Abstract: An electrical insulation device includes an insulator body having a chamber and an electrically insulating material within the chamber. The electrically insulating material includes a dry syntactic foam.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: An electrical insulation device includes an insulator body having a chamber and an electrically insulating material within the chamber. The electrically insulating material includes a dry syntactic foam.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: A processor receives statistical information about a data set included in a column of a data table. The processor receives additional information about the data set that indicates a data format utilized by the data set and a type of information represented by the data set. The processor generates a data dictionary for compression of the data set based, at least in part, on the statistical information and the additional information. The data dictionary is created such that the data dictionary is capable of compressing data that is statistically predicted to be received at a future point.

Abstract: Embodiments relate to processing a data set stored in a computer system. In one aspect, a method of processing a data set stored in a computer system includes providing one or more parameters for quantifying data quality of the data set. A processor generates, for each parameter of the one or more parameters, a reference pattern indicating a dysfunctional behavior of the values of the parameter. The data set is processed to obtain values of the one or more parameters. A parameter of the one or more parameters is identified whose obtained values match a corresponding reference pattern of the generated reference patterns. The identified parameter is assigned a resource weight value indicating the amount of processing resources required to fix the dysfunctional behavior of the identified parameter.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: Embodiments relate to processing a data set stored in a computer system. In one aspect, a method of processing a data set stored in a computer system includes providing one or more parameters for quantifying data quality of the data set. A processor generates, for each parameter of the one or more parameters, a reference pattern indicating a dysfunctional behavior of the values of the parameter. The data set is processed to obtain values of the one or more parameters. A parameter of the one or more parameters is identified whose obtained values match a corresponding reference pattern of the generated reference patterns. The identified parameter is assigned a resource weight value indicating the amount of processing resources required to fix the dysfunctional behavior of the identified parameter.

Abstract: Embodiments relate to processing a data set stored in a computer system. In one aspect, a method of processing a data set stored in a computer system includes providing one or more parameters for quantifying data quality of the data set. A processor generates, for each parameter of the one or more parameters, a reference pattern indicating a dysfunctional behavior of the values of the parameter. The data set is processed to obtain values of the one or more parameters. A parameter of the one or more parameters is identified whose obtained values match a corresponding reference pattern of the generated reference patterns. The identified parameter is assigned a resource weight value indicating the amount of processing resources required to fix the dysfunctional behavior of the identified parameter.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: Embodiments relate to accessing a set of data tables in a source database. A set of table categories is provided for tables in the source database and a set of metrics is provided. For each table of the set of the data tables: the set of metrics is evaluated, the evaluated set of metrics is analyzed, and the table is categorized into one of the set of table categories using the result of the analysis. Information indicative of the table category of each table of the set of tables is output, and in response, a request to select data tables of the set of data tables is received according to a part of the table categories for data processing. A subset of data tables of the set of data tables is selected using the table categories for performing the data processing on the subset of data tables.

Abstract: In an embodiment of the invention, a method for data profiling incorporating an enterprise service bus (ESB) coupling the target and source systems following an extraction, transformation, and loading (ETL) process for a target system and a source system is provided. The method includes receiving baseline data profiling results obtained during ETL from a source application to a target application, caching the updates, determining current data profiling results within the ESB for cached updates, and triggering an action if a threshold disparity is detected upon the current data profiling results and the baseline data profiling results.

Abstract: In an embodiment of the invention, a method for data profiling incorporating an enterprise service bus (ESB) coupling the target and source systems following an extraction, transformation, and loading (ETL) process for a target system and a source system is provided. The method includes receiving baseline data profiling results obtained during ETL from a source application to a target application, caching the updates, determining current data profiling results within the ESB for cached updates, and triggering an action if a threshold disparity is detected upon the current data profiling results and the baseline data profiling results.

Abstract: The invention provides for a data processing system comprising an application server comprising at least one processor. Execution of the instructions cause the processor to: receive an analysis request, the analysis request comprising multiple data analysis commands for generating an analysis report descriptive of a structured data file; divide the commands into private analysis commands and public analysis commands; send the private analysis commands to a trusted distributed file system; send a portion of the public analysis commands to a public distributed file system; send a remainder of the public analysis commands to the trusted distributed file system; and generate the analysis report using public analysis results from the public distributed file system and trusted analysis results from the trusted distributed file system.

Abstract: A processor receives statistical information about a data set included in a column of a data table. The processor receives additional information about the data set that indicates a data format utilized by the data set and a type of information represented by the data set. The processor generates a data dictionary for compression of the data set based, at least in part, on the statistical information and the additional information. The data dictionary is created such that the data dictionary is capable of compressing data that is statistically predicted to be received at a future point.

Abstract: A computer implemented method, computer program product and system for data quality monitoring includes measuring a data quality of loaded data relative to a predefined data quality metric. The measuring the data quality includes identifying delta changes in at least one of the loaded data and the data quality rules relative to a previous measurement of the data quality of the loaded data. Logical calculus defined in the data quality rules is applied to the identified delta changes.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.

Abstract: A computer implemented method for classifying at least one source dataset of a computer system. The method may include providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system. The method may also include calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table. The method may further include classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.