Sap Ag

Sap Se

Hong-Hai Do

Gregor Hackenbroich

Anja Klein

Yinghua Ouyang

Taehyung Lee

Zhen Tian

Matthias Schmitt

Juergen Anke

Christian Drumm

Falk Brauer

Taeyoung Jeong

Xun Zhang

Yanchen Cao

Yeonwoon Jung

Wojciech Barczynski

Alexander Löser

Marcus Schramm

Dongmei Xv

1. Implementations of the present disclosure include receiving, by a database system, a query from an entity, providing a parse tree based on the query, the parse tree including nodes representative of operations to be executed and data objects stored within the database system, generating a module tree based on the parse tree, the module tree including a set of modules provided in sequential order from a root module to a leaf module, and executing an authorization check using the module tree by, for each module, determining a set of data objects and, for each data object in the set of data objects, determining whether the entity is one or more of authorized access the data object and perform an operation on the data object.

Authorization checks and auditing for queries in database systems

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.

Determination and visualization of effective mask expressions

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.

Schema matching for data migration

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 afunction or anfunction 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 thefunction or thefunction.

Enhanced data conversion framework

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. Further, the system includes a processor to identify a plurality of text segments from a document via a set of tagging techniques and to match the identified plurality of text segments against the set of attributes.

Graph based re-composition of document fragments for name entity recognition under exploitation of enterprise databases

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.

Method and system for including data quality in data streams

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.

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Dresden, Germany

Hong-Hai Do