Abstract: In some examples, a set of sentences is extracted from a digital document, and each sentence is scored using a respective informativeness measure and readability measure. Sentences in the set of sentences are selected based on the readability measures and informativeness measures. A low readability, high informativeness sentence is identified from the set of sentences. A concatenated sentence is generated by concatenating at least one contextual sentence with the low readability, high informativeness sentence, where the concatenated sentence has a higher readability than the low readability, high informativeness sentence.

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: In some examples, a set of sentences is extracted from a digital document, and each sentence is scored using a respective informativeness measure and readability measure. Sentences in the set of sentences are selected based on the readability measures and informativeness measures. A low readability, high informativeness sentence is identified from the set of sentences. A concatenated sentence is generated by concatenating at least one contextual sentence with the low readability, high informativeness sentence, where the concatenated sentence has a higher readability than the low readability, high informativeness sentence.

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: 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: 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: Groups of connected concepts are identified for a cluster. At least one interestingness measure is computed for each of the groups of connected concepts in the cluster. An interestingness measure for the cluster is derived based on the interestingness measures for the corresponding groups of connected concepts.

Abstract: A path-based identifier is provided. An entity identifier is generated at an entity of a supply chain and the entity identifier is based on a reliability of the entity of the supply chain at which the entity identifier is generated. In addition, an updated path-based identifier is stored based on a predetermined function, where the entity identifier is an input to the predetermined function.

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: A plurality of differential data stores are stored in persistent storage media. In response to receiving a first request to store a particular data object, one of the differential data stores that are stored in the persistent storage media is selected, wherein selecting the one differential data store is according to a criterion relating to compression of data objects in the differential data stores. The selected differential data store is copied into temporary storage media, where the copying is not delayed after receiving the first request to await receipt of more requests. The particular data object is inserted into the copy of the selected differential data store in the temporary storage media, where the inserting is performed without having to retrieve more data from the selected differential store in the persistent storage media.

Abstract: In one embodiment of the present invention, a source point of a supply chain secures shipment of an object by devising an encryption key and encrypting a message using the encryption key to produce an encrypted message. A portion or portions of the encryption key and the encrypted message are included or incorporated within the object to be shipped, packaging surrounding the object, and/or labels affixed to the object or packaging, prior to shipping the object to a destination point within the supply chain. Upon receipt of the object from the supply chain, the destination point can extract the portion or portions of the encryption key and the encrypted message from the object, packaging surrounding the object, and/or labels affixed to the object or packaging, obtain the remaining portion of the encryption key directly from the source point, reassemble the encryption key, and decrypt the encrypted message to produce a computed message.

Abstract: Information from a package is read. An encryption key is generated from the information read from the package. A text unit is encrypted using the encryption key. The encrypted text unit is affixed to the package.

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: One embodiment is a data processing apparatus that has a chunk store containing specimen data chunks, a manifest store containing a plurality of manifests, each of which represents at least a part of previously processed data and includes at least one reference to at least one of the specimen data chunks, and a sparse chunk index containing information on only some specimen data chunks. Input data is processed into a plurality of input data segments. Each manifest of the first set has at least one reference to one of said specimen data chunks that corresponds to one of the input data chunks of a first input data segment. Specimen data chunks corresponding to other input data chunks of the first input data segment are identified by using the identified first set of manifests and at least one manifest identified when processing previous data.

Abstract: Groups of connected concepts are identified for a cluster. At least one interestingness measure is computed for each of the groups of connected concepts in the cluster. An interestingness measure for the cluster is derived based on the interestingness measures for the corresponding groups of connected concepts.

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: Embodiments of the present invention pertain to determining a subset of documents from which a particular document was derived. According to one embodiment, similarity measurements indicating similarities between contents of documents are received. A subset of the documents that the particular document was derived from is determined based on dates the documents were created and the similarity measurements without requiring document tracking information to be associated with the documents to determine the subset.