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Approximate Continuous Top-k Query over Sliding Window

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Journal of Computer Science & Technology 2017年第1期32卷 93-109页

作者： Rui Zhu Bin wang Shi-Ying Luo Xiao-Chun Yang guo-ren wang College of Computer Science and Engineering Northeastern University Shenyang 110819 China

Continuous top-k query over sliding window is a fundamental problem in database, which retrieves k objects with the highest scores when the window slides. Existing studies mainly adopt exact algorithms to tackle this type of queries, whose key idea is to maintain a subset of objects in the window, and try to retrieve answers from it. However, all the existing algorithms are sensitive to query parameters and data distribution. In addition, they suffer from expensive overhead for incremental maintenance, and thus cannot satisfy real-time requirement. In this paper, we define a novel query named （ε, δ）-approximate continuous top-κ query, which returns approximate answers for top-κ query. In order to efficiently support this query, we propose an efficient framework, named PABF （Probabilistic Approximate Based Framework）, to support approximate top-κ query over sliding window. We firstly maintain a self-adaptive pruning value, which could filter out newly arrived objects who have a probability less than 1 - 5 of being a query result. For those objects that are not filtered, we combine them together, if the score difference among them is less than a threshold. To efficiently maintain these combined results, the framework PABF also proposes a multi-phase merging algorithm. Theoretical analysis indicates that even in the worst case, we require only logarithmic complexity for maintaining each candidate.

关键词： continuous top-k query approximate sliding window

ON THE PROBLEM OF INSTABILITY IN THE DIMENSIONS OF SPLINE SPACES OVER T-MESHES WITH T-CYCLES

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Journal of Computational Mathematics 2015年第3期33卷 248-262页

作者： Qing-Jie guo ren-Hong wang Chong-Jun Li School of Mathematical Sciences Dalian University of Technology Dalian 116024 China

The T-meshes are local modification of rectangular meshes which allow T-junctions. The splines over T-meshes are involved in many fields, such as finite element methods, CAGD etc. The dimension of a spline space is a basic problem for the theories and applications of splines. However, the problem of determining the dimension of a spline space is difficult since it heavily depends on the geometric properties of the partition. In many cases, the dimension is unstable. In this paper, we study the instability in the dimensions of spline spaces over T-meshes by using the smoothing cofactor-conformality method. The modified dimension formulas of spline spaces over T-meshes with T-cycles are also presented. Moreover, some examples are given to illustrate the instability in the dimensions of the spline spaces over some special meshes.

关键词： Spline space Smoothing cofactor-conformality method Instability in the dimension T-meshes.

SE-Chain:A Scalable Storage and Efficient Retrieval Model for Blockchain

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Journal of Computer Science & Technology 2021年第3期36卷 693-706页

作者： Da-Yu Jia Jun-Chang Xin Zhi-Qiong wang Han Lei guo-ren wang School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Key Laboratory of Big Data Management and Analytics of Liaoning Province Shenyang 110819China College of Medicine and Biological Information Engineering Northeastern UniversityShenyang 110169China Neusoft Institute of Intelligent Healthcare Technology Co.Ltd. Shenyang 110179China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China

Massive data is written to blockchain systems for the destination of keeping safe. However, existing blockchain protocols still demand that each full node has to contain the entire chain. Most nodes quit because they are unable to grow their storage space with the size of data. As the number of nodes decreases, the security of blockchains would significantly reduce. We present SE-Chain, a novel scale-out blockchain model that improves storage scalability under the premise of ensuring safety and achieves efficient retrieval. The SE-Chain consists of three parts:the data layer, the processing layer and the storage layer. In the data layer, each transaction is stored in the AB-M tree (Adaptive Balanced Merkle tree), which adaptively combines the advantages of balanced binary tree (quick retrieval) and Merkle tree (quick verification). In the processing layer, the full nodes store the part of the complete chain selected by the duplicate ratio regulation algorithm. Meanwhile, the node reliability verification method is used for increasing the stability of full nodes and reducing the risk of imperfect data recovering caused by the reduction of duplicate number in the storage layer. The experimental results on real datasets show that the query time of SE-Chain based on the AB-M tree is reduced by 17% when 16 nodes exist. Overall, SE-Chain improves the storage scalability extremely and implements efficient querying of transactions.

关键词： SE-Chain AB-M(adaptive balanced Merkle)tree efficient retrieval scale-out blockchain

Keyword Query over Error-Tolerant Knowledge Bases

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Journal of Computer Science & Technology 2016年第4期31卷 702-719页

作者： Yu-Rong Cheng Ye Yuan Jia-Yu Li Lei Chen guo-ren wang College of Computer Science and Engineering Northeastern University Shenyang 110819 China College of Information Science and Engineering Northeastern University Shenyang 110819 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

With more and more knowledge provided by WWW, querying and mining the knowledge bases have attracted much research attention. Among all the queries over knowledge bases, which are usually modelled as graphs, a keyword query is the most widely used one. Although the problem of keyword query over graphs has been deeply studied for years, knowledge bases, as special error-tolerant graphs, lead to the results of the traditional defined keyword queries out of users＇ satisfaction. Thus, in this paper, we define a new keyword query, called confident r-clique, specific for knowledge bases based on the r-clique definition for keyword query on general graphs, which has been proved to be the best one. However, as we prove in the paper, finding the confident r-cliques is #P-hard. We propose a filtering-and-verification framework to improve the search efficiency. In the filtering phase, we develop the tightest upper bound of the confident r-clique, and design an index together with its search algorithm, which suits the large scale of knowledge bases well. In the verification phase, we develop an efficient sampling method to verify the final answers from the candidates remaining in the filtering phase. Extensive experiments demonstrate that the results derived from our new definition satisfy the users＇ requirement better compared with the traditional r-clique definition, and our algorithms are efficient.

关键词： keyword query error-tolerant knowledge base index

I/O Efficient Early Bursting Cohesive Subgraph Discovery in Massive Temporal Networks

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Journal of Computer Science & Technology 2022年第6期37卷 1337-1355页

作者： Yuan Li Jie Dai Xiao-Lin Fan Yu-Hai Zhao guo-ren wang School of Information Science and Technology North China University of TechnologyBeijing 100144China School of Computer Science and Engineering Northeastern UniversityShenyang 110169China School of Computer Beijing Institute of TechnologyBeijing 100081China

Temporal networks are an effective way to encode temporal information into graph data *** the bursting cohesive subgraph(BCS),which accumulates its cohesiveness at the fastest rate,is an important problem in temporal *** BCS has a large number of applications,such as representing emergency events in social media,traffic congestion in road networks and epidemic outbreak in ***,existing methods demand the BCS lasting for a time interval,which neglects the timeliness of the *** this paper,we design an early bursting cohesive subgraph(EBCS)model based on the k-core to enable identifying the burstiness as soon as *** find the EBCS,we first construct a time weight graph(TWG)to measure the bursting level by integrating the topological and temporal ***,we propose a global search algorithm,called GS-EBCS,which can find the exact EBCS by iteratively removing nodes from the ***,we propose a local search algorithm,named LS-EBCS,to find the EBCS by first expanding from a seed node until obtaining a candidate k-core and then refining the k-core to the result subgraph in an optimal time ***,considering the situation that the massive temporal networks cannot be completely put into the memory,we first design an I/O method to build the TWG and then develop I/O efficient global search and local search algorithms,namely I/O-GS and I/O-LS respectively,to find the EBCS under the semi-external *** experiments,conducted on four real temporal networks,demonstrate the efficiency and effectiveness of our proposed *** example,on the DBLP dataset,I/O-LS and LS-EBCS have comparable running time,while the maximum memory usage of I/O-LS is only 6.5 MB,which is much smaller than that of LS-EBCS taking 308.7 MB.

关键词： early bursting cohesive subgraph(EBCS) I/O efficient algorithm semi-external model temporal network

Classifying Uncertain and Evolving Data Streams with Distributed Extreme Learning Machine

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Journal of Computer Science & Technology 2015年第4期30卷 874-887页

作者：韩东红张昕王国仁 College of Information Science and Engineering Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing ( NEU) Ministry of Education Shenyang 110819 China

Conventional classification algorithms are not well suited for the inherent uncertainty, potential concept drift, volume, and velocity of streaming data. Specialized algorithms are needed to obtain efficient and accurate classifiers for uncertain data streams. In this paper, we first introduce Distributed Extreme Learning Machine （DELM）, an optimization of ELM for large matrix operations over large datasets. We then present Weighted Ensemble Classifier Based on Distributed ELM （WE-DELM）, an online and one-pass algorithm for efficiently classifying uncertain streaming data with concept drift. A probability world model is built to transform uncertain streaming data into certain streaming data. Base classifiers are learned using DELM. The weights of the base classifiers are updated dynamically according to classification results. WE-DELM improves both the efficiency in learning the model and the accuracy in performing classification. Experimental results show that WE-DELM achieves better performance on different evaluation criteria, including efficiency, accuracy, and speedup.

关键词： uncertain data stream classification extreme learning machine distributed computing concept drift

Threshold-Based Shortest Path Query over Large Correlated Uncertain Graphs

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Journal of Computer Science & Technology 2015年第4期30卷 762-780页

作者：成雨蓉袁野陈雷王国仁 College of Information Science and Engineering Northeastern University Shenyang 110819 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

With the popularity of uncertain data, queries over uncertain graphs have become a hot topic in the database community. As one of the important queries, the shortest path query over an uncertain graph has attracted much attention of researchers due to its wide applications. Although there are some e？cient solutions addressing this problem, all existing models ignore an important property existing in uncertain graphs： the correlation among the edges sharing the same vertex. In this paper, we apply Markov network to model the hidden correlation in uncertain graphs and compute the shortest path. Unfortunately, calculating the shortest path and corresponding probability over uncertain graphs modeled by Markov networks is a #P-hard problem. Thus, we propose a filtering-and-verification framework to accelerate the queries. In the filtering phase, we design a probabilistic shortest path index based on vertex cuts and blocks of a graph. We find a series of upper bounds and prune the vertices and edges whose upper bounds of the shortest path probability are lower than the threshold. By carefully picking up the blocks and vertex cuts, the index is optimized to have the maximum pruning capability, so that we can filter a large number of vertices which make no contribution to the final shortest path query results. In the verification phase, we develop an e？cient sampling algorithm to determine the final query answers. Finally, we verify the e？ciency and effectiveness of our solutions with extensive experiments.

关键词： shortest path correlated uncertain graph probabilistic shortest path index

Continuous Outlier Monitoring on Uncertain Data Streams

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Journal of Computer Science & Technology 2014年第3期29卷 436-448页

作者：曹科研王国仁韩东红丁国辉王爱侠石凌旭 College of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing Ministry of EducationNortheastern University College of Computer Shenyang Aerospace University Department of Command Information System Engineering Logistic Engineering University of People’s Liberation Army

Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection （CUOD）, which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach -- Probability Pruning for Continuous Uncertain Outlier Detection （PCUOD） to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.

关键词： outlier detection uncertain data stream data mining parameter variable query

Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

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Computers, Materials & Continua 2025年第1期82卷 427-442页

作者： Jing guo Ziying wang Yajuan guo Haitao Jiang State Grid Jiangsu Electric Power Co. Ltd.Research InstituteState Grid Corporation of ChinaNanjing211100China

The rapid proliferation of electric vehicle(EV)charging infrastructure introduces critical cybersecurity vulnerabilities to power grids *** study presents an innovative anomaly detection framework for EV charging stations,addressing the unique challenges posed by third-party aggregation *** approach integrates node equations-based on the parameter identification with a novel deep learning model,xDeepCIN,to detect abnormal data reporting indicative of aggregation *** employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter *** xDeepCIN model,incorporating a Compressed Interaction Network,has the ability to capture complex feature interactions in sparse,high-dimensional charging *** results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance,with F1-scores increasing by up to 32.3%for specific anomaly types compared to traditional methods,such as wide&deep and DeepFM(Factorization-Machine).Our framework exhibits robust scalability,effectively handling networks ranging from 8 to 85 charging ***,we achieve real-time monitoring capabilities,with parameter identification completing within seconds for networks up to 1000 *** research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats,offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.

关键词： Anomaly detection electric vehicle aggregation attack deep cross-network