International Business Machines Corporation

Rafah A Hosn

Yu Deng

Murthy V Devarakonda

Soumitra Sarkar

Ruchi Mahindru

Harigovind Venkatraj Ramasamy

Norbert George Vogl

Andrzej P Kochut

Hidayatullah Habeebullah Shaikh

Jonathan Paul Munson

Michael Reuben Head

Nithya Rajamani

Mahesh Viswanathan

Stephane Herman Maes

Michael Man Behrendt

Robert John Moore

Jan Kleindienst

James William Murray

Thiruvilwamalai Venkatraman Raman

Nancy L Burchfield

Nathaniel Hang

Mark N Wegman

Bruce D Lucas

Charles Francis Wiecha

Michal Shmueli-Scheuer

John H Field

Alpana Tiwari

Thiruvilvama Lai V Raman

Ladislav Seredi

Maria-Cristina V Marinescu

Jennifer A Healey

David Frederick Bantz

Salman Abdul Baset

Daby Mousse Sow

Long Wang

Chunqiang Tang

Jan Sedivy

Daniel Mark Coffman

Nicholas Matthew Mitchell

Nandakishore Kambhatla

Debapriyo Majumdar

Jeremy Brand Sussman

Tomas Macek

John Sidney Davis, Ii

Shajith I Mohamed

Vijil Enara Chenthamarakshan

Carl P Zetie

Veronique Perret

David R Boloker

Jaroslav Gergic

Photina Jaeyun Jang

A method, system and computer program product are disclosed for searching for data. In one embodiment, the invention provides a method comprising identifying a query and a search scope including a set of specified entities; and for each of these entities, estimating a number of documents that would be identified in a search through the entity to answer the query. On the basis of this estimating, a subset of the entities is formed. The query and this subset of entities are sent to a search engine to search the subset of entities to answer the query. In one embodiment, the estimating includes collecting statistical information from queries to build up a historical cache using heuristics or machine learning techniques, wherein the query includes a key word and a scope, and the historical cache contains a maximum number of returned results for an entity given the queries executed.

Using historical information to improve search across heterogeneous indices

Technical solutions are described for analyzing a natural language conversation-generating machine. The computer implemented method includes identifying, from a conversation log, a plurality of adjacency pairs. The method further includes determining, from the adjacency pairs, a number of adjacency pairs with outcome success indicators. The method further includes determining, from the adjacency pairs, a number of adjacency pairs with outcome failure indicators. The method further includes computing a mutual understanding score for the machine by computing a ratio of the number of adjacency pairs with outcome success indicators and the number of adjacency pairs with outcome failure indicators.

Measuring mutual understanding in human-computer conversation

A method, system, and computer program product are disclosed for merging search results. In one embodiment, the method comprises identifying a query, splitting the query into sub-queries, and calculating information content for each of the sub-queries. This method also comprises executing each of the sub-queries to obtain a plurality of search results, and combining the search results based on the information content calculated for the sub-queries. In an embodiment, the execution of each of the sub-queries includes identifying a multitude of search results for at least one of the sub-queries; and the combining includes grouping said multitude of search results into a plurality of clusters, and computing a relevance score for each of said clusters. In the embodiment the combining further includes merging the clusters based on the relevance scores computed for the clusters as well as the information content calculated for the sub-queries.

Information theory based result merging for searching hierarchical entities across heterogeneous data sources

A model is provided for managing resources in a programming environment. A first construct is triggered to update mutable entities in response to changes to related data associated with the mutable entities. In accordance with the updated mutable entities, at least one of a second construct and a third construct is triggered to respectively create a new mutable entity and update a data relationship.

System and method for managing resources using a compositional programming model

A mechanism is provided in a data processing system for automatically throttling the rate of service provided by a Web application programming interface (API) for a software service. A flow controller executing on the data processing system assigns a queue to each consumer of the software service. Responsive to receiving a current request for the software service from a given consumer of the software service, a flow controller executing on the data processing system adds the current request to a given queue assigned to the given consumer. The flow controller sends a next request from the given queue to the Web API based on a licensed rate of service of the given consumer.

Flow controller automatically throttling rate of service provided by web API

Technical solutions are described method for analyzing a natural language conversation-generating machine. An example computer implemented method includes determining, from a plurality of adjacency pairs in a conversation, a number of base pairs. The computer implemented method also includes determining, from the plurality of adjacency pairs, a number of expansion pairs. The computer implemented method also includes computing a efficiency score for the conversation by computing a ratio of the number of base pairs and the number of expansion pairs.

Technical solutions are described for generating structured conversational data. An example method includes receiving an utterance that is part of a conversation and identifying the utterance as part of an adjacency pair. The adjacency pair includes two utterances, each produced by different speakers. The method also includes associating the utterance with a label from a predetermined set of labels based on the identifying of the adjacency pair.

Detection and labeling of conversational actions

Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for implementing an extensible support system for multiple service offerings. For example, such a support system can be a business support system which may be employed in conjunction with a cloud computing environment.

Extensible support system for service offerings

Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for ontology based resource provisioning and management for services. For example, such an ontology based approach can be utilized in conjunction with a business support system which may be employed in conjunction with a cloud computing environment.

Ontology based resource provisioning and management for services

Techniques for model-based analysis of a data center. A method includes creating a metamodel based on domain knowledge to represent a type of object and/or relationship of a data center, using static and dynamic configuration and data analysis techniques to discover topology of elements of the data center and represent the topology as a model that is an instance of the metamodel, extending the metamodel, using the model to perform analysis of the data center in connection with a specified task, leveraging domain knowledge represented in nodes of the extended metamodel to guide the analysis in terms of determining guidelines to apply to each node and determining relationships to traverse to continue the analysis, extending the domain knowledge by updating the extended metamodel upon discovery of additional knowledge for use in improving analysis tasks, and extending the model on-demand using dynamic analysis techniques upon detection of multiple analysis failures.

Dynamic model-based analysis of data centers

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New York, NY, United States of America

Rafah A Hosn