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Lecture Notes in Computer Science

作者： Liao, Guoqiong Wu, Linqing Wan, Changxuan Xiong, Naixue School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China Department of Computer Science Georgia State University United States

ISBN: (纸本)9783642236402

In recent years, large amounts of uncertain data are emerged with the widespread employment of the new technologies, such as wireless sensor networks, RFID and privacy protection. According to the features of the uncertain data streams such as incomplete, full of noisy, non-uniform and mutable, this paper presents a probability frequent pattern tree called PFP-tree and a method called PFP-growth, to mine probability frequent patterns based on probability damped windows. The main characteristics of the suggested method include: (1) adopting time-based probability damped window model to enhance the accuracy of mined frequent patterns;(2) setting an item index table and a transaction index table to speed up retrieval on the PFP-tree;and (3) pruning the tree to remove the items that cannot become frequent patterns;. The experimental results demonstrate that PFP-growth method has better performance than the main existing schemes in terms of accuracy, processing time and storage space. © 2011 Springer-Verlag.

关键词： Probability

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Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Yuxin Zhang, Lizhong Zhu, Tao Luo, Wenjian School of Computer Science and Information Engineering Hefei University of Technology Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Anhui Hefei230027 China School of Computer Science and Information Engineering University of South China Hunan Hengyang421200 China School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518000 China

ISBN: (纸本)9798350337020

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to 'track the moving optima', rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://***/DMiC-Lab-HFUT/SMDE-Transfer. © 2023 IEEE.

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data science:Trends, perspectives, and prospects

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data Science and Informetrics 2022年第3期2卷 1-21页

作者： Chaolemen Borjigin Chen Zhang Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina Information Resource Management School Renmin University of ChinaBeijingChina

data science is a rapidly growing academic field with significant implications for all conventional scientific studies. However, most relevant studies have been limited to one or several facets of data science from a specific application domain perspective and less to discuss its theoretical framework. data science is unique in that its research goals, perspectives, and body of knowledge are distinct from other sciences. The core theories of data science are the DIKW pyramid, data-intensive scientific discovery, data science life cycle, data wrangling or munging,big data analytics, data management, and governance, data products Dev Ops, and big data visualization. Six main trends characterize the recent theoretical studies on data science are:(1)the growing significance of data Ops,(2) the rise of citizen data scientists,(3) enabling augmented data science,(4) integrating data warehouse with data lake,(5) diversity of domain-specific data science, and(6) implementing data stories as data products. Further development of data science should prioritize four ways to turn challenges into opportunities:(1) accelerating theoretical studies of data science,(2) the trade-off between explainability and performance,(3) achieving data ethics, privacy and trust, and(4) aligning academic curricula with industrial needs.

关键词： CCS concepts General and reference Surveys and overviews data science Big data data products data-driven management The DIKW pyramid

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Theoretical data Science: bridging the gap between domain-general and domain-specific studies

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data Science and Informetrics 2021年第1期1卷 1-28页

作者： Chaolemen Borjigin Chen Zhang Zhizong Sun Ni Yi Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina School of Information Resource Management Renmin University of ChinaBeijingChina

The entering into big data era gives rise to a novel discipline called data *** Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent *** domain-dependent studies and domain-independent ones are evolving into Domain-general data Science and Domain-specific data ***-general data Science emphasizes data Science in a general sense,involving concepts,theories,methods,technologies,and ***-specific data Science is a variant of Domain-general data Science and varies from one domain to *** most popular Domain-specific data Science includes data journalism,Industrial data Science,Business data Science,Health data Science,Biological data Science,Social data Science,and Agile data *** difference between Domain-general data Science and Domain-specific data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered *** a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ***,DSDS and DGDS possess complementary *** data Science(TDS)is a new branch of data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data *** will bridge the gap between DGDS and *** contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of data Science *** main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual ***,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data *** studies of TDS should be focused

关键词： data Science Big data Theoretical data Science Domain-general data Science Domain-specific data Science

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Counterfactual Faithful data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings 3

Counterfactual Faithful Data Generation Based on Disentangle...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Ding, Xu Zhu, Qiang Wang, Song Zhang, Yiqi Wang, Hui Xu, Juan Institute of Industry and Equipment Technology Hefei University of Technology Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China Bengbu Triumph Engineering Technology Co. Ltd Bengbu China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Recently, deep learning and human-out-of-the-loop methods enjoy their prosperous applications in mechanical fault diagnosis. Nonetheless, the None-IID(independent and identically distributed) issue radicated in acquired data severely limits the stability and accuracy of compound fault diagnosis of rolling bearings. This paper proposes a sample augmentation method for generating simulated signals based on the concept of counterfactuals. Firstly, disentangled representations and counterfactual faithful theory are applied to classify the original signal into two categories of properties. One is the fault semantics encoded from the original vibration signal. And the other is the sample attribute encoded by the encoder of Variational Autoencoders (VAEs). Secondly, the counterfactual faithful pseudo-samples are conjured through the Generative Adversarial Network(GAN) using the seeds of the 'factual' sample attributes and 'counterfactual' fault semantics to compensate for the drawback of distribution shift. Finally, the original samples and pseudo-samples are used as the CNN classifier dataset to realize bearing fault diagnosis. Experiments show that this method can generate counterfactual signals that are highly consistent with the original data distribution and can achieve better classification accuracy after balancing the dataset. © 2022 IEEE.

关键词： counterfactual faithfulness fault diagnosis rolling bearing structural causal model VAEGAN

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Large-scale data archiving: At the interface of archive science and computer science

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data Science and Informetrics 2023年第3期3卷 1-17页

作者： Chaolemen Borjigin Qingwen Jin Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina School of Information Resource Management Renmin University of ChinaBeijingChina

Both computer science and archival science are concerned with archiving large-scale data,but they have different ***-scale data archiving in computer science focuses on technical aspects that can reduce the cost of data storage and improve the reliability and efficiency of Big data *** weaknesses lie in inadequate and non-standardized *** in archival science focuses on the management aspects and neglects the necessary technical considerations,resulting in high storage and retention costs and poor ability to manage Big ***,the integration of large-scale data archiving and archival theory can balance the existing research limitations of the two fields and propose two research topics for related research-archival management of Big data and large-scale management of archived Big data.

关键词： data archiving Archive Science Computer Science Large-scale data data storage

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K-nearest neighbors in uncertain graph

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2011年第10期48卷 1850-1858页

作者： Zhang, Yinglong Li, Cuiping Chen, Hong Du, Lingxia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China School of Computer and Information Engineer Jiangxi Agriculture University Nanchang 330045 China

In many areas, a lot of data have been modeled by graphs which are subject to uncertainties, such as molecular compounds and protein interaction networks. While many real applications, for example, collaborative filtering, fraud detection, and link prediction in social networks etc, rely on efficiently answering k-nearest neighbor queries (kNN), which is the problem of computing the most "similar" k nodes to a given query node. To solve the problem, in this paper a novel method based on measurement of SimRank is proposed. However, because graphs evolve over time and are uncertainly, the computing cost can be very high in practice to solve the problem using the existing algorithms of SimRank. So the paper presents an optimization algorithm. Introducing path threshold, which is suitable in both determined graph and uncertain graph, the algorithm merely considers the local neighborhood of a given query node instead of whole graph to prune the search space. To further improving efficiency, the algorithm adopts sample technology in uncertain graph. At the same time, theory and experiments interpret and verify that the optimization algorithm is efficient and effective.

关键词： Collaborative filtering

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Design of Wireless data Center Network Structure Based on ExCCC-DCN 6th

Design of Wireless Data Center Network Structure Based on Ex...

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6th International Conference on data Science, ICDS 2019

作者： Jing, Yanhao Han, Zhijie Du, Xiaoyu Zhang, Qingfang School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

A data center is a cluster of servers, which is typically an organic collection of tens of thousands of servers. The sheer number of servers determines how well its performance is related to how it is interconnected. Just like the topology of Ethernet determines its network capacity and communication characteristics, the network structure of the data center has a great impact on its performance and capacity. The research object ExCCC-DCN in this paper has excellent node capacity, and its construction cost and operating energy consumption are relatively low, which is very suitable for deploying large-scale data centers. However, ExCCC-DCN is deployed on a wired link, and a large number of wired links can make maintenance and upgrade of the data center more difficult. Therefore, this paper utilizes the advantages of high transmission rate, strong anti-interference and high security of 60 GHz millimeter wave to wirelessly transform ExCCC-DCN, making it more flexible in the case of maintaining communication efficiency. © Springer Nature Singapore Pte Ltd 2020.

关键词： Millimeter waves

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Multi-scale Graph Convolutional Neural Network for Object Recognition from Point Cloud data 2nd

Multi-scale Graph Convolutional Neural Network for Object Re...

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2nd International Conference on Urban Intelligence and Applications, ICUIA 2020

作者： Lu, Qiang Chen, Chao Teng, Jinfeng Zhang, Chunyuan Huang, Yi Xuan, Shanli Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230009 China

ISBN: (纸本)9789813346000

How to make robots understand the point cloud data which is collected from the 3D sensor and complete the recognition has become a hot research direction in recent years. In this paper, we propose a new approach to improve the critical robotic capability, semantic understanding of the environment (i.e., 3D object recognition). The convolutional neural network (CNN) method has a very good recognition result in the 2D image domain, but it has certain difficulty in applying irregular and unordered 3D point clouds data. The network for point cloud data generally uses the convolution to realize the extraction of point cloud features by finding the neighborhood features on the point set. Due to the different neighborhood scales caused by the irregularity of 3D point cloud data, we propose a CNN structure that combines multi-scale features. By finding multiple neighborhoods of the point set and establishing local graph extraction features, the stable expression of the local neighborhood is obtained. At the same time, the key point calibration method is added, so that the network can dynamically focus on key point features to improve the recognition result. In a series of analytical experiments, we demonstrate competing results that demonstrate the effectiveness of the network structure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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A New Type Wireless data Center of Comb Topology 6th

A New Type Wireless Data Center of Comb Topology

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6th International Conference on data Science, ICDS 2019

作者： Zhang, Qingfang Han, Zhijie Du, Xiaoyu School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

In recent years, group communication has become more and more popular. In the current data center network, multicast plays an extremely important role in group communication. Traditional data center network is designed based on wires. Researchers have proposed Dcell, Bcube and other data center topologies successively. However, in the traditional data center network, the wired topology structure has the disadvantages of high overhead, complex deployment, high maintenance cost, slow heat dissipation and so on. Moreover, the existing wireless data center Flyways structure and Graphite structure have the problems of low connectivity and poor scalability. So this paper proposes and designs a new data center network topology Comb structure (Comb model is a multi-layer cellular structure model, which is a multi-layer structure composed of single-layer honeycomb-like structure interlaced with each other). The structure can effectively improve the connectivity of the data center, shorten the routing path, reduce the probability of node failure in the network, and ensure the smooth network. © Springer Nature Singapore Pte Ltd 2020.

关键词： Topology

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