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.

Data wrangling (DW) is a fundamental step to prepare data for downstream mining tasks. Recent studies explore large language models (LLMs) to form a lightweight DW paradigm. Such studies typically require prompting an LLM with a DW task together with a few examples as task demonstrations (i.e., in-context learning). A problem yet to be explored is how to select the examples, to maximize task effectiveness given constraints on the size of the examples. To fill this gap, we introduce the constrained Shapley value (CSV), a tailored variant of the Shapley value with a constraint on the LLM prompt size, to guide example selection. We show that CSV has desirable properties in example importance estimation. Using CSV directly for LLM-based DW is still computationally intractable. We further propose activated contribution (ACSV) as an unbiased estimation for CSV and sample allocation algorithms with approximation guarantees. Empirical results show that, compared with DW examples manually selected by experts, CSV improves the effectiveness of LLMs for DW tasks including schema mapping, entity matching, error detection, and missing value imputation by 5.90% averagly in F1 score, demonstrating the general applicability of CSV for in-context learning example selection towards DW tasks.

Entity Matching (EM) is a crucial task in data integration. Compared to deep learning-based EM solutions, tree-based machine learning models are more computationally effective and explainable, making them more applicable in real-world EM scenarios. However, Random Forest-based EM methods select features with a static feature engineering rule set for all attributes. Consequently, they suffer model retraining cost to select features, and can hardly customize to different EM tasks. To tackle this problem, we propose Description-Similarity Rules (DSR) for EM feature engineering. DSR introduces diverse attribute value distribution metrics and data-driven thresholds to traditional EM feature engineering rules. Unfortunately, both the DSR search space and its online model retraining costs are exponential. By pushing the model retraining to the offline stage, our DSR set mining algorithm is one order of magnitude faster than the baseline algorithms, taking only seconds for online selection. Empirically, DSR outperforms the feature engineering methods by 3.75 % on average F1 score, while reaching the state-of-the-art EM performance on several datasets.

The dictionary-based approach is one of the most representative types of time series classification (TSC) algorithm due to its high accuracy, efficiency, and good interpretability. However, existing studies focus on the centralized scenario where data from multiple sources are gathered. Considering that in many practical applications, data owners are reluctant to share their data due to privacy concerns, we study an unexplored problem involving collaboratively building the dictionary-based model over the data owners without disclosing their private data (i.e., in the federated scenario). We propose FedDict, a novel dictionarybased TSC approach customized for the federated setting to benefit from the advantages of the centralized algorithms. To further improve the performance and practicality, we propose a novel federated optimization algorithm for training logistic regression classifiers using dictionary features. The algorithm does not rely on any secure broker and is more accurate and efficient than existing solutions without hyper-parameter tuning. We also propose two contract algorithms for federated dictionary building, such that the user can flexibly balance the running time and the TSC performance through a predefined time limit. Extensive experiments on a total of 117 highly heterogeneous datasets validate the effectiveness of our methods and the superiority over existing solutions.

Unsupervised (a.k.a. Self-supervised) representation learning (URL) has emerged as a new paradigm for time series analysis, because it has the ability to learn generalizable time series representation beneficial for many downstream tasks without using labels that are usually difficult to obtain. Considering that existing approaches have limitations in the design of the representation encoder and the learning objective, we have proposed Contrastive Shapelet Learning (CSL), the first URL method that learns the general-purpose shapelet-based representation through unsupervised contrastive learning, and shown its superior performance in several analysis tasks, such as time series classification, clustering, and anomaly detection. In this paper, we develop TimeCSL, an end-to-end system that makes full use of the general and interpretable shapelets learned by CSL to achieve explorable time series analysis in a unified pipeline. We introduce the system components and demonstrate how users interact with TimeCSL to solve different analysis tasks in the unified pipeline, and gain insight into their time series by exploring the learned shapelets and representation.

With years of development, a significant number of Time Series Classification (TSC) algorithms have been proposed and applied to various fields such as scientific research and industry scenarios, including traditional statistical methods, machine learning methods, and recently deep learning models. However, choosing a suitable model along with good parameter values that perform well on a given task, which is also known as Combined Algorithm Selection and Hyperparameter optimization problem (CASH), is still challenging. How to automatically select the appropriate algorithm according to the task during analyzing is a topic worthy of further research. Nevertheless, for TSC, a field that has been developed for decades, there is no effective and efficient approach for automatic algorithm selection. To the best of our knowledge, the current approach is based on genetic search, which is very computationally intensive and time-consuming. Therefore, in this paper, we propose TSC-AutoML, a zero-configuration and meta-learning-based approach for the automatic Time Series Classification algorithm CASH (also known as TSC-CASH). TSC-AutoML extracts knowledge from historical tasks and performs automatic feature selection and knowledge filtering with a reinforcement learning policy. The experience extracted is filtered and transformed into metadata. The meta-learner trained on the metadata together with our proposed warm start strategy will select an optimal algorithm for tasks uploaded by users, and then our proposed Hyperparameter Optimization method based on the Fast Warm Start strategy searches for hyperparameter combinations of the selected algorithm and adjusts parameter configuration to achieve top performance. The entire process is pre-trained, automated for the new task, and parameter-free for the user to decide, making it easy for users with the little domain experience to get started easily. Experimental results illustrate that TSC-AutoML outperforms existing methods in terms of both time and accuracy of optimum algorithm selection.

Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vision) to encode the time series data and rely on strong assumptions to design learning objectives, which limits their ability to perform well. To deal with these problems, we propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation through a popular contrasting learning paradigm. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal, and a data augmentation library is employed to improve the generalization. We conduct extensive experiments using tens of real-world datasets to assess the representation quality on many downstream tasks, including classification, clustering, and anomaly detection. The results demonstrate the superiority of our method against not only URL competitors, but also techniques specially designed for downstream tasks. Our code has been made publicly available at https://github.com/real2fish/CSL.