Stream data are often dirty, for example, owing to unreliable sensor reading or erroneous extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter, which may seriously alter the data without preserving the original information. We argue that the cleaning should avoid changing those originally correct/clean data, a.k.a. the minimum modification rule in data cleaning. To capture the knowledge about what is clean , we consider the (widely existing) constraints on the speed and acceleration of data changes, such as fuel consumption per hour, daily limit of stock prices, or the top speed and acceleration of a car. Guided by these semantic constraints, in this article, we propose the constraint-based approach for cleaning stream data. It is notable that existing data repair techniques clean (a sequence of) data as a whole and fail to support stream computation. To this end, we have to relax the global optimum over the entire sequence to the local optimum in a window. Rather than the commonly observed NP-hardness of general data repairing problems, our major contributions include (1) polynomial time algorithm for global optimum, (2) linear time algorithm towards local optimum under an efficient median-based solution , and (3) experiments on real datasets demonstrate that our method can show significantly lower L1 error than the existing approaches such as smoother.
Incomplete information often occur along with many database applications, e.g., in data integration, data cleaning or data exchange. The idea of data imputation is to fill the missing data with the values of its neighbors who share the same information. Such neighbors could either be identified certainly by editing rules or statistically by relational dependency networks. Unfortunately, owing to data sparsity, the number of neighbors (identified w.r.t. value equality) is rather limited, especially in the presence of data values with variances. In this paper, we argue to extensively enrich similarity neighbors by similarity rules with tolerance to small variations. More fillings can thus be acquired that the aforesaid equality neighbors fail to reveal. To fill the missing values more, we study the problem of maximizing the missing data imputation. Our major contributions include (1) the np-hardness analysis on solving and approximating the problem, (2) exact algorithms for tackling the problem, and (3) efficient approximation with performance guarantees. Experiments on real and synthetic data sets demonstrate that the filling accuracy can be improved.
Missing numerical values are prevalent, e.g., owing to unreliable sensor reading, collection and transmission among heterogeneous sources. Unlike categorized data imputation over a limited domain, the numerical values suffer from two issues:(1) sparsity problem, the incomplete tuple may not have sufficient complete neighbors sharing the same/similar values for imputation, owing to the (almost) infinite domain; (2) heterogeneity problem, different tuples may not fit the same (regression) model. In this study, enlightened by the conditional dependencies that hold conditionally over certain tuples rather than the whole relation, we propose to learn a regression model individually for each complete tuple together with its neighbors. Our IIM, Imputation via Individual Models, thus no longer relies on sharing similar values among the k complete neighbors for imputation, but utilizes their regression results by the aforesaid learned individual (not necessary the same) models. Remarkably, we show that some existing methods are indeed special cases of our IIM, under the extreme settings of the number ℓ of learning neighbors considered in individual learning. In this sense, a proper number ℓ of neighbors is essential to learn the individual models (avoid over-fitting or under-fitting). We propose to adaptively learn individual models over various number ℓ of neighbors for different complete tuples. By devising efficient incremental computation, the time complexity of learning a model reduces from linear to constant. Experiments on real data demonstrate that our IIM with adaptive learning achieves higher imputation accuracy than the existing approaches.
Errors are prevalent in time series data, such as GPS trajectories or sensor readings. Existing methods focus more on anomaly detection but not on repairing the detected anomalies. By simply filtering out the dirty data via anomaly detection, applications could still be unreliable over the incomplete time series. Instead of simply discarding anomalies, we propose to (iteratively) repair them in time series data, by creatively bonding the beauty of temporal nature in anomaly detection with the widely considered minimum change principle in data repairing. Our major contributions include: (1) a novel framework of iterative minimum repairing (IMR) over time series data, (2) explicit analysis on convergence of the proposed iterative minimum repairing, and (3) efficient estimation of parameters in each iteration. Remarkably, with incremental computation, we reduce the complexity of parameter estimation from O ( n ) to O (1). Experiments on real datasets demonstrate the superiority of our proposal compared to the state-of-the-art approaches. In particular, we show that (the proposed) repairing indeed improves the time series classification application.
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Due to the development of internet technology and computer science, data is exploding at an exponential rate. Big data brings us new opportunities and challenges. On the one hand, we can analyze and mine big data to discover hidden information and get more potential value. On the other hand, the 5V characteristic of big data, especially Volume which means large amount of data, brings challenges to storage and processing. For some traditional data mining algorithms, machine learning algorithms and data profiling tasks, it is very difficult to handle such a large amount of data. The large amount of data is highly demanding hardware resources and time consuming. Sampling methods can effectively reduce the amount of data and help speed up data processing. Sampling technology has been widely used in big data context. Data profiling is the activity that finds metadata of data set and has many use cases, e.g., performing data profiling tasks on relational data, graph data, and time series data for anomaly detection and data repair. However, data profiling is computationally expensive, especially for large data sets. Hence this article focuses on researching sampling for data profiling tasks in big data context and investigates the application of sampling in different categories of data profiling. From the experimental results of these studies, the results got from the sampled data are close to or even exceed the results of the full amount of data. Therefore, sampling technology plays an important role in the era of big data, and we also have reason to believe that sampling technology will become an indispensable step in big data processing in the future.INDEX TERMS Big data, large amount, sampling, data profiling.
As data volume grows extensively, data profiling helps to extract metadata of large-scale data. However, one kind of metadata, order statistics, is difficult to be computed because they are not mergeable or incremental. Thus, the limitation of time and memory space does not support their computation on large-scale data. In this paper, we focus on an order statistic, quantiles, and present a comprehensive analysis of studies on approximate quantile computation. Both deterministic algorithms and randomized algorithms that compute approximate quantiles over streaming models or distributed models are covered. Then, multiple techniques for improving the efficiency and performance of approximate quantile algorithms in various scenarios, such as skewed data and high-speed data streams, are presented. Finally, we conclude with coverage of existing packages in different languages and with a brief discussion of the future direction in this area. INDEX TERMS Data profiling, order statistics, approximate quantile, streaming model, distributed model.
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