Data quality issues are prevalent in information systems, making data cleaning a complex and time-consuming task, particularly with large-scale datasets and the lack of standardized automated processes. Existing cleaning pipelines often lack automated schemes to guide the execution of cleaning algorithms and the sequence of error corrections, limiting their practicality in real-world big data applications. To address the growing demand for advanced cleaning tools driven by the complexity of data activities, we propose the UniClean framework for on-demand big data cleaning. UniClean employs a unified cleaning operation (Uniop) from multiple cleaners to optimize the data cleaning workflow. It integrates a cleaning parameter generation pipeline, a cleaning parameter selection pipeline, and a module for cleaning process preparation and optimization, covering the entire workflow from cleaner modeling and data preparation to optimal cleaning operation generation. UniClean provides an adaptive (data-driven cleaning workflow generation) and flexible (multi-signal extension system) solution to meet the urgent need for high-quality data in today's data-driven decision-making environments. We demonstrate how UniClean effectively addresses the challenges of big data cleaning across diverse information system landscapes.

The scale of data analysis tasks have increased, highlighting the critical importance of data quality. Data quality assessment and repair have become pivotal in data preparation. Despite the availability of numerous algorithms for data cleaning, these often focus on optimizing efficiency and minimizing labor costs, neglecting the explicit relationship between data quality management costs and benefits. This omission can lead to the failure of promising data analysis solutions. To address this, we propose CBAClean, a comprehensive system that integrates cost-benefit analysis into data cleaning. CBAClean aims to assist users in quantifying the costs of data quality management and providing optimal data cleaning solutions tailored to their needs. Key features include task-centered multi-perspective data quality assessment, a comprehensive data quality repair operator library, fine-grained human role division for effective cost control, and recommendation of optimal data cleaning solutions based on cost-benefit calculations. By incorporating cost-benefit analysis, CBAClean enhances the practical application of data quality management on real-world data governance platforms.

Denial constraints are vital in data quality management, but traditional mining algorithms struggle with time series data. To address this, we introduce a novel data quality rule, threshold Denial Constraints ($t$DCs), which enables predicate scaling in numerical contexts. We formalize the inference system for$t$DCs and demonstrate the monotonicity and abruptness of threshold predicates. To efficiently mine$t$DCs, we design the tDCDiscover algorithm, which leverages batch computation of differences and thresholds to significantly reduce the time required for acquiring homologous predicate evidence, achieving a 50% -66% decrease. Additionally, we introduce an evidence matrix to store evidence, lowering the complexity of evidence matching from$O(m)$to$O(1)$. We propose two pruning strategies: triviality pruning and prediction coverage pruning, to effectively decrease the search paths to one-fifth of their original number and eliminating at least 90% of unnecessary paths. We theoretically prove that tDCDiscover ensures minimal, valid, and complete results. Experimental results on eight real-world datasets demonstrate that, compared to the current state-of-the-art denial constraint mining techniques, tDCDiscover achieves more than double the efficiency when processing high-dimensional time series data. In downstream data cleaning tasks, tDCDiscover improves error detection precision by an average of 40% and repair accuracy by 18%, further offering advantages in time series data quality management.

Data cleaning is an essential technique to enhance data quality. Despite the proposal of various algorithms with different cleaning strategies, current automated cleaning technologies still fall short of practical requirements when dealing with large-scale data containing mixed errors. This paper presents UniClean to efficiently solve the mixed error cleaning problem with three key technical contributions. (1) A unified construction and extension method for cleaners, enabling cleaning methods to easily utilize various cleaners to perform cleaning tasks. (2) Three optimization strategies to achieve efficiency-oriented cleaning preparation. (3) A cleaning algorithm based on an optimized cleaning process to effectively clean mixed errors. UniClean achieves a time complexity of O (| D error | 4 · | Op | + |D| · | D error |), significantly enhancing scalability. Experiments on public and large-scale enterprise datasets demonstrate that UniClean achieves over 40% improvement across five metrics, compared to five state-of-the-art cleaning methods, and delivers more than 30% gains in F1 and REDR on complex datasets, while completing the cleaning process within hours even for millions of records.

Real-world datasets often suffer from multiple quality issues, hindering downstream model performance and increasing cleaning costs. To address this, we propose DemandClean, a reinforcement learning-based adaptive data cleaning framework that dynamically balances cleaning effectiveness and operational costs. DemandClean explicitly considers data authenticity (alignment with real-world facts), diversity (richness of feature values), and downstream models' noise tolerance. We categorize data errors as missing (reducing authenticity and diversity), semantic (affecting only authenticity), and syntactic (affecting authenticity but potentially increasing diversity). Based on these errors, DemandClean intelligently selects among Repair, Delete, or No actions, guided by error rates and model robustness. For interpretability, the framework visually distinguishes authenticity, diversity, and tolerance. Extensive experiments confirm that DemandClean achieves near-optimal accuracy at substantially reduced preprocessing costs. Specifically, it reduces repair actions by 80.0% and deletions by 80.7% compared to "Repair All" strategies, while maintaining or even exceeding their predictive performance, thus offering an interpretable, cost-effective, and scalable solution for practical applications.