Abstract: In some examples, a system receives a user input to create a classification to use for a first data set. In addition, the system may receive a user input to associate the classification with the first data set as reference data. The system may determine a classification association with the first data set based in part on comparing a first classification model corresponding to the reference data with a second classification model of a second data set, the second classification model being determined based at least in part on a plurality of data properties of the second data set. Further, the system may determine a user curation result with respect to the classification association with the first data set, and may update the classification model for the first data set based at least in part on the user curation result.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: A gateway device includes a network interface connected to data sources, and computer instructions, that when executed cause a processor to access data portions from the data sources. The processor accesses classification rules, which are configured to classify a data portion of the plurality of data portions as sensitive data in response to the data portion satisfying the rule. Each rule is associated with a significance factor representative of an accuracy of the classification rule. The processor applies each of the set of classification rules to a data portion to obtain an output of whether the data is sensitive data. The output are weighed by significance factors to produce a set of weighted outputs. The processor determines if the data portion is sensitive data by aggregating the set of weighted outputs, and presents the determination in a user interface. Security operations may also be performed on the data portion.

Abstract: Various embodiments of a method, system and article of manufacture to discover relationships among a first set of elements and a second set of elements are provided. At least one metric algorithm is identified based on a metric selection parameter. A raw result is determined based on the at least one metric algorithm, a first specified structural description of the first set of elements and a second specified structural description of the second set of elements. The raw result comprises a plurality of relationship measurements and the raw result is ordered. In some embodiments, a balanced result is produced based on the raw result and a matching strategy algorithm. In other embodiments, the matching strategy algorithm is identified based on a matching strategy selection parameter.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: In one aspect, semantics and relationships and mappings are identified between a first and a second data source. Data between the first and second data source is compared. A binding condition is discovered between portions of data in the first and the second data source based upon the comparison, wherein the binding condition identifies data within the first and second data sources that map to each other. The binding condition is used to discover correlations between portions of data in the first and the second data source, wherein the correlations identify data in the first data source that correspond to values in the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source, wherein the transformation function generates data in the second data source data in the first data source.

Abstract: Techniques are provided for viewing mappings between objects. A main view is displayed, wherein the main view shows one or more source objects, one or more target objects, and zero or more mappings between the one or more source objects and the one or more target objects. Input selecting a type of view to be displayed in the main view is received, wherein each type of view provides a different amount of detail regarding the mappings. In response to receiving the input, the selected type of view is created, and the created view is displayed. Additionally, techniques are provided for viewing objects. One or more objects along with mappings between the one or more objects are displayed. View filters are provided that may be applied to the one or more objects, wherein the view filters enable hiding at least one of mapped or unmapped objects. Moreover, techniques are provided for viewing nodes. A structure is displayed that includes one or more nodes.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: An apparatus and method are described for the discovery of semantics, relationships and mappings between data in different software applications, databases, files, reports, messages, or systems. In one aspect, semantics and relationships and mappings are identified between a first and a second data source. A binding condition is discovered between portions of data in the first and the second data source. The binding condition is used to discover correlations between portions of data in the first and the second data source. The binding condition and the correlations are used to discover a transformation function between portions of data in the first and the second data source.

Abstract: Mechanisms for searching XML repositories for semantically related schemas from a variety of structured metadata sources, including web services, XSD documents and relational tables, in databases and Internet applications. A search is formulated as a problem of computing a maximum matching in pairwise bipartite graphs formed from query and repository schemas. The edges of such a bipartite graph capture the semantic similarity between corresponding attributes of the schema based on their name and type semantics. Tight upper and lower bounds are also derived on the maximum matching that can be used for fast ranking of matchings whilst still maintaining specified levels of precision and recall. Schema indexing is performed by ‘attribute hashing’, in which matching schemas of a database are found by indexing using query attributes, performing lower bound computations for maximum matching and recording peaks in the resulting histogram of hits.

Abstract: Provided are techniques for testing quality of relationship discovery. Reference mappings and test mappings are received, wherein each of the reference mappings and test mappings includes one or more mappings, and wherein each of the mappings includes a mapping input and a mapping output, Each of the reference mappings and test mappings is parsed to generate a reference structure and a test structure, wherein the reference structure and test structure each contains entries with mapping outputs as keys matched with mapping inputs as values. The reference structure and the test structure are compared to determine the quality of relationships discovered in the test mappings.

Abstract: Provided are a method, system, and program for determining compliance of a database architecture to an enterprise data standard. A physical model is generated defining database elements in a database. A logical model is provided representing a definition of elements and their relationships. The logical model is used to generate a mapping of database element names in the physical model to corresponding elements in the logical model. The mapping and the logical model are processed to determine an extent to which the database elements and relationships in the physical model violate rules of the logical model.