Data Cleaning

Chu, Xu; Ilyas, Ihab F.; Krishnan, Sanjay; Wang, Jiannan

doi:10.1145/2882903.2912574

Cited by 302 publications

(69 citation statements)

References 63 publications

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“…Following the decoding interview proceedings, a narrative data analysis approach as suggested by Creswell and Creswell (2017) was used to transcribe the audio recordings made during the decoding interview sessions and to analyse the data. After transcribing the data, it was cleansed by searching for faults and repairing them (Chu et al, 2016). As the discussions were open-ended, the transcripts also contained some illogical and repeated statements.…”

Section: Discussionmentioning

confidence: 99%

Decoding the underlying cognitive processes and related support strategies utilised by expert instructors during source code comprehension

Khomokhoana

Nel²

2020

SACJ

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Many novice programmers fail to comprehend source code and its related concepts in the same way that their instructors do. As emphasised in the Decoding the Disciplines (DtDs) framework, each discipline (including Computer Science) has its own unique set of mental operations. However, instructors often take certain important mental operations for granted and do not explain these 'hidden' steps explicitly when modelling problem solutions. A clear understanding of the underlying cognitive processes and related support strategies employed by experts during source code comprehension (SCC) could ultimately be utilised to help novice programmers to better execute the cognitive processes necessary to efficiently comprehend source code. Positioned within Step 2 of the DtDs framework, this study employed decoding interviews and observations, followed by narrative data analysis, to identify the underlying cognitive processes and related support (though often 'hidden') strategies utilised by a select group of experienced programming instructors during an SCC task. The insights gained were then used to formulate a set of important cognitive-related support strategies for efficient SCC. Programming instructors are encouraged to continuously emphasise strategies like these when modelling their expert ways of thinking regarding efficient SCC more explicitly to their novice students.

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Section: Discussionmentioning

confidence: 99%

Decoding the underlying cognitive processes and related support strategies utilised by expert instructors during source code comprehension

Khomokhoana

Nel²

2020

SACJ

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“…Data cleaning techniques enable the detection and repair of data errors by identifying integrity constraints, duplicates, functional dependencies, and so on. These activities can be automatic or human-guided, and error detection techniques can be applied on the original database or later on in the data processing pipeline [13]. In this paper, the discovery of errors after some processing and integration is of particular importance given the use of large-scale cross comparisons and the integration of external sources.…”

Section: G Scalia Et Al / Towards a Scientific Data Framework To Sumentioning

confidence: 99%

“…The ChemKED format is supported through the PyKED utility (https://github.com/pr-omethe-us/PyKED) which allow to convert a ChemKED file in an equivalent ReSpecTh file 13. An experiment is interpretable by the framework if its definition has been added to the domain knowledge.…”

mentioning

confidence: 99%

Towards a scientific data framework to support scientific model development

Scalia

Pelucchi

Stagni

et al. 2019

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Editor: Silvio Peroni (https://orcid.org/0000-0003-0530-4305) Solicited reviews: HannaĆwiek-Kupczyńska (https://orcid.org/0000-0001-9113-567X); Aliaksandr Birukou (https://orcid.org/0000-0002-4925-9131); Stian Soiland-Reyes (https://orcid.org/0000-0001-9842-9718)Abstract. The sharing of scientific and scholarly data has been increasingly promoted over the last decade, leading to open repositories in many different scientific domains. However, data sharing and open data are not final goals in themselves, the real benefit is in data reuse, which allows leveraging investments in research and enables large-scale data-driven research progress.This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). 2451-8484 © 2019 -IOS Press and the authors. G. Scalia et al. / Towards a scientific data framework to support scientific model developmentFocusing on reuse, this paper discusses the design of an integrated framework to automatically take advantage of large amounts of scientific data extracted from the literature to support research, and in particular scientific model development. Scientific models reproduce and predict complex phenomena and their development is a rather challenging task, within which scientific experiments have a key role in their continuous validation. Starting from the combustion kinetics domain, this paper discusses a set of use cases and a first prototype for such a framework which leads to a set of new requirements and an architecture that can be generalized to other domains. The paper analyzes the needs, the challenges and the research directions for such a framework, in particular those related to data management, automatic scientific model validation, data aggregation and data analysis, to leverage large amounts of published scientific data for new knowledge extraction.

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“…After completing the data acquisition and collection, data cleaning checked the resulting dataset to identify and correct possible errors such as missing values, outlier values or different data formats (Chu, Ilyas, Krishnan, & Wang, 2016). This guarantees the highest degree of data reliability.…”

Section: Data Preparationmentioning

confidence: 99%

Open government data portals in the European Union: Considerations, development, and expectations

Espinosa

Luján-Mora

2019

Technological Forecasting and Social Change

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The goal of open government data (OGD) initiatives is to promote transparency, efficiency and public participation in public management policies. To do so, public organizations must consider which elements might help the development of their open government data portals (OGDP). This paper studies the evolution of OGDP in the 28 countries of the European Union (EU) in a multidisciplinary setting. Whereas the comparative frameworks in the literature are mostly based only on technological parameters, this exploratory research aims to uncover which factors might uphold the successful development of OGDP through the analysis of the relationships between a number of technical and socioeconomical indicators over a period of three years (2015-2017), using a clustering methodology. The results show that EU countries are slowly homogenizing their OGD approaches into two currents/speeds, based mainly on economic factors and open government development status. The originality of this research lies in the sense that it provides not only a technical benchmark, but also a longitudinal and multidisciplinary perspective that will add to the current formulation of OGD policies and practices in any international setting.

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Data Cleaning

Cited by 302 publications

References 63 publications

Decoding the underlying cognitive processes and related support strategies utilised by expert instructors during source code comprehension

Decoding the underlying cognitive processes and related support strategies utilised by expert instructors during source code comprehension

Towards a scientific data framework to support scientific model development

Open government data portals in the European Union: Considerations, development, and expectations

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