Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
AJAX: an extensible data cleaning tool
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Supporting Fine-grained Data Lineage in a Database Visualization Environment
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Why and Where: A Characterization of Data Provenance
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Lineage Tracing for General Data Warehouse Transformations
Proceedings of the 27th International Conference on Very Large Data Bases
Declarative Data Cleaning: Language, Model, and Algorithms
Proceedings of the 27th International Conference on Very Large Data Bases
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
Practical Lineage Tracing in Data Warehouses
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Optimizing ETL Processes in Data Warehouses
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data mapper: an operator for expressing one-to-many data transformations
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Data quality problems arise in different application contexts and require appropriate handling so that information becomes reliable. Examples of data anomalies are: missing values, the existence of duplicates, misspellings, data inconsistencies and wrong data formats. Current technologies handle data quality problems through: (i) software programs written in a programming language (e.g., C or Java) or an RDBMS programming language, (ii) the integrity constraints mechanisms offered by relational database management systems; or (iii) using a commercial data quality tool. None of these approaches is appropriate when handling non-conventional data applications dealing with large amounts of information. In fact, the existing technology is not able to support the design of a data flow graph that effectively and efficiently produce clean data. AJAX is a data cleaning and transformation tool that overcomes these aspects. In this paper, we present an overview of the entire set of functionalities supported by the AJAX system. First, we explain the logical and physical levels of the AJAX framework, and the advantages brought in terms of specification and optimization of data cleaning programs. Second, the set of logical data cleaning and transformation operators is described and exemplified, using the declarative language proposed. Third, we illustrate the purpose of the debugging facility and how it is supported by the exception mechanism offered by logical operators. Finally, the architecture of the AJAX system is presented and experimental validation of the prototype is briefly referred.