Abstract: Examples relate to deploying distributed applications using virtual nodes. In some examples, virtual nodes are created and are each assigned a core subset of a number of processing cores, an Internet protocol (IP) address, and an in-memory file system configured to provide access to a portion of physically shared memory. At this stage, a distributed application that is configured to be deployed to a plurality of machine nodes is deployed to the plurality of virtual nodes. On a first virtual node, a reference to a first dataset stored in physically shared memory is sent to a second virtual node, where the physically shared memory is accessible to each of the plurality of virtual nodes. Next, on the second virtual node, the first dataset is accessed through the in-memory file system of the first virtual node.

Abstract: In one implementation, a poster engine may maintain a thread history associated with a response poster, a status engine may maintain a potential thread status of a thread of a forum based on the thread history, and a pool engine may maintain an inspection pool based on the potential thread status. In another implementation, a thread status of a thread of the forum may be maintained based on a correlation rating of a response poster and a pool having a number of threads based on the thread status may be provided.

Abstract: A technique to generate a feature set. A plurality of samples from a data set can be clustered. Features can be selected based on the clusters. The features can be added to the feature set. Additional samples can be clustered and features selected and added to the feature set until a convergence threshold is reached.

Abstract: Examples herein involve graph processing using a shared memory. An example method includes distributing vertices of a graph to a plurality of graph partition processors of a system, the graph partition processors to process non-overlapping sets of vertices of the graph; storing a master copy of a vertex state of the graph in a shared memory of the system; instructing the graph partition processors to iteratively process respective vertices of the graph partitions based on local copies of the state of the graph stored in respective local memories of the graph partition processors; and updating the master copy of the state of the graph in the shared memory based on the iterative processing of the partitions of the vertices by the graph partition processors until convergence of the graph.

Abstract: Examples herein involve graph processing using a shared memory. An example method includes distributing vertices of a graph to a plurality of graph partition processors of a system, the graph partition processors to process non-overlapping sets of vertices of the graph; storing a master copy of a vertex state of the graph in a shared memory of the system; instructing the graph partition processors to iteratively process respective vertices of the graph partitions based on local copies of the state of the graph stored in respective local memories of the graph partition processors; and updating the master copy of the state of the graph in the shared memory based on the iterative processing of the partitions of the vertices by the graph partition processors until convergence of the graph.

Abstract: Examples relate to deploying distributed applications using virtual nodes. In some examples, virtual nodes are created and are each assigned a core subset of a number of processing cores, an Internet protocol (IP) address, and an in-memory file system configured to provide access to a portion of physically shared memory. At this stage, a distributed application that is configured to be deployed to a plurality of machine nodes is deployed to the plurality of virtual nodes. On a first virtual node, a reference to a first dataset stored in physically shared memory is sent to a second virtual node, where the physically shared memory is accessible to each of the plurality of virtual nodes. Next, on the second virtual node, the first dataset is accessed through the in-memory file system of the first virtual node.

Abstract: In a method for generating a query plan for estimating a number of unique entry counts of an attribute in a database, a sample of entries in the database is identified, at least one of a sampling percent and a coefficient of variance of the entries in the sample is identified, and a skewness of the entries in the sample is calculated. In addition, at least one of a plurality of estimators is selected based upon the skewness of the entries and at least one of the sampling percent and the coefficient of variance of the entries in the sample. Moreover, a query plan is generated from the selected at least one of the plurality of estimators. A query optimizer for performing the method is provided.

Abstract: A technique to generate a feature set. A plurality of samples from a data set can be clustered. Features can be selected based on the clusters. The features can be added to the feature set. Additional samples can be clustered and features selected and added to the feature set until a convergence threshold is reached.

Abstract: A method for determining topic relevance of an email thread with an electronic device is described. The method includes removing redundancy from email messages in an email thread, grouping a number of email threads into a number of email clusters, identifying high information gain terms for each email cluster, identifying topic terms for each email cluster from the high information gain terms and determining a relevance of the number of email threads in an email cluster based on the topic terms for the email cluster and a threshold number of email messages in an email thread.

Abstract: A method of generating training documents for training a classifying device comprises, with a processor, sampling from a distribution of words in a number of original documents, and creating a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents. A device for classifying textual documents comprises a processor; and a memory communicatively coupled to the processor, the memory comprising a sampling module to, when executed by the processor, determine the distribution of words in a number of original documents, a pseudo-document creation module to, when executed by the processor, create a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents, and a training module to, when executed by the processor, train the device to classify textual documents based on the pseudo-documents.

Abstract: Systems and methods for clustering a plurality of feature vectors. A hierarchical clustering algorithm is performed on the plurality of feature vectors to provide a plurality of clusters and a cluster similarity measure for each cluster representing the quality of the cluster. Each cluster of the plurality of clusters with a cluster similarity measure meeting a threshold value is accepted. A clustering algorithm is performed on each cluster that fails to meet the threshold value to provide a set of subclusters each having an associated cluster similarity measure. Each subcluster having a cluster similarity measure meeting the threshold value is accepted.

Abstract: A method of generating training documents for training a classifying device comprises, with a processor, determining a number of sub-samples in a number of original documents, and creating a number of pseudo-documents from the sub-samples, the pseudo-documents comprising a portion of the number of sub-samples. A device for training a classifying device comprises a processor, and a memory communicatively coupled to the processor. The memory comprises a sampling module to, when executed by the processor, determine a number of sub-samples in a number of original documents, a pseudo-document creation module to, when executed by the processor, create a number of pseudo-documents from the sub-samples, the pseudo-documents comprising a portion of the number of sub-samples, and a training module to, when executed by the processor, train a classifying device to classify textual documents based on the pseudo-documents.

Abstract: For documents in a collection, respective data structures containing information representing occurrence of terms in the corresponding documents are generated. For a first one of the documents, at least one moment is computed based on the information in the data structure corresponding to the first document, where the at least one moment represents at least one characteristic of a distribution of values derived from the information in the data structure corresponding to the first document. The at least one moment is useable to categorize the first document into one of a plurality of classes of documents.

Abstract: A method of generating training documents for training a classifying device comprises, with a processor, sampling from a distribution of words in a number of original documents, and creating a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents. A device for classifying textual documents comprises a processor; and a memory communicatively coupled to the processor, the memory comprising a sampling module to, when executed by the processor, determine the distribution of words in a number of original documents, a pseudo-document creation module to, when executed by the processor, create a number of pseudo-documents from the distribution of words, the pseudo-documents comprising a similar distribution of words as the original documents, and a training module to, when executed by the processor, train the device to classify textual documents based on the pseudo-documents.

Abstract: Documents in a document thread include descriptive terms that have weights. An indication indicates when documents in the document thread reach a threshold of weight for the document thread.

Abstract: Systems and methods for clustering a plurality of feature vectors. A hierarchical clustering algorithm is performed on the plurality of feature vectors to provide a plurality of clusters and a cluster similarity measure for each cluster representing the quality of the cluster. Each cluster of the plurality of clusters with a cluster similarity measure meeting a threshold value is accepted. A clustering algorithm is performed on each cluster that fails to meet the threshold value to provide a set of subclusters each having an associated cluster similarity measure. Each subcluster having a cluster similarity measure meeting the threshold value is accepted.

Abstract: A method (200) of identifying a principal document in a document set is provided. An exemplary method includes obtaining a document set comprising a plurality of documents (202) and grouping the plurality of documents into a plurality of clusters based, at least in part, on a textual similarity between each of the plurality of documents (204). The method also includes obtaining one or more descriptive terms corresponding to the plurality of documents, wherein the descriptive terms are terms within the plurality of documents that have been identified as being useful for discriminating between the clusters (206). The method also includes, for each cluster, identifying a subset of descriptive terms based, at least in part, on a prevalence of the descriptive terms within the documents of the cluster (208) and identifying the principal documents in the cluster based, at least in part, on a prevalence of the subset of descriptive terms within each of the documents in the cluster (210).

Abstract: A method of identifying a fresh document in a document set is provided. The method may include obtaining a query document that is included in a document set comprising a plurality of documents. The method may also include grouping the plurality of documents into a plurality of fine clusters based on a textual similarity between the plurality of documents. The method may also include identifying a target fine cluster within the plurality of fine clusters, the target fine cluster including the query document. The method may also include ordering the documents included in the target fine cluster by time to identify the fresh document. The method may also include generating a query response that includes the fresh document.

Abstract: A computer system that includes a graphical user interface used to organize a group of documents is provided. The system includes a processor that is adapted to execute machine-readable instructions. The system also includes a storage device that is adapted to store data. The data includes a plurality of documents and instructions that are executable by the processor to generate the graphical user interface. The graphical user interface includes a cluster map that includes the results of a clustering algorithm applied to the documents. The graphical user interface also includes a principal documents screen that includes a principal document that is identified by weighting each of the documents in a cluster based, at least in part, on an occurrence of representative terms in the document. The representative terms are terms that have been identified by the clustering algorithm as being more effective for distinguishing between documents that belong to different clusters.

Abstract: A method of identifying a provenance of a document is provided. The method may include obtaining a query document that is included in a document set comprising a plurality of documents. The method may also include grouping the plurality of documents into a plurality of fine clusters based on a textual similarity between the plurality of documents. The method may also include identifying a target fine cluster within the plurality of fine clusters, the target fine cluster including the query document. The method may also include ordering the documents included in the target fine cluster based, at least in part, on metadata associated with each of the documents to identify a source document. The method may also include generating a query response that includes the source document.