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24 results on '"Cristina Civili"'

1. VADA: an architecture for end user informed data preparation

Author

Nikolaos Konstantinou, Edward Abel, Luigi Bellomarini, Alex Bogatu, Cristina Civili, Endri Irfanie, Martin Koehler, Lacramioara Mazilu, Emanuel Sallinger, Alvaro A. A. Fernandes, Georg Gottlob, John A. Keane, and Norman W. Paton

Subjects
Data preparation, Data quality, Data integration, Computer engineering. Computer hardware, TK7885-7895, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract Background Data scientists spend considerable amounts of time preparing data for analysis. Data preparation is labour intensive because the data scientist typically takes fine grained control over each aspect of each step in the process, motivating the development of techniques that seek to reduce this burden. Results This paper presents an architecture in which the data scientist need only describe the intended outcome of the data preparation process, leaving the software to determine how best to bring about the outcome. Key wrangling decisions on matching, mapping generation, mapping selection, format transformation and data repair are taken by the system, and the user need only provide: (i) the schema of the data target; (ii) partial representative instance data aligned with the target; (iii) criteria to be prioritised when populating the target; and (iv) feedback on candidate results. To support this, the proposed architecture dynamically orchestrates a collection of loosely coupled wrangling components, in which the orchestration is declaratively specified and includes self-tuning of component parameters. Conclusion This paper describes a data preparation architecture that has been designed to reduce the cost of data preparation through the provision of a central role for automation. An empirical evaluation with deep web and open government data investigates the quality and suitability of the wrangling result, the cost-effectiveness of the approach, the impact of self-tuning, and scalability with respect to the numbers of sources.

Published
2019
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4. Enhancing DLV for Large-Scale Reasoning.

Author

Nicola Leone, Carlo Allocca, Mario Alviano, Francesco Calimeri, Cristina Civili, Roberta Costabile, Alessio Fiorentino, Davide Fuscà, Stefano Germano, Giovanni Laboccetta, Bernardo Cuteri, Marco Manna, Simona Perri, Kristian Reale, Francesco Ricca, Pierfrancesco Veltri, and Jessica Zangari

Published
2019
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5. Large-Scale Reasoning on Expressive Horn Ontologies.

Author

Carlo Allocca, Francesco Calimeri, Cristina Civili, Roberta Costabile, Bernardo Cuteri, Alessio Fiorentino, Davide Fuscà, Stefano Germano, Giovanni Laboccetta, Marco Manna, Simona Perri, Kristian Reale, Francesco Ricca, Pierfrancesco Veltri, and Jessica Zangari

Published
2019

9. Querying Large Expressive Horn Ontologies.

Author

Carlo Allocca, Mario Alviano, Francesco Calimeri, Cristina Civili, Roberta Costabile, Bernardo Cuteri, Alessio Fiorentino, Davide Fuscà, Stefano Germano, Giovanni Laboccetta, Nicola Leone, Marco Manna, Simona Perri, Kristian Reale, Francesco Ricca, Pierfrancesco Veltri, and Jessica Zangari

Published
2019

10. Large Scale DLV: Preliminary Results.

Author

Nicola Leone, Carlo Allocca, Mario Alviano, Francesco Calimeri, Cristina Civili, Roberta Costabile, Bernardo Cuteri, Alessio Fiorentino, Davide Fuscà, Stefano Germano, Giovanni Laboccetta, Marco Manna, Simona Perri, Kristian Reale, Francesco Ricca, Pierfrancesco Veltri, and Jessica Zangari

Published
2019

19. Data Context Informed Data Wrangling.

Author

Martin Koehler, Alex Bogatu, Cristina Civili, Nikolaos Konstantinou 0001, Edward Abel, Alvaro A. A. Fernandes, John A. Keane, Leonid Libkin, and Norman W. Paton

Published
2018

24. Data Context Informed Data Wrangling

Author

Edward Abel, John A. Keane, Martin Koehler, Alvaro A. A. Fernandes, Leonid Libkin, Cristina Civili, Norman W. Paton, Nikolaos Konstantinou, and Alex Bogatu

Subjects
Data Integration, FOS: Computer and information sciences, Data, context and interaction, Information Systems and Management, Control and Optimization, Computer Networks and Communications, Computer science, Big data, 02 engineering and technology, computer.software_genre, Maintenance engineering, Computer Science - Databases, 020204 information systems, Schema (psychology), 0202 electrical engineering, electronic engineering, information engineering, business.industry, Data Wrangling, 020207 software engineering, Databases (cs.DB), Data science, Data profiling, Computer Science - Distributed, Parallel, and Cluster Computing, Hardware and Architecture, Data Context, Distributed, Parallel, and Cluster Computing (cs.DC), business, computer, Information Systems, Data integration
Abstract

The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process have been carriedout using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. Cost-effective data wrangling processes need to ensure that data wrangling steps benefit from automation wherever possible. In this paper, we define a methodology to fullyautomate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Instance-based evidence together with data profiling paves the way to inform automation in several stepswithin the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair. The approach is evaluated with real estate data showing substantial improvements in the results of automated wrangling.

Published
2018
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