Abstract: Embodiments associate a relevant semantic data type (e.g., date) with incoming raw data (e.g., a column of digits) which lacks metadata. Assignment of semantic data type is inferred from a plurality of features. A first step determines a first feature comprising success rate in converting the raw data into various semantic data types. Then, alignment between observed/reference distributions of other features (e.g., data first digit, data length) is determined per-semantic data type. Total scores for each semantic data type are calculated from the combined features, and used as a basis for ranking the semantic data types. The total scores may reflect a weighting of the various features. In a second step, top-ranked semantic data types may be further differentiated from one another by applying additional features. User feedback regarding accuracy of semantic data type assignment, may be incorporated into training data used to modify the feature reference distributions.

Abstract: Embodiments associate a relevant semantic data type (e.g., date) with incoming raw data (e.g., a column of digits) which lacks metadata. Assignment of semantic data type is inferred from a plurality of features. A first step determines a first feature comprising success rate in converting the raw data into various semantic data types. Then, alignment between observed/reference distributions of other features (e.g., data first digit, data length) is determined per-semantic data type. Total scores for each semantic data type are calculated from the combined features, and used as a basis for ranking the semantic data types. The total scores may reflect a weighting of the various features. In a second step, top-ranked semantic data types may be further differentiated from one another by applying additional features. User feedback regarding accuracy of semantic data type assignment, may be incorporated into training data used to modify the feature reference distributions.

Abstract: Embodiments associate a relevant semantic data type (e.g., date) with incoming raw data (e.g., a column of digits) which lacks metadata. Assignment of semantic data type is inferred from a plurality of features. A first step determines a first feature comprising success rate in converting the raw data into various semantic data types. Then, alignment between observed/reference distributions of other features (e.g., data first digit, data length) is determined per-semantic data type. Total scores for each semantic data type are calculated from the combined features, and used as a basis for ranking the semantic data types. The total scores may reflect a weighting of the various features. In a second step, top-ranked semantic data types may be further differentiated from one another by applying additional features. User feedback regarding accuracy of semantic data type assignment, may be incorporated into training data used to modify the feature reference distributions.

Abstract: Embodiments associate a relevant semantic data type (e.g., date) with incoming raw data (e.g., a column of digits) which lacks metadata. Assignment of semantic data type is inferred from a plurality of features. A first step determines a first feature comprising success rate in converting the raw data into various semantic data types. Then, alignment between observed/reference distributions of other features (e.g., data first digit, data length) is determined per-semantic data type. Total scores for each semantic data type are calculated from the combined features, and used as a basis for ranking the semantic data types. The total scores may reflect a weighting of the various features. In a second step, top-ranked semantic data types may be further differentiated from one another by applying additional features. User feedback regarding accuracy of semantic data type assignment, may be incorporated into training data used to modify the feature reference distributions.

Abstract: Embodiments associate a relevant semantic data type (e.g., date) with incoming raw data (e.g., a column of digits) which lacks metadata. Assignment of semantic data type is inferred from a plurality of features. A first step determines a first feature comprising success rate in converting the raw data into various semantic data types. Then, alignment between observed/reference distributions of other features (e.g., data first digit, data length) is determined per-semantic data type. Total scores for each semantic data type are calculated from the combined features, and used as a basis for ranking the semantic data types. The total scores may reflect a weighting of the various features. In a second step, top-ranked semantic data types may be further differentiated from one another by applying additional features. User feedback regarding accuracy of semantic data type assignment, may be incorporated into training data used to modify the feature reference distributions.

Abstract: A system and method for managing business intelligence data is described. In some example embodiments, the system extracts data and metadata from a business intelligence file, generates a data bundle of the data and metadata, generates an application bundle based on the date bundle, and generates an interactive document using the data bundle and application bundle.

Abstract: A system and method for managing business intelligence data is described. In some example embodiments, the system extracts data and metadata from a business intelligence file, generates a data bundle of the data and metadata, generates an application bundle based on the date bundle, and generates an interactive document using the data bundle and application bundle.

Abstract: A method and apparatus are provided for coding or decoding an image comprising macro-blocks which are distributed in lines and columns. The processing of at least one given macro-block requires the pre-processing of at least one other macro-block on which said dependent macro-block depends. Moreover, the macro-blocks are processed sequentially line by line or column by column. Processing of the macro-blocks is multithreaded over N processors, N?2. The image is separated into N vertical bands each comprising a plurality of lines and at least one column of macro-blocks if the macro-block is processed sequentially line by line, or into N horizontal bands each comprising a plurality of columns and at least one line of macro-blocks if the macro-block is processed sequentially column by column. One of the N bands is processed by each processor, and the processing operations performed by the N processors is synchronized.

Abstract: A method and apparatus are provided for coding or decoding an image comprising macro-blocks which are distributed in lines and columns. The processing of at least one given macro-block requires the pre-processing of at least one other macro-block on which said dependent macro-block depends. Moreover, the macro-blocks are processed sequentially line by line or column by column. Processing of the macro-blocks is multithreaded over N processors, N?2. The image is separated into N vertical bands each comprising a plurality of lines and at least one column of macro-blocks if the macro-block is processed sequentially line by line, or into N horizontal bands each comprising a plurality of columns and at least one line of macro-blocks if the macro-block is processed sequentially column by column. One of the N bands is processed by each processor, and the processing operations performed by the N processors is synchronized.