Sap Se

Business Objects Software Limited

Prasanthi Thatavarthy

Jeffrey Woody

Ronald Dupey

Ryan Champlin

Mark Spiess

Srinivasan Kumar

Kimberly Starks

Michael Alexander

Chad Taylor

Ron Dupey

Shibajee Dutta Gupta

Jeff Woody

A processing tool is described that can evaluate a plurality of fields in a plurality of records and assign a scoring measure to each field based on the evaluation of each field. The scoring measures can provide an indication as to the uniqueness in content within each field, which can assist with determining one or more fields that are most useful for performing a search to identify similar records.

Domain agnostic similarity detection

Methods and systems are described for providing an editor for modifying predefined data match policies. The methods and systems can receive search requests including search criteria and obtain data records and a plurality of respective data match policies for the data records that correspond to the search criteria. The methods and systems can display a first set of data records and a second set of data records. The first set of data records may match the search criteria and the second set of data records may be identified as a partial match to one or more data match policies associated with the first set of data records. The methods and systems can receive a request to modify a data match policy corresponding to at least one of the second set of data records and generate and display a plurality of suggestions for executing the at least one request.

Smart match tuning

A system includes reception of a first data record, identification of one or more component elements in the first data record, application of a cleansing operation to change a first component element of the one or more component elements to a first cleansed component element, the first cleansed component element being different from the first component element, automatic determination a change significance of the change, and storage an identifier of the first cleansed component element in association with an identifier of the change significance.

Significant cleanse change information

A system includes identification of a group of records of a plurality of records of a database table associated with a plurality of columns, each of the plurality of records including zero or one value for each column, determination of a plurality of rules, each rule associated with one or more of the plurality of columns and for determining a record from which to select values for the associated one or more columns, evaluation of each of the plurality of rules to determine, for each rule, a record of the group of records, and determination of a first record of the group of records based on the record determined for each rule.

Dynamic master record selection

A system includes reception of first address data associated with a first location, the first address data comprising first geo-coordinates, determination of a first two or more candidate cities based on the first address data, identification of a first one of the first two or more candidate cities based on the first geo-coordinates, and generation of second address data associated with the first location based on the identified first one of the first two or more candidate cities.

Address remediation using geo-coordinates

A system includes identification of a first one or more columns of a first database table as associated with a first entity and a first one or more columns of a second database table as associated with the first entity, mapping of the data of each row of the first one or more columns of the first database table to respective first rows of a predetermined schema associated with the entity, mapping of the data of each row of the first one or more columns of the second database table to respective second rows of the predetermined schema associated with the entity, determining of matching ones of the first rows and the second rows, and based on the matching ones of the first rows and the second rows, identification of the first one or more columns of the first database table and the first one or more columns of the second database table as a JOIN key between the first database table and the second database table.

Fuzzy join key

The subject matter disclosed herein provides methods for identifying duplicate data records using a graphical user interface. One or more data records may be accessed from one or more source files. The data records may have one or more data fields associated with one or more data types. One or more match themes may be proposed based on the data types. The match themes may have one or more rules for identifying duplicate data records. A selection of a match theme and at least one rule associated with the selected match theme may be received. The data records may be processed using the selected match theme and rules to identify the duplicate data records. A graphical user interface previewing the duplicate data records may be displayed. The duplicate data records may be organized into match groups. Related apparatus, systems, techniques, and articles are also described.

Graphical user interface for a data record matching application

Technical solutions for detection potential data quality problems are provided. In some implementations, a method includes: automatically without human intervention, identifying a subset of side effect data associated with a set of enterprise data. The side effect data include a plurality of data fields. The method further includes: selecting a first set of data quality detection rules in accordance with a first data field in the plurality of data fields; identifying one or more candidate data quality problems in the set of side effect data by comparing the set of side effect data to the first set of data quality detection rules; and responsive to identifying the one or more candidate data quality problems: causing to be displayed to a user: information representing the one or more candidate data quality problems; and one or more candidate solutions for correcting the one or more candidate data quality problems.

Automatic detection of potential data quality problems

According to some embodiments, a plurality of records may be received, and a data quality platform may execute a matching process to match at least some records with each other. A replay request associated with at least two of the received records may be received from a user, and information about the at least two records and a first decision in the matching process may be graphically displayed via a user interface. A result of the first decision in the matching process may then be graphically displayed to the user. Similarly, information about the at least two records and a second decision in the matching process, subsequent to the first decision, may be graphically displayed in the user interface. A result of the second decision in the matching process may then be graphically displayed to the user.

Graphical record matching process replay for a data quality user interface

Various embodiments of systems and methods for aligning records for visual comparison are described herein. In one aspect, the method includes identifying a plurality of records. A reference record is selected from the plurality of records. One or more non-reference record is merged into the reference record to generate a merged reference record. The merged reference record includes one or more fields missing in the reference record. The missing fields are included as blank fields from the non-reference records. The merged reference record is merged into a non-reference record to generate the merged non-reference record. The merged non-reference record includes fields missing in the non-reference record. Missing fields are included as blank fields. The merged records are displayed on a user interface in a card view.

Growing community of inventors

Onalaska, WI, United States of America

Prasanthi Thatavarthy