Abstract: A generic approach for automatically ingesting data into an HDFS (Hadoop File System) based data warehouse includes a datahub server, a generic pipelined data loading framework, and a meta-data model that, together, address data loading efficiency, data source heterogeneities, and data warehouse schema evolvement. The loading efficiency is achieved via the MapReduce scale-out solution. The meta-data model is comprised of configuration files and a catalog. The configuration file is setup per ingestion task. The catalog manages the data warehouse schema. When a scheduled data loading task is executed, the configuration files and the catalog collaboratively drive the datahub server to load the heterogeneous data to their destination schemas automatically.

Abstract: According to one embodiment of the present invention, a method for managing uncertain data is provided. The method includes specifying data uncertainty using at least one variable generation (VG) function. The VG function generates pseudorandom samples of uncertain data values. A random database based on the VG function is specified and multiple Monte Carlo instantiations of the random database are generated. Using a Monte Carlo method, a query is repeatedly executed over the multiple Monte Carlo instantiations to output a Monte Carlo method result and associated query-results. The Monte Carlo method result may then be used to estimate statistical properties of a probability distribution of the query-result.

Abstract: According to one embodiment of the present invention, a method for managing uncertain data is provided. The method includes specifying data uncertainty using at least one variable generation (VG) function, wherein the VG function generates pseudorandom samples of uncertain data values. A random database based on the VG function is specified. and multiple Monte Carlo instantiations of the random database are generated. Using a Monte Carlo method, a query is repeatedly executed over the multiple Monte Carlo instantiations to output a Monte Carlo method result and associated query-results. The Monte Carlo method result may then be used to estimate statistical properties of a probability distribution of the query-result.

Abstract: According to one embodiment of the present invention, a method for managing uncertain data is provided. The method includes specifying data uncertainty using at least one variable generation (VG) function, wherein the VG function generates pseudorandom samples of uncertain data values. A random database based on the VG function is specified. and multiple Monte Carlo instantiations of the random database are generated. Using a Monte Carlo method, a query is repeatedly executed over the multiple Monte Carlo instantiations to output a Monte Carlo method result and associated query-results. The Monte Carlo method result may then be used to estimate statistical properties of a probability distribution of the query-result.