Data quality in a big data context: about Twitter’s data quality
"In each of the phases of a Big Data analysis process, Data Quality (DQ) plays a key role. Given the particular characteristics of the data at hand, the traditional DQ methods, based on quality dimensions and metrics, must be adapted and extended, in order to capture the new characteristics tha...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Proyecto final de Grado |
| Lenguaje: | Inglés |
| Publicado: |
2018
|
| Materias: | |
| Acceso en línea: | http://ri.itba.edu.ar/handle/123456789/1172 |
| Aporte de: |
| Sumario: | "In each of the phases of a Big Data analysis process, Data Quality (DQ) plays a key role. Given the particular characteristics of the data at hand, the traditional DQ methods, based on quality dimensions and metrics, must be adapted and extended, in order to capture the new characteristics that Big Data introduces. This paper dives into this problem, re-defining the DQ dimensions and metrics for a Big Data scenario, where the data arrives, in this particular case, as unstructured documents in real time, such as JSON objects. This general scenario is instantiated to study the concrete case of Twitter feeds. Further, the paper also describes the implementation of a system that acquires tweets in real time, and computes the quality of each tweet, applying the quality metrics that are defined formally in the paper. The
implementation includes a web user interface that allows filtering the tweets, for example, by keywords, and visualizing the quality of a data stream in many different ways. Experiments are performed and their results discussed." |
|---|