Abstract: The present disclosure involves systems, software, and computer implemented methods for determining and visualizing effective mask expressions. One example method includes identifying a request for an object in a software application. The request is associated with a particular user. An object hierarchy associated with the requested object is identified. At least one column in the object hierarchy is associated with a mask expression. A current dependent object in the identified object hierarchy is determined. Masking status data for the current dependent object is determined that identifies whether masking is to be applied to the current dependent object when fulfilling the request. The generated masking status data is used to determine which masking expressions are to be applied to which columns in the object hierarchy when responding to the request.

Abstract: The present disclosure involves systems, software, and computer implemented methods for determining and visualizing effective mask expressions. One example method includes identifying a request for an object in a software application. The request is associated with a particular user. An object hierarchy associated with the requested object is identified. At least one column in the object hierarchy is associated with a mask expression. A current dependent object in the identified object hierarchy is determined. Masking status data for the current dependent object is determined that identifies whether masking is to be applied to the current dependent object when fulfilling the request. The generated masking status data is used to determine which masking expressions are to be applied to which columns in the object hierarchy when responding to the request.

Abstract: Embodiments include a system for matching an element of a source schema to an element of a target schema. The system includes a processing unit and a communication unit. The processing unit may be configured to: identify a sample data item of the element of the target schema; match a part of the sample data item to a part of a sample instance of the source schema; and match the element of the source schema to which the part of the sample instance of the source schema belongs to the element of the target schema. The communication unit may be configured to: provide the sample data item through an interface and receive the sample instance of the source schema.

Abstract: An enhanced data conversion framework, in which a data record in each of first and second data sources is populated with manually selected, representative sample data, the first and second data sources using different data storage schemas to store the representative sample data as instance values of instance elements. Parameters for a CONCATENATE function or an EXTRACT function are automatically determined based on a selected succession graph, and non-sample data is converted between the different data storage schemas of the first and second data sources, using the CONCATENATE function or the EXTRACT function.

Abstract: Methods and systems are described that involve recognizing complex entities from text documents with the help of structured data and Natural Language Processing (NLP) techniques. In one embodiment, the method includes receiving a document as input from a set of documents, wherein the document contains text or unstructured data. The method also includes identifying a plurality of text segments from the document via a set of tagging techniques. Further, the method includes matching the identified plurality of text segments against attributes of a set of predefined entities. Lastly, a best matching predefined entity is selected for each text segment from the plurality of text segments. In one embodiment, the system includes a set of documents, each document containing text or unstructured data. The system also includes a database storage unit that stores a set of predefined entities, wherein each entity contains a set of attributes.

Abstract: Methods and systems are described that involve recognizing complex entities from text documents with the help of structured data and Natural Language Processing (NLP) techniques. In one embodiment, the method includes receiving a document as input from a set of documents, wherein the document contains text or unstructured data. The method also includes identifying a plurality of text segments from the document via a set of tagging techniques. Further, the method includes matching the identified plurality of text segments against attributes of a set of predefined entities. Lastly, a best matching predefined entity is selected for each text segment from the plurality of text segments. In one embodiment, the system includes a set of documents, each document containing text or unstructured data. The system also includes a database storage unit that stores a set of predefined entities, wherein each entity contains a set of attributes.

Abstract: An enhanced data conversion framework, in which a data record in each of first and second data sources is populated with manually selected, representative sample data, the first and second data sources using different data storage schemas to store the representative sample data as instance values of instance elements. Parameters for a CONCATENATE function or an EXTRACT function are automatically determined based on a selected succession graph, and non-sample data is converted between the different data storage schemas of the first and second data sources, using the CONCATENATE function or the EXTRACT function.

Abstract: A method and system are described for including data quality in data streams. An example method may include obtaining a first group of data items, each data item including one or more data attribute values. A first group of data quality items may be determined, each data quality item including one or more data quality attribute values associated with one of the data items of the first group. A first aggregated data quality value may be determined based on the first group of data quality items. A first data stream interval including the first group of data items and the first aggregated data quality value may be output.

Abstract: A method and system are described for managing data quality. An example method may include obtaining a first data stream interval including a first group of data items and a first aggregated data quality value associated with a quality of obtaining the first group, each data item including data attribute values, each data quality item including data quality attribute values associated with one of the data items. The first aggregated data quality value, a first indicator associating the first aggregated data quality value with the first group, and the first group may be selected. The first group and the first indicator may be stored in a user table of a database. A data quality table associated with the user table may be determined based on an entry in a system table. The first aggregated data quality value and the first indicator may be stored in the data quality table.

Abstract: Embodiments include a system for matching an element of a source schema to an element of a target schema. The system includes a processing unit and a communication unit. The processing unit may be configured to: identify a sample data item of the element of the target schema; match a part of the sample data item to a part of a sample instance of the source schema; and match the element of the source schema to which the part of the sample instance of the source schema belongs to the element of the target schema. The communication unit may be configured to: provide the sample data item through an interface and receive the sample instance of the source schema.

Abstract: A method and system are described for including data quality in data streams. An example method may include obtaining a first group of data items, each data item including one or more data attribute values. A first group of data quality items may be determined, each data quality item including one or more data quality attribute values associated with one of the data items of the first group. A first aggregated data quality value may be determined based on the first group of data quality items. A first data stream interval including the first group of data items and the first aggregated data quality value may be output.

Abstract: A method and system are described for managing data quality. An example method may include obtaining a first data stream interval including a first group of data items and a first aggregated data quality value associated with a quality of obtaining the first group, each data item including data attribute values, each data quality item including data quality attribute values associated with one of the data items. The first aggregated data quality value, a first indicator associating the first aggregated data quality value with the first group, and the first group may be selected. The first group and the first indicator may be stored in a user table of a database. A data quality table associated with the user table may be determined based on an entry in a system table. The first aggregated data quality value and the first indicator may be stored in the data quality table.