Abstract: A set of transition characteristics can be identified. The set of transition characteristics can include continuities and discontinuities between data fields and data visualization channels among a plurality of data visualizations. The set of transition characteristics can be identified by analyzing the plurality of data visualizations and identifying similarities and differences among the data fields and the data visualization channels. A distribution of the data fields and the data visualization channels across the plurality of data visualizations can be determined. A collection of the data visualizations can be determined based on the distribution of the data fields and the data visualization channels across the plurality of data visualizations. The collection of the data visualizations can include at least a subset of the plurality of data visualizations.

Abstract: Media items may be obtained. The media items may be characterized by capture information indicating a capture time and a capture location of individual media items. The media items may be clustered into scenes based on proximity of the capture times of the media items. The scenes may be clustered into collections based on proximity of the capture times and/or the capture locations of the media items within the scenes. The collections may be iteratively clustered into higher collections based on proximity of the capture times and/or the capture locations of the media items within the collections. One or more collections may be identified for inclusion in a media compilation based on a size of the identified collection(s). A media compilation, including one or more of the media items included in the identified collection(s), may be generated.

Abstract: Methods and systems described herein may include processing a change data capture stream in parallel and optimizing the write operations contained in the change data capture stream such that the write operations may be written to a change table in parallel. Processing the change data capture stream may include decoding the transaction data contained in the change data capture stream in parallel. After the transaction data in the change data capture stream has been decoded, the transaction data may be analyzed to identify write operations that write to the same row. Write operations that write to the same row may be merged into a single write operation. The merging of write operations may be performed until each write operation writes to a different row. The write operations may then be written to the change table in parallel.

Abstract: According to an example, in a method for managing storage of data from an input table, a projection definition that includes an expression to be executed on data contained in a column of the input table may be accessed. The expression on the data contained in the column of the input table may be executed to obtain results data, which may be stored in an expression projection, in which the expression projection includes a column that provides physical storage for the results data. The results data may be stored in the expression projection, segmented, and encoded. In addition, the expression projection may be stored in a node.

Abstract: A data handling system enables the unsupervised creation of an information extraction dictionary by expanding upon a word or phrase included within an expansion query. Prior to receiving the expansion query, the data handling system performs an unsupervised learning of an information corpus which includes text to assign a corpus vector to each word and phrase of the text. After the expansion query, the data handling system compares the expansion query to the corpus vectors. The data handling system ranks the corpus vectors by similarity to the expansion query and provides a ranked list of words or phrases associated with the ranked corpus vectors. The ranked list may be subsequently utilized as the information extraction dictionary.

Abstract: A data handling system enables the unsupervised creation of an information extraction dictionary by expanding upon a word or phrase included within an expansion query. Prior to receiving the expansion query, the data handling system performs an unsupervised learning of an information corpus which includes text to assign a corpus vector to each word and phrase of the text. After the expansion query, the data handling system compares the expansion query to the corpus vectors. The data handling system ranks the corpus vectors by similarity to the expansion query and provides a ranked list of words or phrases associated with the ranked corpus vectors. The ranked list may be subsequently utilized as the information extraction dictionary.

Abstract: Systems and methods are provided for receiving and processing a query to determine an intent of the query, and analyzing the intent of the query to determine that the intent of the query is associated with a use case state diagram. Systems and methods further provide for traversing a plurality of nodes in the user case state diagram to generate a value for each relevant node, performing an operation associated with the query using the generated values, and providing the results of the operation to a computing device.

Abstract: Processing a database query for sets of data includes assigning a unique identifier from an integer space to each entity within data and creating one or more sets of entities each pertaining to a corresponding entity within the data. A representation is then generated on disk for each set of entities, wherein each representation encompasses and is suited for a range of the unique identifiers of entities within a corresponding set and indicates a presence of an entity within that corresponding set. Finally, a query is processed based on the representation for each set of entities to retrieve data satisfying the query, wherein the representation provides a constant time for association and dissociation operations that are append-only operations with deferred merge and automatic filtering of deleted and duplicate entities at query time.

Abstract: Systems and methods are provided herein for multi-pass duplicate identification using sorted neighborhoods. Data comprising a plurality of data records is received. Neighborhood records are generated by merging the plurality of data records with reference records stored in a remote data store. A resource identification field is assigned to each reference record. A pair distance, for each pair of neighborhood records having different resource identification fields, is determined by calculating a standard deviation of distances between each attribute of the pair scaled by a filled pairs quote value. Possible duplicate records are identified by evaluating each pair distance against a threshold, each possible duplicate having grouped attributes. Final duplicate records are identified by matching each group to a key.