A Conceptual Approach to Traffic Data Wrangling

Mashael Al-Jubairah, Sandra Sampaio, Hapsoro Adi Permana, Pedro Sampaio

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Data Wrangling (DW) is the subject of growing interest given its potential to improve data quality. DW applies interactive and iterative data profiling, cleaning, transformation, integration and visualization operations to improve the quality of data. Several domain inde- pendent DW tools have been developed to tackle data quality issues across domains. Using generic data wrangling tools requires a time- consuming and costly DW process often involving advanced IT knowledge beyond the skills set of traffic analysts. In this paper, we propose a conceptual approach to data wrangling for traffic data by creating a domain-specific language for specifying traffic data wrangling tasks and an abstract set of wrangling operators that serve as the target conceptual construct for mapping domain-specific wrangling tasks. The conceptual approach discussed in this paper is tool-independent and platform agnostic and can be mapped into specific implementations of DW functions available in existing scripting languages and tools such as R, Python, Trifacta. Our aim is to enable a typical traffic analyst without expert Data Science knowledge to be able to perform basic DW tasks relevant to his domain.
Original languageEnglish
Title of host publicationData Analytics
Subtitle of host publicationProceedings of 31st British International Conference on Databases, BICOD 2017
EditorsAndrea Cali, Peter Wood, Nigel Martin, Alexandra Poulovassilis
Place of PublicationCham
PublisherSpringer Nature
Number of pages12
ISBN (Electronic)9783319607955
ISBN (Print)9783319607948
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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