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：数据整理仍然是数据科学流程中最耗时的任务，无线网络数据也不例外。现有的自动或辅助数据整理方法主要针对无序的单表数据。然而，与传统数据集中表格各行无序且独立不同，无线网络数据集通常通过多个测量设备采集，生成多个按时间排序的表格，必须整合这些表格才能获得完整数据集。例如，要创建某个地理区域内5G基站信号质量的数据集，需要将手机采集的GPS数据与相应基站的射频测量数据进行连接。然而，连接键的时间戳通常存在采样周期不匹配的问题，导致数据错位。通用时间序列数据集的数据整理技术在此也不适用，因为它们缺乏对特定领域数据语义的了解，而这些语义通常由网络协议和系统配置定义。为了辅助无线网络数据集整理，我们展示了WN-Wrangle，一个面向无线网络领域的交互式整理助手。它能够推荐最优的前k个下一步整理操作，并提供丰富的领域特定解释。在底层，WN-Wrangle通过增强时间约束机制和无线网络语义感知机制，对扩展的整理操作符集进行评分和排序，以提升数据质量。我们演示了WN-Wrangle如何识别无线网络领域特有的隐蔽数据质量问题，并为从广泛使用的POWDER城市级无线测试平台获取的数据集建议准确的整理步骤。