Data quality plays a key role in big data management today. With the explosive growth of data from a variety of sources, the quality of data is faced with multiple problems. Motivated by this, we study the multiple data cleaning on incompleteness and inconsistency with currency reasoning and determination in this paper. We introduce a 4-step framework, named${\sf Imp3C}$, for errors detection and quality improvement in incomplete and inconsistent data without timestamps. We achieve an integrated currency determining method to compute the currency orders among tuples, according to currency constraints. Thus, the inconsistent data and missing values are repaired effectively considering the temporal impact. For both effectiveness and efficiency consideration, we carry out inconsistency repair ahead of incompleteness repair. A currency-related consistency distance metric is defined to measure the similarity between dirty tuples and clean ones more accurately. In addition, currency orders are treated as an important feature in the missing imputation training process. The solution algorithms are introduced in detail with case studies. A thorough experiment on three real-life datasets verifies our method${\sf Imp3C}$improves the performance of data repairing with multiple quality problems.${\sf Imp3C}$outperforms the existing advanced methods, especially in the datasets with complex currency orders.

With the growth of data from various sources, data quality is faced with multiple problems. In this paper, we study the multiple data cleaning on incompleteness and inconsistency with currency reasoning and determination. We introduce a 4-step method, named Imp3C, for error detection and repair in incomplete and inconsistent data without timestamps. We propose an integrated currency determining approach to compute currency order among tuples, thus, the dirty data can be repaired effectively considering the temporal impact. Experiments on three real-life datasets verify that Imp3C improves data repairing performance with multiple quality problems, especially in datasets with complex currency orders.

The skyline query is important in database community. Recently, owing to the inherent uncertainty of some applications, skyline query on uncertain data has been widelystudied using probabilistic model, e.g. p-skyline. In the scenario where uncertain data is vertically distributed among multiple servers, the main purpose of p-skyline computation is to minimize the retrieved records from servers to the local client due to the dominance factor of expensive network communication. In this paper, we present three communication-efficient p-skyline algorithms ASR, IASR and FSLR on vertically distributed uncertain data. ASR alternates sorted and random accesses to retrieve the records at servers and performs retrieving-boundingchecking iteration until all the objects can be determined whether they are in the p-skyline result or not. The communication of the instances not retrieved can be saved. IASR is an improved version of ASR. By examining the net gain of retrieving-boundingchecking iteration, IASR early terminates the iteration to further reduce the cost of communication. Compared to ASR and IASR, FSLR performs random accesses only on demand. FSLR first conducts sorted accesses to get loose upper bounds of skyline probabilities of the instances. Then, FSLR uses random accesses to complement a part of retrieved instances to get tighter upper and lower bounds of skyline probabilities until the p-skyline result is computed. Our experimental results demonstrate that our algorithms ASR, IASR and FSLR significantly outperform the intuitive method for p-skyline computation on vertically distributed uncertain data.