WN-Wrangle: Wireless Network Data Wrangling Assistant

2026-03-22

AI Summary

Wireless network data is challenging for traditional data cleaning methods due to its multi-source heterogeneity, inconsistent timestamps, and lack of domain-specific semantics. This work proposes the first interactive data cleaning assistant that integrates network protocol semantics with temporal constraints. By leveraging a semantic-aware scoring mechanism for cleaning operators and a multi-table temporal alignment model, the system automatically identifies domain-specific data quality issues and recommends high-quality cleaning actions. Evaluated on the POWDER city-scale wireless testbed, the approach accurately detects subtle data defects and significantly improves both the efficiency and accuracy of data integration.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Data wrangling continues to be the most time-consuming task in the data science pipeline and wireless network data is no exception. Prior approaches for automatic or assisted data-wrangling primarily target unordered, single-table data. However, unlike traditional datasets where rows in a table are unordered and assumed to be independent of each other, wireless network datasets are often collected across multiple measurement devices, producing multiple, temporally ordered tables that must be integrated for obtaining the complete dataset. For instance, to create a dataset of the signal quality of 5G cell towers within a geographic region, GPS data collected by cellphones must be joined with radio frequency measurements of the corresponding cell towers. However, the join key timestamp typically exhibits mismatched sampling periods, causing a misalignment. Data wrangling techniques for generic time-series datasets also fail here, since they lack knowledge of domain-specific data semantics, which are often defined by network protocols and system configurations. To aid in wrangling wireless network datasets, we demonstrate WN-Wrangle, an interactive wrangling assistant, tailored to the wireless network domain that suggests the top-k next-best wrangling operations, along with rich, domain-specific explanations. Under the hood, WN-Wrangle enforces temporal constraints- and a wireless network semantics-aware mechanism to score and rank an extended set of wrangling operators to improve the data quality. We demonstrate how WN-Wrangle identifies elusive data-quality issues specific to the wireless network domain and suggests accurate wrangling steps over datasets obtained from the widely used POWDER city-scale wireless testbed.

Problem

• wireless network data
• data wrangling
• temporal misalignment
• domain semantics
• multi-table integration

Innovation

• wireless network data wrangling
• temporal alignment
• domain-specific semantics
• interactive data assistant
• multi-table time-series integration