Data Preparation
- Raw data are often messy: they follow different encodings, records are not well structured, values do not adhere to patterns, etc. Such data are in general not fit to be ingested by downstream applications, such as data analytics tools, or even by data management systems. The act of obtaining information from raw data relies on some data preparation process. Data preparation is integral to advanced data analysis and data management, not only for data science but for any data-driven applications. Existing data preparation tools are operational and useful, but there is still room for improvement and optimization. With increasing data volume and its messy nature, the demand for prepared data increases day by day. <br /> To cater to this demand, companies and researchers are developing techniques and tools for data preparation. To better understand the available data preparation systems, we have conducted a survey to investigate (1) prominent data preparation tools, (2) distinctive tool features, (3) the need for preliminary dataRaw data are often messy: they follow different encodings, records are not well structured, values do not adhere to patterns, etc. Such data are in general not fit to be ingested by downstream applications, such as data analytics tools, or even by data management systems. The act of obtaining information from raw data relies on some data preparation process. Data preparation is integral to advanced data analysis and data management, not only for data science but for any data-driven applications. Existing data preparation tools are operational and useful, but there is still room for improvement and optimization. With increasing data volume and its messy nature, the demand for prepared data increases day by day. <br /> To cater to this demand, companies and researchers are developing techniques and tools for data preparation. To better understand the available data preparation systems, we have conducted a survey to investigate (1) prominent data preparation tools, (2) distinctive tool features, (3) the need for preliminary data processing even for these tools and, (4) features and abilities that are still lacking. We conclude with an argument in support of automatic and intelligent data preparation beyond traditional and simplistic techniques.…
Author details: | Mazhar HameedORCiD, Felix NaumannORCiDGND |
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DOI: | https://doi.org/10.1145/3444831.3444835 |
ISSN: | 0163-5808 |
ISSN: | 1943-5835 |
Title of parent work (English): | SIGMOD record |
Subtitle (English): | a survey of commercial tools |
Publisher: | Association for Computing Machinery |
Place of publishing: | New York |
Publication type: | Article |
Language: | English |
Date of first publication: | 2020/12/17 |
Publication year: | 2020 |
Release date: | 2023/01/06 |
Tag: | data cleaning; data quality; data wrangling |
Volume: | 49 |
Issue: | 3 |
Number of pages: | 12 |
First page: | 18 |
Last Page: | 29 |
Funding institution: | HPI research school on Data Science and Engineering |
Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
Peer review: | Referiert |