arXiv subjects

Luca Zecchini

Publications and source records attributed to Luca Zecchini.

3 recordsLinked to original sources

arXiv2026

Clean Me If You Can: A Large Collection of Real-World Addresses for Data Cleaning Benchmarking

There has been extensive research on automating and scaling data cleaning, i.e., the detection and correction of erroneous values in tabular data. Yet, existing approaches often perform well only within controlled environments. One of the major bottlenecks in data cleaning research is the lack of real-world datasets. In this paper, we address this gap by providing a large, dirty dataset with postal entries and their corresponding ground truth. We discuss the design decisions and challenges for obtaining the dataset. We demonstrate the limitations of existing cleaning approaches when faced with our proposed datasets and derive guidelines for future research.

cs.DB↗

arXiv2023

DXP: Billing Data Preparation for Big Data Analytics

In this paper, we present the data preparation activities that we performed for the Digital Experience Platform (DXP) project, commissioned and supervised by Doxee S.p.A.. DXP manages the billing data of the users of different companies operating in various sectors (electricity and gas, telephony, pay TV, etc.). This data has to be processed to provide services to the users (e.g., interactive billing), but mainly to provide analytics to the companies (e.g., churn prediction or user segmentation). We focus on the design of the data preparation pipeline, describing the challenges that we had to overcome in order to get the billing data ready to perform analysis on it. We illustrate the lessons learned by highlighting the key points that could be transferred to similar projects. Moreover, we report some interesting results and considerations derived from the preliminary analysis of the prepared data, also pointing out some possible future directions for the ongoing project, spacing from big data integration to privacy-preserving temporal record linkage.

cs.DB↗

arXiv2023

Evaluation of Dataframe Libraries for Data Preparation on a Single Machine

Data preparation is a trial-and-error process that typically involves countless iterations over the data to define the best pipeline of operators for a given task. With tabular data, practitioners often perform that burdensome activity on local machines by writing ad hoc scripts with libraries based on the Pandas dataframe API and testing them on samples of the entire dataset-the faster the library, the less idle time its users have. In this paper, we evaluate the most popular Python dataframe libraries in general data preparation use cases to assess how they perform on a single machine. To do so, we employ 4 real-world datasets with heterogeneous features, covering a variety of scenarios, and the TPC-H benchmark. The insights gained with this experimentation are useful to data scientists who need to choose which of the dataframe libraries best suits their data preparation task at hand. In a nutshell, we found that: for small datasets, Pandas consistently proves to be the best choice with the richest API; when data fits in RAM and there is no need for complete compatibility with Pandas API, Polars is the go-to choice thanks to its in-memory execution and query optimizations; when a GPU is available, CuDF often yields the best performance, while for very large datasets that cannot fit in the GPU memory and RAM, PySpark (thanks to a multithread execution and a query optimizer) proves to be the best option.

cs.DB↗