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.

翻译：数据整理仍是数据科学流水线中最耗时的任务，无线网络数据亦不例外。先前自动或辅助数据整理方法主要针对无序的单表数据。然而，与传统数据集中表内行无序且假设彼此独立不同，无线网络数据集通常通过多个测量设备采集，生成多个需整合以获取完整数据集的时间有序表。例如，要创建地理区域内5G基站信号质量的数据集，需将手机收集的GPS数据与对应基站的射频测量数据连接，但连接键的时间戳通常存在采样周期不匹配导致的对齐问题。通用时间序列数据集的数据整理技术在此同样失效，因为其缺乏由网络协议和系统配置定义的领域特定数据语义知识。为辅助无线网络数据集整理，我们演示了WN-Wrangle——一个面向无线网络领域的交互式整理助手，可推荐前k个最佳后续整理操作并提供丰富的领域特定解释。在底层，WN-Wrangle通过强制执行时间约束及基于无线网络语义感知的机制，对扩展的整理操作符集进行评分与排序以提升数据质量。我们展示了WN-Wrangle如何识别无线网络领域特有的隐蔽数据质量问题，并针对从广泛使用的城市级无线测试平台POWDER获取的数据集推荐精确的整理步骤。