Abstract: Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and/or further analyzed to produce an analysis outcome.

Abstract: Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and/or further analyzed to produce an analysis outcome.

Abstract: Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and/or further analyzed to produce an analysis outcome.

Abstract: A method for monitoring the depth of at least one via (11) in a wafer including the steps of arranging the via (11) as a capacitive plate (21), providing a corresponding capacitive plate (23), applying an electrical potential difference to the via (11) and the corresponding capacitive plate (23), measuring the resultant capacitance between the via (11) and a corresponding capacitive plate (23) and determining the depth of the at least one via (11) by the capacitance.

Abstract: A method for monitoring the depth of at least one via (11) in a wafer comprising the steps of arranging the via (11) as a capacitive plate (21), providing a corresponding capacitive plate (23), applying an electrical potential difference to the via (11) and the corresponding capacitive plate (23), measuring the resultant capacitance between the via (11) and a corresponding capacitive plate (23) and determining the depth of the at least one via (11) by the capacitance.