process models relating to the running process. A confidence trace bias is also generated for any existing process model identified. An enhanced bias value is then calculated by combining the confidence trace bias value and value of the transition confidence parameter. Using as input the extracted process execution trace data, the prior knowledge data, the identified existing model and the enhanced bias value, a learned process model is then generated.

Process model generated using biased process mining

A method of managing a plurality of processes in a repository of a computer system is disclosed. For example, the method includes forming a model associated with differences among the plurality of processes. The model associated with differences includes one or more features for expressing the differences. The method further includes forming a model of priority among the one or more features, and organizing the plurality of processes according to the model associated with differences and according to the model of priority. At least one of the one or more features is a semantic feature. One or more of the forming of the model associated with differences, the forming of the model of priority and the organizing of the plurality of processes are implemented on a processor device.

Managing processes in a repository

A system and method allow a user to extract the set of customizations performed on an application and use these to estimate the time and effort and cost of (a) migrating to a new version of the application and/or (b) consolidating systems. The user can browse the extracted data and select configuration elements for re-use. After downloading the one or more configurations and comparing them, the user selects elements of the configurations for re-use. The first step is to scan one or more application systems and extract the configuration data using a surveyor. The method according to the invention automatically identifies configuration differences. The user then selects configuration elements for re-use. A graphic user interface (GUI) can be provided which allows the user to make these selections by dragging and dropping selected elements to a 'To Be' configuration. The selected configurations are then uploaded and installed on an instance of the application.

System and method for application configuration comparison and reuse

An interactive design of process model for packaged application software installation and configuration generates a process model from a natural language description of software installation or configuration related operation. A data structure representing one or more work products related to the process model is identified, and one or more elements of the data structure are identified that are related to one or more sub-processes of the process model. Input information associated with said one or more sub-processes is captured and stored. The input information is linked with the one or more elements of the data structure related to said one or more sub-processes. The one or more sub-processes in the process model may be also demonstrated through a simulation.

Interactive blueprinting for packaged applications

A method for business process to customized program code mapping in a pre-packaged computing solution can include establishing a connection from a business process to code mapping module executing in memory by a processor of a computer to a source pre-packaged computing solution, extracting both a business process hierarchy (BPH) and customized program code from the source pre-packaged computing solution, storing the BPH and the extracted customized program code in storage coupled to the computer, selecting a business process in the BPH and mapping the selected business process to corresponding portions of the customized program code, and generating and displaying in the computer a dependency graph indicating dependency relationships of the mapped portions of the customized program code for the selected business process of the BPH of the source pre-packaged computing solution.

Automated discovery of programmatic resources

An authorization engine is provided in a remote device for mobile authorization using policy based access control. To ensure that remote devices can enforce consistent authorization policies even when the devices are not connected to the server, the remote device downloads the relevant authorization policies when the business objects are downloaded and enforces the policies when operations are invoked. The memory footprint of downloadable authorization policies is reduced to fit onto a resource-constrained remote device. A policy evaluation engine interprets and enforces the downloaded policies on the remote device using only the limited computational resources of the remote device.

Mobile authorization using policy based access control

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Dobbs Ferry, NY, United States of America

Richard Thomas Goodwin