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您的检索式：作者名="Xiaoou Ding"
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| 1 | Efficient Currency Determination Algorithms for Dynamic Data显示文摘Data quality is an important aspect in data application and management, and currency is one of the major dimensions influencing its quality. In real applications, datasets timestamps are often incomplete and unavailable,or even absent. With the increasing requirements to update real-time data, existing methods can fail to adequately determine the currency of entities. In consideration of the velocity of big data, we propose a series of efficient algorithms for determining the currency of dynamic datasets, which we divide into two steps. In the preprocessing step, to better determine data currency and accelerate dataset updating, we propose the use of a topological graph of the processing order of the entity attributes. Then, we construct an Entity Query B-Tree(EQB-Tree) structure and an Entity Storage Dynamic Linked List(ES-DLL) to improve the querying and updating processes of both the data currency graph and currency scores. In the currency determination step, we propose definitions of the currency score and currency information for tuples referring to the same entity and use examples to discuss methods and algorithms for their computation. Based on our experimental results with both real and synthetic data, we verify that our methods can efficiently update data in the correct order of currency. | Xiaoou Ding Hongzhi Wang Yitong Gao Jianzhong Li Hong Gao | 2017 | Tsinghua Science and Technology2017，22，3： | 2 |
| 2 | IoT data cleaning techniques: A survey显示文摘Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data trustworthiness.This paper provides a systematic summary of the two main stages of data cleaning for Internet of Things(IoT)data with time series characteristics,including error data detection and data repairing.In respect to error data detection techniques,it categorizes an overview of quantitative data error detection methods for detecting single-point errors,continuous errors,and multidimensional time series data errors and qualitative data error detection methods for detecting rule-violating errors.Besides,it provides a detailed description of error data repairing techniques,involving statistics-based repairing,rule-based repairing,and human-involved repairing.We review the strengths and the limitations of the current data cleaning techniques under IoT data applications and conclude with an outlook on the future of IoT data cleaning. | Xiaoou Ding Hongzhi Wang Genglong Li Haoxuan Li Yingze Li Yida Liu | 2022 | Intelligent and Converged Networks2022，3，4： | 0 |
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