Frameworks for entity matching: A comparison
Data & Knowledge Engineering
Wrangler: interactive visual specification of data transformation scripts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data collection by the people, for the people
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Profiler: integrated statistical analysis and visualization for data quality assessment
Proceedings of the International Working Conference on Advanced Visual Interfaces
Interactive analysis of big data
XRDS: Crossroads, The ACM Magazine for Students - Big Data
Information Visualization - Special issue on State of the Field and New Research Directions
De-duplication of aggregation authority files
International Journal of Metadata, Semantics and Ontologies
De-duplication of aggregation authority files
International Journal of Metadata, Semantics and Ontologies
Hi-index | 0.00 |
Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.