You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Time Series Data Cleaning: A Survey 
Chapter Title:  B. EVALUATION CRITERIA 
1. Abstract of Cited Paper (BIB001):  To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications. 
2. Abstract of Cited Paper (BIB002):  We introduce the notion of statistical distortion as an essential metric for measuring the effectiveness of data cleaning strategies. We use this metric to propose a widely applicable yet scalable experimental framework for evaluating data cleaning strategies along three dimensions: glitch improvement, statistical distortion and cost-related criteria. Existing metrics focus on glitch improvement and cost, but not on the statistical impact of data cleaning strategies. We illustrate our framework on real world data, with a comprehensive suite of experiments and analyses.

Literature Review:
The Root Mean Square (RMS) error BIB002 is used to evaluate the effectiveness of the cleaning algorithm. Let x denotes the sequence consisting of the true values of the time series, x denotes the sequence consisting of the observations after the error is added, and x denotes the sequence consisting of the repaired values after the cleaning. Here the RMS error BIB002 is represented as shown in equation BIB001 . The equation BIB001 measures the distance between the true value and the cleaned value. The smaller the RMS error, the better the cleaning effect. Other criteria include error distance between incorrect data and correct data, repaired distance between erroneous data and cleaned results (as shown in equation  referring to the minimum modification principle in data repairing). Dasu and Loh BIB003 propose a statistical distortion method to evaluate the quality of cleaning methods. The proposed method directly observes the numerical distribution in the data set and evaluates the quality according to the variation of the distribution caused by different cleaning methods.