Abstract: Given a number of records and a number of target classes to which these records belong to, a (weakly) supervised machine learning classification method leverages known possibly dirty classification rules, efficiently and accurately learns a classification model from training data, and applies the learned model to the data records to predict their classes.

Abstract: An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions send to the experts. The system solves the problem of schema mapping and record deduplication a holistic way by unifying these problems into a unified linkage problem.

Abstract: Given a number of records and a number of target classes to which these records belong to, a (weakly) supervised machine learning classification method leverages known possibly dirty classification rules, efficiently and accurately learns a classification model from training data, and applies the learned model to the data records to predict their classes.

Abstract: Fast record deduplication is accomplished by providing as an input, data records having multiple attributes, and local similarity functions of individual attributes with local similarity thresholds. Bin IDs are then generated based on the local similarity functions and the local similarity thresholds. The Bin IDs are unique identifiers of a respective bin of records, and the bin of records is a set of records that are possibly pairwise similar. Local candidate pairs are identified based on data records that share Bin IDs. The local candidate pairs are aggregated to produce a set of global candidate pairs. The set of global candidate pairs are filtered by deciding whether a pair of data records represents a duplicate.

Abstract: A data curation system is provided that includes various methods to enable efficient reuse of human and machine effort. To reuse effort, various facilities are presented that model, save, and allow for querying of provenance and state information of a curation workflow and allow for incremental, stateful transitions of the data and metadata thereof.

Abstract: An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions send to the experts. The system solves the problem of schema mapping and record deduplication a holistic way by unifying these problems into a unified linkage problem.

Abstract: An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions sent to the experts. The system solves the problem of schema mapping and record deduplication in a holistic way by unifying these problems into a unified linkage problem.

Abstract: Given a number of records and a number of target classes to which these records belong to, a (weakly) supervised machine learning classification method leverages known possibly dirty classification rules, efficiently and accurately learns a classification model from training data, and applies the learned model to the data records to predict their classes.

Abstract: Fast record deduplication is accomplished by providing as an input, data records having multiple attributes, and local similarity functions of individual attributes with local similarity thresholds. Bin IDs are then generated based on the local similarity functions and the local similarity thresholds. The Bin IDs are unique identifiers of a respective bin of records, and the bin of records is a set of records that are possibly pairwise similar. Local candidate pairs are identified based on data records that share Bin IDs. The local candidate pairs are aggregated to produce a set of global candidate pairs. The set of global candidate pairs are filtered by deciding whether a pair of data records represents a duplicate.

Abstract: A data curation system is provided that includes various methods to enable efficient reuse of human and machine effort. To reuse effort, various facilities are presented that model, save, and allow for querying of provenance and state information of a curation workflow and allow for incremental, stateful transitions of the data and metadata thereof.

Abstract: A very efficient computer system is presented to generate all pairs of records that have a certain similarity. Similarity is defined in terms of the textual similarity of the record attributes and/or absolute difference for numeric record attributes. Software assigns each record to a number of bins, and then compares pairs of records that belong to the same bin. This is more efficient than comparing all pairs of records since the number of records compared to each other is much smaller.

Abstract: A data curation system that includes various methods to enable efficient reuse of human and machine effort. To reuse effort, various facilities are presented that model, save, and allow the querying of provenance and state information of a curation workflow and allow for incremental, stateful transitions of the data and the metadata.

Abstract: A method for integrating data into a database comprises storing data comprising a plurality of records which each comprise a plurality of attributes; analyzing a sample of records from the plurality of records by: identifying duplicate pairs of records in the sample records; analyzing each attribute of each record of the duplicate pairs of records to identify a respective attribute condition which is indicative that the pairs of records are duplicates; wherein the method further comprises: comparing each attribute of a record with the respective attribute condition and, if the attribute satisfies the attribute condition, allocating the record to a disjoint group which comprises records with an attribute that satisfies the same respective attribute condition; identifying duplicate pairs of records in the records in each disjoint group; identifying duplicate pairs of records in records that are not allocated to a disjoint group; and consolidating each duplicate pair of records into one consolidated record and s

Abstract: An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions sent to the experts. The system solves the problem of schema mapping and record deduplication in a holistic way by unifying these problems into a unified linkage problem.

Abstract: An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions send to the experts. The system solves the problem of schema mapping and record deduplication a holistic way by unifying these problems into a unified linkage problem.

Abstract: A data curation system that includes various methods to enable efficient reuse of human and machine effort. To reuse effort, various facilities are presented that model, save, and allow the querying of provenance and state information of a curation workflow and allow for incremental, stateful transitions of the data and the metadata.

Abstract: An end-to-end data curation system and the various methods used in linking, matching, and cleaning large-scale data sources. The goal of this system is to provide scalable and efficient record deduplication. The system uses a crowd of experts to train the system. The system operator can optionally provide a set of hints to reduce the number of questions send to the experts. The system solves the problem of schema mapping and record deduplication a holistic way by unifying these problems into a unified linkage problem.

Abstract: A computer-implemented method comprising partitioning data representing an input instance of a database including multiple tuples into multiple fragments of tuples, detecting tuples which violate a data quality specification in respective ones of the fragments, selecting a data cleaning asset on the basis of characteristics of errors in detected tuples for a fragment and based on declared asset capabilities, assigning a selected data cleaning asset to the fragment, the selected data cleaning asset to provide a set of candidate corrections for the detected tuples in the fragment, providing data representing an output instance of the database in which detected tuples are replaced with selected candidate corrections.

Abstract: A method for integrating data into a database comprises storing data comprising a plurality of records which each comprise a plurality of attributes; analysing a sample of records from the plurality of records by: identifying duplicate pairs of records in the sample records; analysing each attribute of each record of the duplicate pairs of records to identify a respective attribute condition which is indicative that the pairs of records are duplicates; wherein the method further comprises: comparing each attribute of a record with the respective attribute condition and, if the attribute satisfies the attribute condition, allocating the record to a disjoint group which comprises records with an attribute that satisfies the same respective attribute condition; identifying duplicate pairs of records in the records in each disjoint group; identifying duplicate pairs of records in records that are not allocated to a disjoint group; and consolidating each duplicate pair of records into one consolidated record and s

Abstract: A computer-implemented method comprising partitioning data representing an input instance of a database including multiple tuples into multiple fragments of tuples, detecting tuples which violate a data quality specification in respective ones of the fragments, selecting a data cleaning asset on the basis of characteristics of errors in detected tuples for a fragment and based on declared asset capabilities, assigning a selected data cleaning asset to the fragment, the selected data cleaning asset to provide a set of candidate corrections for the detected tuples in the fragment, providing data representing an output instance of the database in which detected tuples are replaced with selected candidate corrections.