VADA: an architecture for end user informed data preparation

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 t...
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Autor*in:	Konstantinou, Nikolaos [verfasserIn] Abel, Edward Bellomarini, Luigi Bogatu, Alex Civili, Cristina Irfanie, Endri Koehler, Martin Mazilu, Lacramioara Sallinger, Emanuel Fernandes, Alvaro A. A. Gottlob, Georg Keane, John A. Paton, Norman W.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Data preparation Data quality Data integration

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Journal of Big Data - Berlin : SpringerOpen, 2014, 6(2019), 1 vom: 21. Aug.
Übergeordnetes Werk:	volume:6 ; year:2019 ; number:1 ; day:21 ; month:08

Links:	Volltext

DOI / URN:	10.1186/s40537-019-0237-9

Katalog-ID:	SPR036632538

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520			\|a 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.
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allfields	10.1186/s40537-019-0237-9 doi (DE-627)SPR036632538 (SPR)s40537-019-0237-9-e DE-627 ger DE-627 rakwb eng Konstantinou, Nikolaos verfasserin aut VADA: an architecture for end user informed data preparation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Data preparation (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Abel, Edward aut Bellomarini, Luigi aut Bogatu, Alex aut Civili, Cristina aut Irfanie, Endri aut Koehler, Martin aut Mazilu, Lacramioara aut Sallinger, Emanuel aut Fernandes, Alvaro A. A. aut Gottlob, Georg aut Keane, John A. aut Paton, Norman W. (orcid)0000-0003-2008-6617 aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 21. Aug. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:21 month:08 https://dx.doi.org/10.1186/s40537-019-0237-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1 21 08
spelling	10.1186/s40537-019-0237-9 doi (DE-627)SPR036632538 (SPR)s40537-019-0237-9-e DE-627 ger DE-627 rakwb eng Konstantinou, Nikolaos verfasserin aut VADA: an architecture for end user informed data preparation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Data preparation (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Abel, Edward aut Bellomarini, Luigi aut Bogatu, Alex aut Civili, Cristina aut Irfanie, Endri aut Koehler, Martin aut Mazilu, Lacramioara aut Sallinger, Emanuel aut Fernandes, Alvaro A. A. aut Gottlob, Georg aut Keane, John A. aut Paton, Norman W. (orcid)0000-0003-2008-6617 aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 21. Aug. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:21 month:08 https://dx.doi.org/10.1186/s40537-019-0237-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1 21 08
allfields_unstemmed	10.1186/s40537-019-0237-9 doi (DE-627)SPR036632538 (SPR)s40537-019-0237-9-e DE-627 ger DE-627 rakwb eng Konstantinou, Nikolaos verfasserin aut VADA: an architecture for end user informed data preparation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Data preparation (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Abel, Edward aut Bellomarini, Luigi aut Bogatu, Alex aut Civili, Cristina aut Irfanie, Endri aut Koehler, Martin aut Mazilu, Lacramioara aut Sallinger, Emanuel aut Fernandes, Alvaro A. A. aut Gottlob, Georg aut Keane, John A. aut Paton, Norman W. (orcid)0000-0003-2008-6617 aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 21. Aug. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:21 month:08 https://dx.doi.org/10.1186/s40537-019-0237-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1 21 08
allfieldsGer	10.1186/s40537-019-0237-9 doi (DE-627)SPR036632538 (SPR)s40537-019-0237-9-e DE-627 ger DE-627 rakwb eng Konstantinou, Nikolaos verfasserin aut VADA: an architecture for end user informed data preparation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Data preparation (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Abel, Edward aut Bellomarini, Luigi aut Bogatu, Alex aut Civili, Cristina aut Irfanie, Endri aut Koehler, Martin aut Mazilu, Lacramioara aut Sallinger, Emanuel aut Fernandes, Alvaro A. A. aut Gottlob, Georg aut Keane, John A. aut Paton, Norman W. (orcid)0000-0003-2008-6617 aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 21. Aug. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:21 month:08 https://dx.doi.org/10.1186/s40537-019-0237-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1 21 08
allfieldsSound	10.1186/s40537-019-0237-9 doi (DE-627)SPR036632538 (SPR)s40537-019-0237-9-e DE-627 ger DE-627 rakwb eng Konstantinou, Nikolaos verfasserin aut VADA: an architecture for end user informed data preparation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Data preparation (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Data integration (dpeaa)DE-He213 Abel, Edward aut Bellomarini, Luigi aut Bogatu, Alex aut Civili, Cristina aut Irfanie, Endri aut Koehler, Martin aut Mazilu, Lacramioara aut Sallinger, Emanuel aut Fernandes, Alvaro A. A. aut Gottlob, Georg aut Keane, John A. aut Paton, Norman W. (orcid)0000-0003-2008-6617 aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 6(2019), 1 vom: 21. Aug. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:6 year:2019 number:1 day:21 month:08 https://dx.doi.org/10.1186/s40537-019-0237-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2019 1 21 08
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topic_title	VADA: an architecture for end user informed data preparation Data preparation (dpeaa)DE-He213 Data quality (dpeaa)DE-He213 Data integration (dpeaa)DE-He213
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title	VADA: an architecture for end user informed data preparation
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author_browse	Konstantinou, Nikolaos Abel, Edward Bellomarini, Luigi Bogatu, Alex Civili, Cristina Irfanie, Endri Koehler, Martin Mazilu, Lacramioara Sallinger, Emanuel Fernandes, Alvaro A. A. Gottlob, Georg Keane, John A. Paton, Norman W.
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title_sort	vada: an architecture for end user informed data preparation
title_auth	VADA: an architecture for end user informed data preparation
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. © The Author(s) 2019
abstractGer	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. © The Author(s) 2019
abstract_unstemmed	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. © The Author(s) 2019
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title_short	VADA: an architecture for end user informed data preparation
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author2	Abel, Edward Bellomarini, Luigi Bogatu, Alex Civili, Cristina Irfanie, Endri Koehler, Martin Mazilu, Lacramioara Sallinger, Emanuel Fernandes, Alvaro A. A. Gottlob, Georg Keane, John A. Paton, Norman W.
author2Str	Abel, Edward Bellomarini, Luigi Bogatu, Alex Civili, Cristina Irfanie, Endri Koehler, Martin Mazilu, Lacramioara Sallinger, Emanuel Fernandes, Alvaro A. A. Gottlob, Georg Keane, John A. Paton, Norman W.
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