Name
Abstract | ||
|---|---|---|
The combination of data and technology is having a high impact on the way we live. The world is getting smarter thanks to the quantity of collected and analyzed data. However, it is necessary to consider that such amount of data is continuously increasing and it is necessary to deal with novel requirements related to variety, volume, velocity, and veracity issues. In this paper we focus on veracity that is related to the presence of uncertain or imprecise data: errors, missing or invalid data can compromise the usefulness of the collected values. In such a scenario, new methods and techniques able to evaluate the quality of the available data are needed. In fact, the literature provides many data quality assessment and improvement techniques, especially for structured data, but in the Big Data era new algorithms have to be designed. We aim to provide an overview of the issues and challenges related to Data Quality assessment in the Big Data scenario. We also propose a possible solution developed by considering a smart city case study and we describe the lessons learned in the design and implementation phases.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3216122.3216124 | IDEAS 2018: 22nd International Database Engineering & Applications Symposium
Villa San Giovanni
Italy
June, 2018 |
Field | DocType | ISBN |
Data science,Data quality,Computer science,Smart city,Compromise,Invalid Data,Big data,Data model,Database | Conference | 978-1-4503-6527-7 |
Citations | PageRank | References |
2 | 0.36 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cinzia Cappiello | 1 | 978 | 64.35 |
| Walter Samá | 2 | 3 | 0.74 |
| Monica Vitali | 3 | 64 | 11.82 |