Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.

Nowadays the demands for managing and analyzing substantially increasing collections of time series are becoming more challenging. Subsequence matching, as a core subroutine in time series analysis, has drawn significant research attention. Most of the previous works only focus on matching the subsequences with equal length to the query. However, many scenarios require support for efficient variable-length subsequence matching. In this paper, we propose a new representation, Uniform Piecewise Aggregate Approximation (UPAA) with the capability of aligning features for variable-length time series while remaining the lower bounding property. Based on UPAA, we present a compact index structure by grouping adjacent subsequences and similar subsequences respectively. Moreover, we propose an index pruning algorithm and a data filtering strategy to efficiently support variable-length subsequence matching without false dismissals. The experiments conducted on both real and synthetic datasets demonstrate that our approach achieves considerably better efficiency, scalability, and effectiveness than existing approaches.

Understanding the reasons behind differences between aggregated sequences derived from SQL queries is crucial for data scientists. However, existing methods often suffer from being labor-intensive, lacking scalability, providing only approximate solutions, and inadequately supporting sequence difference explanations. In response, we introduce SDEcho, a novel framework designed to automate the explanation searching for sequence differences in high-dimensional and high-volume datasets. SDEcho utilizes advanced pruning techniques, considering pattern, order, and dimension perspectives, as well as their interactions, to prune the entire explanation space while maintaining explanations accurate and concise. This hybrid pruning approach significantly accelerates the explanation searching process, making SDEcho a valuable tool for data analysis tasks. Extensive experiments on synthetic and real-world datasets, along with a case study, demonstrate that SDEcho outperforms existing methods in terms of both effectiveness and efficiency.