Abstract: 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.

Abstract: A method for assembling stream processing graphs in a stream processing system, includes: performing, in an offline manner, translating a plurality of processing element and data source descriptions into a planning language and performing reasoning on the plurality of processing element and data source 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 stream processing graphs, each of the processing graphs including a plurality of the translated and reasoned processing elements or data sources that satisfy the desired processing outcome.

Abstract: A hybrid machine/human computing arrangement which advantageously involves humans to assist a computer to solve particular tasks, allowing the computer to solve the tasks more efficiently. In one embodiment, a computer system decomposes a task, such as, for example, image or speech comparison, into subtasks for human performance, and requests the performances. The computer system programmatically conveys the request to a central coordinating server of the hybrid machine/human computing arrangement, which in turn dispatches the subtasks to personal computers operated by the humans. The humans perform the subtasks and provide the results back to the server, which receives the responses, and generates a result for the task based at least in part on the results of the human performances.

Abstract: Improved information processing techniques for measuring similarity between instances in an ontology are disclosed. For example, a method of measuring similarity between instances in an ontology for use in an information retrieval system includes the following steps. A set of instances from the ontology is obtained. At least one of the following similarity metrics for the set of instances is computed: (i) a first metric that measures similarity between instances in the set of instances with respect to ontology concepts to which the instances belong; (ii) a second metric which measures similarity between instances in the set of instances where the instances are subjects in statements involving a given ontology property; and (iii) a third metric which measures similarity between instances in the set of instances where the instances are objects in statements involving a given ontology property.

Abstract: A system and method for context-aware unified communication for enabling communications between users over a common communications platform or heterogeneous communications platforms.

Abstract: 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.

Abstract: 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.

Abstract: The present invention provides a method, system, and computer program product for enhancing collaboration using a corporate social network to infer contacts. A method in accordance with an embodiment of the present invention includes: providing a social network for an organization, the social network including a plurality of nodes and links, wherein each node represents a member of the organization, and wherein each link represents a relationship between a pair of the nodes; receiving a request by a requesting member of the organization for a required type of contact; and inferring a contact to the requesting member by searching the social network along a plurality of dimensions from the node representing the requesting member.

Abstract: A number of Web 2.0 sites support collaborative tagging systems, which allow users to tag resources with keywords. The tags enable search and retrieval of resources both for the user and for other users, using interfaces like a conventional search form or a tag cloud. A tag hierarchy-based search and retrieval system is provided that enhances the existing interfaces by improving search recall and allowing the discovery of even poorly annotated resources. The system uses tag co-occurrence information to automatically learn tag hierarchies. The learned hierarchies are used for automatically inferring additional tags to resources. These inferences are used to improve the recall of queries issued from a search form or via a tag cloud. The learned hierarchies can be viewed as an emergent ontology that is built up through the collaborative wisdom of a large number of users.

Abstract: 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.

Abstract: An edge-covering based greedy method for shared filter ordering includes receiving a plurality of queries, wherein each query is a conjunction of filters, and each filter is shared across multiple queries of the plurality of queries, evaluating a unit price of each filter, evaluating, sequentially, each filter in order of a lowest to a highest unit price, to determine a value for each filter, and changing an ordering of the filters according to the values. An edge-covering based harmonic method for shared filter ordering includes receiving a plurality of queries, wherein each query is a conjunction of filters, and each filter is shared across multiple queries of the plurality of queries, evaluating a weight of each filter, evaluating, randomly, each filter having a probability inversely proportionally to its weight, to determine a value, and changing an ordering of the filters according to the values.

Abstract: A semantic publish-subscribe system, including: a plurality of subscribers; a plurality of publishers; and a server that receives and stores subscriptions that are described in web ontology language (OWL) graph patterns from the plurality of subscribers and that receives events that are described in semantic graphs formed by OWL assertions from the plurality of publishers, wherein when the server receives an event from a publisher, the server expands the OWL assertions of the event using a reasoner to produce an expanded event, the server matches the expanded event to a subscription, and then, the server notifies a subscriber whose interest matches the event.

Abstract: A method for modeling a web service operation, includes: defining an input message pattern that describes input requirements of a web service operation, wherein the input message pattern includes a set of variables representing data elements that must be contained in a message input to the web service operation, and a graph pattern that semantically describes the data elements that must be contained in the message input to the web service operation; and defining an output message pattern that describes an output message of the web service operation, wherein the output message pattern includes a set of variables and exemplars that represent data elements contained in the output message, and a graph pattern that semantically describes the data elements contained in the output message.

Abstract: A method for constructing a stream processing requests so that a stream processing application satisfying the stream processing request can be assembled, includes: inputting a stream processing request, wherein the stream processing request includes a goal that is represented by a graph pattern that semantically describes a desired stream processing outcome; and assembling a stream processing graph, wherein the stream processing graph is assembled to include at least one processing element that satisfies the desired processing outcome by associating the graph pattern that semantically describes the desired processing outcome with a graph pattern that semantically describes an input of the at least one processing element and a graph pattern that semantically describes an output of the at least one processing element, or to include at least one data source by associating the graph pattern that semantically describes the desired processing outcome with a graph pattern that semantically describes an output of the

Abstract: 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.

Abstract: A method for assembling a stream processing application, includes: inputting a plurality of data source descriptions, wherein each of the data source descriptions includes a graph pattern that semantically describes an output of a data source; inputting a plurality of component descriptions, wherein 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; inputting a stream processing request, wherein the stream processing request includes a goal that is represented by a graph pattern that semantically describes a desired stream processing outcome; assembling a stream processing graph, wherein the stream processing graph includes at least one data source or at least one component that satisfies the desired processing outcome; and outputting the stream processing graph.

Abstract: A method for modeling components of a stream processing application, includes: defining an input message pattern of a processing element, wherein the input message pattern includes variables representing data objects that must be included in a message input to the processing element, and a graph pattern that semantically describes the data objects that must be included in the message input to the processing element; and defining an output message pattern of the processing element, wherein the output message pattern includes variables and new data objects, the variables and new data objects representing data objects that must be included in a message output from the processing element, and a graph pattern that semantically describes the data objects in the message output from the processing element.

Abstract: A method for assembling stream processing graphs in a stream processing system, includes: performing, in an offline manner, translating a plurality of processing element and data source descriptions into a planning language and performing reasoning on the plurality of processing element and data source 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 stream processing graphs, each of the processing graphs including a plurality of the translated and reasoned processing elements or data sources that satisfy the desired processing outcome.

Abstract: 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.

Abstract: Improved information processing techniques for measuring similarity between instances in an ontology are disclosed. For example, a method of measuring similarity between instances in an ontology for use in an information retrieval system includes the following steps. A set of instances from the ontology is obtained. At least one of the following similarity metrics for the set of instances is computed: (i) a first metric that measures similarity between instances in the set of instances with respect to ontology concepts to which the instances belong; (ii) a second metric which measures similarity between instances in the set of instances where the instances are subjects in statements involving a given ontology property; and (iii) a third metric which measures similarity between instances in the set of instances where the instances are objects in statements involving a given ontology property.