Abstract: System, method, and computer program product to reduce an amount of processing required to generate a response to a first case by a deep question answering system, by, determining that a similarity score, of the first case relative to a second case, exceeds a similarity threshold, identifying a first feature of the second case having a first relevance score exceeding a relevance threshold, identifying a first candidate answer for the first case that does not have the first feature, and refraining from analyzing the first candidate answer in generating the response to the first case, thereby reducing the amount of processing of the deep question answering system.

Abstract: System, method, and computer program product to reduce an amount of processing required to generate a response to a first case by a deep question answering system, by, determining that a similarity score, of the first case relative to a second case, exceeds a similarity threshold, identifying a first feature of the second case having a first relevance score exceeding a relevance threshold, identifying a first candidate answer for the first case that does not have the first feature, and refraining from analyzing the first candidate answer in generating the response to the first case, thereby reducing the amount of processing of the deep question answering system.

Abstract: Techniques are disclosed for modeling costs when editing a workflow process model. A request may be received to assign a cost factor to a workflow process step of the workflow process model. Responsive to the request, the cost factor may be assigned to the workflow process step, such that a traversal of the workflow process step by a transaction invoking the workflow process model results in the cost factor being included in a total cost to be charged for executing the transaction.

Abstract: System, method, and computer program product to identify changes in evidence used to answer questions by a deep question answering system, by identifying a first evidence related to a feature, the deep question answering system having identified the feature as being relevant to answering a first type of question of a plurality of types of questions, and responsive to: (i) detecting a change in the first evidence, and (ii) determining that a confidence score of a first response generated for a first question, of the first type of question, exceeds a confidence threshold, generating an updated response for the first question based on the changed first evidence.

Abstract: System, method, and computer program product to identify changes in evidence used to answer questions by a deep question answering system, by identifying a first evidence related to a feature, the deep question answering system having identified the feature as being relevant to answering a first type of question of a plurality of types of questions, and responsive to: (i) detecting a change in the first evidence, and (ii) determining that a confidence score of a first response generated for a first question, of the first type of question, exceeds a confidence threshold, generating an updated response for the first question based on the changed first evidence.

Abstract: Administering a business transaction, including: receiving a request for a business transaction, the request containing data to be processed by the business transaction and metadata describing the data and the request; creating, in dependence upon classification rules, the data to be processed by the business transaction, and the metadata describing the data and the request, a business object representing the business transaction; selecting, in dependence upon workflow selection rules and attributes of the business object, one or more workflows to process the data to be processed by the business transaction; processing the data to be processed by the business transaction with the workflows, thereby creating a resultant business object and resultant data; and routing, in dependence upon content routing rules and the attributes of the resultant business object, the resultant data to a destination.

Abstract: Method, computer program product, and system to perform an operation for a deep question answering system. The operation begins by computing a concept score for a first concept in a first case received by the deep question answering system, the concept score being based on a machine learning concept model for the first concept. The operation then excludes the first concept from consideration when analyzing a candidate answer and an item of supporting evidence to generate a response to the first case upon determining that the concept score does not exceed a predefined concept minimum weight threshold. The operation then increases a weight applied to the first concept when analyzing the candidate answer and the item of supporting evidence to generate the response to the first case when the concept score exceeds a predefined maximum weight threshold.

Abstract: Method, computer program product, and system to perform an operation for a deep question answering system. The operation begins by computing a concept score for a first concept in a first case received by the deep question answering system, the concept score being based on a machine learning concept model for the first concept. The operation then excludes the first concept from consideration when analyzing a candidate answer and an item of supporting evidence to generate a response to the first case upon determining that the concept score does not exceed a predefined concept minimum weight threshold. The operation then increases a weight applied to the first concept when analyzing the candidate answer and the item of supporting evidence to generate the response to the first case when the concept score exceeds a predefined maximum weight threshold.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for improving answers to an input question. An input question is received and parsed for a known set of critical information required to answer the input question. Responsive to one or more pieces of critical information failing to be identifiable in the input question, a user who submitted the input question is prompted for the one or more pieces of critical information via a graphical user interface (GUI).

Abstract: Administering medical digital images including receiving a medical image business object representing a transaction carrying out a type of service request made by a health care provider; selecting, in dependence upon workflow selection rules and the attributes of the medical image business object, one or more clinical workflows to process the medical image according to the transaction; and processing the medical image of the request with the clinical workflows, thereby creating a resultant business object and resultant medical image including selecting, by the workflow dispatcher in dependence upon metadata for computational units in the distributed medical digital image computing environment, particular computational units in the distributed medical digital image computing environment to perform particular services of the one or more clinical workflows, deploying the particular services to the particular computational units, and executing the services on the computational units upon which they are deployed.

Abstract: Managing failover operations on a cluster of computers, including: identifying, by a failover hold module, a failure to access data storage in the cluster of computers; preventing the execution of all read operations directed to the data storage that were received after the failure to access data storage was identified; executing all write operations directed to the data storage that were received after the failure to access data storage was identified, including writing data to a cache; identifying that a failover to alternative data storage is complete; executing the held read operations, including reading data from the alternative data storage; and copying, from cache to the alternative data storage, the data written to the cache as part of the write operations.

Abstract: Systems, methods and articles of manufacture for accessing data for a fee are provided. Fee schedules are defined for any arbitrary granularity of data, including for fields and data structures (e.g., tables in a database). Fees may be calculated based on the type of operation to be performed. Fees may also be calculated per operation and/or per data item involved in the operation.

Abstract: System, method, and computer program product to identify changes in evidence used to answer questions by a deep question answering system, by identifying a first evidence related to a feature, the deep question answering system having identified the feature as being relevant to answering a first type of question of a plurality of types of questions, and responsive to: (i) detecting a change in the first evidence, and (ii) determining that a confidence score of a first response generated for a first question, of the first type of question, exceeds a confidence threshold, generating an updated response for the first question based on the changed first evidence.

Abstract: System, method, and computer program product to identify relevant features in a deep question answering system, by classifying a first case received by the deep question answering system, and, while training the deep question answering system to answer the first case, identifying a first feature in the first case, computing a first feature score for the first feature, the first feature score indicating a relevance of the first feature in generating a correct response to the first case, and, identifying the first feature as relevant in answering the classified first case upon determining that the first feature score exceeds a relevance threshold.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: System, method, and computer program product to reduce an amount of processing required to generate a response to a first case by a deep question answering system, by, determining that a similarity score, of the first case relative to a second case, exceeds a similarity threshold, identifying a first feature of the second case having a first relevance score exceeding a relevance threshold, identifying a first candidate answer for the first case that does not have the first feature, and refraining from analyzing the first candidate answer in generating the response to the first case, thereby reducing the amount of processing of the deep question answering system.

Abstract: System, method, and computer program product to identify relevant features in a deep question answering system, by classifying a first case received by the deep question answering system, and, while training the deep question answering system to answer the first case, identifying a first feature in the first case, computing a first feature score for the first feature, the first feature score indicating a relevance of the first feature in generating a correct response to the first case, and, identifying the first feature as relevant in answering the classified first case upon determining that the first feature score exceeds a relevance threshold.

Abstract: System, method, and computer program product to reduce an amount of processing required to generate a response to a first case by a deep question answering system, by, determining that a similarity score, of the first case relative to a second case, exceeds a similarity threshold, identifying a first feature of the second case having a first relevance score exceeding a relevance threshold, identifying a first candidate answer for the first case that does not have the first feature, and refraining from analyzing the first candidate answer in generating the response to the first case, thereby reducing the amount of processing of the deep question answering system.

Abstract: System, method, and computer program product to identify changes in evidence used to answer questions by a deep question answering system, by identifying a first evidence related to a feature, the deep question answering system having identified the feature as being relevant to answering a first type of question of a plurality of types of questions, and responsive to: (i) detecting a change in the first evidence, and (ii) determining that a confidence score of a first response generated for a first question, of the first type of question, exceeds a confidence threshold, generating an updated response for the first question based on the changed first evidence.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.