Abstract: A system and associated method for cross-guided data clustering by aligning target clusters in a target domain to source clusters in a source domain. The cross-guided clustering process takes the target domain and the source domain as inputs. A common word attribute shared by both the target domain and the source domain is a pivot vocabulary, and all other words in both domains are a non-pivot vocabulary. The non-pivot vocabulary is projected onto the pivot vocabulary to improve measurement of similarity between data items. Source centroids representing clusters in the source domain are created and projected to the pivot vocabulary. Target centroids representing clusters in the target domain are initially created by conventional clustering method and then repetitively aligned to converge with the source centroids by use of a cross-domain similarity graph that measures a respective similarity of each target centroid to each source centroid.

Abstract: A method, system and program of executing an interpreted programming language application from a server at a designated virtual machine on a client computer are provided. A manifest file is extracted which indicates at least one resource for the interpreted programming language application as well as the repository file associated with the resource. The repository file is located in a web start cache and moved to a run directory accessible by the designated virtual machine, outside the web start cache. The interpreted programming language application is then executed from the run directory using the at least one resource.

Abstract: A computer system for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A process for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source- target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A computer program product evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A system and associated method for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A method, system and program of executing an interpreted programming language application from a server at a designated virtual machine on a client computer are provided. A manifest file is extracted which indicates at least one resource for the interpreted programming language application as well as the repository file associated with the resource. The repository file is located in a web start cache and moved to a run directory accessible by the designated virtual machine, outside the web start cache. The interpreted programming language application is then executed from the run directory using the at least one resource.

Abstract: A mechanism is provided for executing an interpreted programming language application from a server at a designated virtual machine on a client computer. A manifest file is extracted which indicates at least one resource for the interpreted programming language application as well as the repository file that provides the at least one resource. The repository file is located in a web start cache and copied to a run directory accessible by the designated virtual machine, outside the web start cache. After terminating the web start application, a particular virtual machine is designated from a plurality of virtual machines based upon the run directory to which the bootstrap class copied the resource repository file thereby forming the designated virtual machine. The interpreted programming language application is then executed from the run directory using the at least one resource.

Abstract: The different illustrative embodiments provide a method, computer system, and computer program product to manage a display of objects. A processor unit displays a graphical control configured to receive a user input of a value to control the display of a set of objects relative to a group of properties for the set of objects in a table. A processor unit changes the display of the set of objects to a ratio in a plurality of ratios in the table for the set of objects relative to the group of properties for the set of objects in the table based on the value.

Abstract: Methods, apparatus, and products for staging a file within a distributed computing system are disclosed that include defining a topology of the network; mapping each computer to a network location in the topology of the network; selecting, in dependence upon the topology of the network and the network locations of the computers in the network, one or more servers on which to stage the file for use by clients in the network; and staging the file on the one or more selected servers. The distributed computing system includes a plurality of computers coupled for data communications through a data communications network, the distributed computing system also includes files of computer data and computer program instructions for use by the computers, the computers include clients that use the files and servers capable of functioning as staging locations for files for use by the computers in the network.

Abstract: Determining synonyms of words in a set of documents. Particularly, when provided with a word or phrase as input, in exemplary embodiments there is afforded the return of a predetermined number of “top” synonym words (or phrases) for an input word (or phrase) in a specific collection of text documents. Further, a user is able to provide ongoing and iterative positive or negative feedback on the returned synonym words, by manually accepting or rejecting such words as the process is underway.

Abstract: A system and associated method for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A system and associated method for cross-guided data clustering by aligning target clusters in a target domain to source clusters in a source domain. The cross-guided clustering process takes the target domain and the source domain as inputs. A common word attribute shared by both the target domain and the source domain is a pivot vocabulary, and all other words in both domains are a non-pivot vocabulary. The non-pivot vocabulary is projected onto the pivot vocabulary to improve measurement of similarity between data items. Source centroids representing clusters in the source domain are created and projected to the pivot vocabulary. Target centroids representing clusters in the target domain are initially created by conventional clustering method and then repetitively aligned to converge with the source centroids by use of a cross-domain similarity graph that measures a respective similarity of each target centroid to each source centroid.

Abstract: A mechanism is provided for providing a native code library from a server to an application written in an interpreted programming language for execution on a remote computer. An installer object is invoked from within a main class of the interpreted programming language application. The install object extracts a file list from the server including at least one native code library filename indicating a native code library. The native code library indicated by the native code library filename is installed to a target directory on the remote computer and the main class of the interpreted programming language application is then invoked on the remote computer. Additional native code library dependencies are also installed to the target directory on the remote computer.

Abstract: A system, computer-implementable method, and computer-readable medium for processing a collection of work items in a data processing system. According to a preferred embodiment of the present invention, an agent monitor assembles a work items list that includes a collection of work items, wherein at least one work item among the collection of work items is a work item that is unassigned to at least one agent among a collection of agents. The agent monitor assigns the at least one work item to the at least one agent. In response to determining that the at least one agent has entered into a hang state, the agent monitor releases the at least one work item from the at least one agent.

Abstract: A method of determining a number of connections to initially utilize for a download of a target data file from a set of one or more servers includes determining a maximum number of initial connections based at least in part upon a number of servers available to serve a target data file to be downloaded, determining a size of the target data file, and determining the number of initial connections to utilize for a download of the target data file. The later determination is based at least in part upon the maximum number of initial connections, the size of the target data file, and a chunk size of one or more chunks into which the target data file is to be decomposed.

Abstract: Methods, apparatus, and products for staging a file within a distributed computing system are disclosed that include defining a topology of the network; mapping each computer to a network location in the topology of the network; selecting, in dependence upon the topology of the network and the network locations of the computers in the network, one or more servers on which to stage the file for use by clients in the network; and staging the file on the one or more selected servers. The distributed computing system includes a plurality of computers coupled for data communications through a data communications network, the distributed computing system also includes files of computer data and computer program instructions for use by the computers, the computers include clients that use the files and servers capable of functioning as staging locations for files for use by the computers in the network.

Abstract: A system, computer-implementable method, and computer-readable medium for processing a collection of work items in a data processing system. According to a preferred embodiment of the present invention, an agent monitor assembles a work items list that includes a collection of work items, wherein at least one work item among the collection of work items is a work item that is unassigned to at least one agent among a collection of agents. The agent monitor assigns the at least one work item to the at least one agent. In response to determining that the at least one agent has entered into a hang state, the agent monitor releases the at least one work item from the at least one agent.

Abstract: A method of determining a number of connections to initially utilize for a download of a target data file from a set of one or more servers includes determining a maximum number of initial connections based at least in part upon a number of servers available to serve a target data file to be downloaded, determining a size of the target data file, and determining the number of initial connections to utilize for a download of the target data file. The later determination is based at least in part upon the maximum number of initial connections, the size of the target data file, and a chunk size of one or more chunks into which the target data file is to be decomposed.

Abstract: A method, system, and computer-usable medium for dropping slow connections during a multi-connection download. In a preferred embodiment of the present invention, a download manager establishes a predetermined number of connections between a client and a predetermined number of servers. The download manager sets a minimum threshold data transfer rate for the client for a particular connection and drops a first connection that is transferring data at a rate below the minimum threshold if an overall transfer rate of a download after dropping the first connection is greater than the predetermined number of connections times the minimum threshold data transfer rate.