Clio grows up: from research prototype to industrial tool

By L. Haas, Mauricio A. Hernández, Howard Ho, Lucian Popa, M. Roth. ACM SIGMOD Conference, 2005

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bb8087d663948c373145ad0ad97ef6d97c948832

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Citations in the corpus

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, unknown): “Data cleaning and wrangling systems address data quality issues by using integrity constraints [27], type inference [36], schema matching [43], outlier detection [51] and more.”
Schema mappings, data exchange, and metadata management (2005, background): “Some of the ﬁndings of this investigation inﬂuenced the development of Clio, which, in the meantime, has evolved from a prototype to an industrial-strength tool [19].”
Semantic Matching: Algorithms and Implementation (2007, background): “, query expressions that automatically translate/exchange data instances between the information sources, see, for example, [16,48].”
Profiler: integrated statistical analysis and visualization for data quality assessment (2012, background): “Many of these interfaces focus on data integration [9, 15, 21, 27, 34] or entity resolution [17].”
Enterprise Data Analysis and Visualization: An Interview Study (2012, methodology): “Examples include visualizations to assist schema mapping for data integration [13, 33] and visual analytics for data deduplication [6, 23].”
Data Wrangling: The Challenging Yourney from the Wild to the Lake (2015, background): “…evolution from the solid foundation of work in data federation and data integration, including Extract-Transform-Load (ETL) techniques, data cleansing, schema integration and metadata management systems [13][15][29][33] provide a historical perspective of the research challenges in these areas.”
Model management 2.0: manipulating richer mappings (2007, methodology): “The Clio project has papers explaining how to interpret mappings over XML schemas [45][99], and in [ 49 ] how to generate XSLT transformations.”
Wrangler: interactive visual specification of data transformation scripts (2011, methodology): “These techniques include methods for detecting erroneous values [10, 11], information extraction [1, 25], entity resolution [6], type inference [7], and schema matching [9, 21].”