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Low-cost Remote Rain and Stream Data Acquisition System for Mapping of Potential Micro-Hydro Sites
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作者 R. C. Pallugna A. B. Cultura +1 位作者 C. M. Gozon N. R. Estoperez 《Energy and Power Engineering》 2013年第4期56-62,共7页
The stream and rain data acquisition system presented in this paper makes the mapping of hydro potentials in the region or of the country economically and practically possible. Moreover, it can also serve as a flood w... The stream and rain data acquisition system presented in this paper makes the mapping of hydro potentials in the region or of the country economically and practically possible. Moreover, it can also serve as a flood warning system. 展开更多
关键词 stream data Acquisition MICRO-HYDRO FLOOD WARNING System
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An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data
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作者 R.Rajakumar S.Sathiya Devi 《China Communications》 SCIE CSCD 2024年第5期249-260,共12页
Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL... Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets. 展开更多
关键词 anomaly detection deep learning hyperparameter optimization OVERSAMPLING SMOTE streaming data
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Improved Data Stream Clustering Method: Incorporating KD-Tree for Typicality and Eccentricity-Based Approach
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作者 Dayu Xu Jiaming Lu +1 位作者 Xuyao Zhang Hongtao Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第2期2557-2573,共17页
Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims... Data stream clustering is integral to contemporary big data applications.However,addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research.This paper aims to elevate the efficiency and precision of data stream clustering,leveraging the TEDA(Typicality and Eccentricity Data Analysis)algorithm as a foundation,we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm.The original TEDA algorithm,grounded in the concept of“Typicality and Eccentricity Data Analytics”,represents an evolving and recursive method that requires no prior knowledge.While the algorithm autonomously creates and merges clusters as new data arrives,its efficiency is significantly hindered by the need to traverse all existing clusters upon the arrival of further data.This work presents the NS-TEDA(Neighbor Search Based Typicality and Eccentricity Data Analysis)algorithm by incorporating a KD-Tree(K-Dimensional Tree)algorithm integrated with the Scapegoat Tree.Upon arrival,this ensures that new data points interact solely with clusters in very close proximity.This significantly enhances algorithm efficiency while preventing a single data point from joining too many clusters and mitigating the merging of clusters with high overlap to some extent.We apply the NS-TEDA algorithm to several well-known datasets,comparing its performance with other data stream clustering algorithms and the original TEDA algorithm.The results demonstrate that the proposed algorithm achieves higher accuracy,and its runtime exhibits almost linear dependence on the volume of data,making it more suitable for large-scale data stream analysis research. 展开更多
关键词 data stream clustering TEDA KD-TREE scapegoat tree
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Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review
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作者 Nur Laila Ab Ghani Izzatdin Abdul Aziz Said Jadid AbdulKadir 《Computers, Materials & Continua》 SCIE EI 2023年第5期4649-4668,共20页
Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approac... Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams. 展开更多
关键词 CLUSTERING subspace clustering projected clustering data stream stream clustering high dimensionality evolving data stream concept drift
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A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification
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作者 R.Rajakumar S.Sathiya Devi 《Intelligent Automation & Soft Computing》 SCIE 2023年第2期2101-2116,共16页
Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approach... Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches. 展开更多
关键词 streaming data classification outlier removal feature selection machine learning metaheuristics
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Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams
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作者 E.Susi A.P.Shanthi 《Computer Systems Science & Engineering》 SCIE EI 2023年第6期3231-3246,共16页
Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.... Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to time.The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time.This work proposes an adap-tive learning algorithm-based framework,Twitter Sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time.The framework also works on static data by converting them to data streams using the Kafka tool.The experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,respectively.Though the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic. 展开更多
关键词 Sentiment drift sentiment classification big data BERT real time data streams TWITTER
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Clustering algorithm for multiple data streams based on spectral component similarity 被引量:1
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作者 邹凌君 陈崚 屠莉 《Journal of Southeast University(English Edition)》 EI CAS 2008年第3期264-266,共3页
A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive (AR... A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive (AR) modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods. 展开更多
关键词 data streams CLUSTERING AR model spectral component
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MRST-- An Efficient Monitoring Technology of Summarization on Stream Data 被引量:1
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作者 樊小泊 解婷婷 +1 位作者 李翠平 陈红 《Journal of Computer Science & Technology》 SCIE EI CSCD 2007年第2期190-196,共7页
Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to pro... Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree (MRST) which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others. 展开更多
关键词 Haar wavelet sliding window stream data
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Data partitioning based on sampling for power load streams
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作者 王永利 徐宏炳 +2 位作者 董逸生 钱江波 刘学军 《Journal of Southeast University(English Edition)》 EI CAS 2005年第3期293-298,共6页
A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,wh... A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry.The first step of this method is to parallel sample the data,which is implemented as an extended reservoir-sampling algorithm.A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively.The second step of this method is to partition the fluxes of data streams averagely,which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases:one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector.The experimental results on actual data prove that the method is efficient,practical and suitable for time-varying data streams processing. 展开更多
关键词 data streams continuous queries parallel processing sampling data partitioning
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Min-wise hash function-based sampling over distributed data streams
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作者 崇志宏 倪巍伟 +2 位作者 徐立臻 吕建华 谢英豪 《Journal of Southeast University(English Edition)》 EI CAS 2009年第4期456-459,共4页
In order to avoid the redundant and inconsistent information in distributed data streams, a sampling method based on min-wise hash functions is designed and the practical semantics of the union of distributed data str... In order to avoid the redundant and inconsistent information in distributed data streams, a sampling method based on min-wise hash functions is designed and the practical semantics of the union of distributed data streams is defined. First, for each family of min-wise hash functions, the data with the minimum hash value are selected as local samples and the biased effect caused by frequent updates in a single data stream is filtered out. Secondly, for the same hash function, the sample with the minimum hash value is selected as the global sample and the local samples are combined at the center node to filter out the biased effect of duplicated updates. Finally, based on the obtained uniform samples, several aggregations on the defined semantics of the union of data streams are precisely estimated. The results of comparison tests on synthetic and real-life data streams demonstrate the effectiveness of this method. 展开更多
关键词 data streams AGGREGATION rain-wise hashing
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Oracle Data Guard与Oracle Streams技术对比 被引量:4
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作者 关锦明 张宗平 李海雁 《现代计算机》 2007年第10期72-74,共3页
Oracle Data Guard和Oracle Streams是提高数据库可用性,构建灾难备份系统以及实现数据库分布的理想的技术解决方案。探讨Oracle Data Guard和Oracle Streams技术的实现原理以及技术特点。
关键词 数据库 数据保护 数据复制 数据同步 data GUARD streamS
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Super point detection based on sampling and data streaming algorithms
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作者 程光 强士卿 《Journal of Southeast University(English Edition)》 EI CAS 2009年第2期224-227,共4页
In order to improve the precision of super point detection and control measurement resource consumption, this paper proposes a super point detection method based on sampling and data streaming algorithms (SDSD), and... In order to improve the precision of super point detection and control measurement resource consumption, this paper proposes a super point detection method based on sampling and data streaming algorithms (SDSD), and proves that only sources or destinations with a lot of flows can be sampled probabilistically using the SDSD algorithm. The SDSD algorithm uses both the IP table and the flow bloom filter (BF) data structures to maintain the IP and flow information. The IP table is used to judge whether an IP address has been recorded. If the IP exists, then all its subsequent flows will be recorded into the flow BF; otherwise, the IP flow is sampled. This paper also analyzes the accuracy and memory requirements of the SDSD algorithm , and tests them using the CERNET trace. The theoretical analysis and experimental tests demonstrate that the most relative errors of the super points estimated by the SDSD algorithm are less than 5%, whereas the results of other algorithms are about 10%. Because of the BF structure, the SDSD algorithm is also better than previous algorithms in terms of memory consumption. 展开更多
关键词 super point flow sampling data streaming
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An online anomaly detection method for stream data using isolation principle and statistic histogram
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作者 Zhiguo Ding Minrui Fei Dajun Du 《International Journal of Modeling, Simulation, and Scientific Computing》 EI 2015年第2期85-106,共22页
Online anomaly detection for stream data has been explored recently,where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation.However,due to the inherent complex... Online anomaly detection for stream data has been explored recently,where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation.However,due to the inherent complex characteristics of stream data,such as quick generation,tremendous volume and dynamic evolution distribution,how to develop an effective online anomaly detection method is a challenge.The main objective of this paper is to propose an adaptive online anomaly detection method for stream data.This is achieved by combining isolation principle with online ensemble learning,which is then optimized by statistic histogram.Three main algorithms are developed,i.e.,online detector building algorithm,anomaly detecting algorithm and adaptive detector updating algorithm.To evaluate our proposed method,four massive datasets from the UCI machine learning repository recorded from real events were adopted.Extensive simulations based on these datasets show that our method is effective and robust against different scenarios. 展开更多
关键词 Online anomaly detection stream data isolation principle ensemble learning statistic histogram
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Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse 被引量:3
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作者 Yihong Yang Sheng Ding +4 位作者 Yuwen Liu Shunmei Meng Xiaoxiao Chi Rui Ma Chao Yan 《Digital Communications and Networks》 SCIE CSCD 2022年第4期498-507,共10页
Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute ... Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making.In the process,anomaly detection for wireless sensor data plays an important role.However,the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness,correlations,and concept drift,which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency.First,the data stream is usually generated quickly,which means that the data stream is infinite and enormous.Hence,any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space.Second,there exist correlations among different data streams,and traditional algorithms hardly consider these correlations.Third,the underlying data generation process or distribution may change over time.Thus,traditional anomaly detection algorithms with no model update will lose their effects.Considering these issues,a novel method(called DLSHiForest)based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection.Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach.Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency. 展开更多
关键词 Anomaly detection data stream DLSHiForest Smart greenhouse Edge computing
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An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data 被引量:1
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作者 Romany F.Mansour Shaha Al-Otaibi +3 位作者 Amal Al-Rasheed Hanan Aljuaid Irina V.Pustokhina Denis A.Pustokhin 《Computers, Materials & Continua》 SCIE EI 2021年第9期2843-2858,共16页
Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directl... Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directly in these big data streams.At the same time,streaming data from several applications results in two major problems such as class imbalance and concept drift.The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming Data.The presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and classification.MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is employed.Besides,Statistical Test of Equal Proportions(STEPD),a CDD technique is also utilized.Finally,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for classification.In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried out.The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam dataset.An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model.The proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively. 展开更多
关键词 streaming data concept drift classification model deep learning class imbalance data
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Dynamically Computing Approximate Frequency Counts in Sliding Window over Data Stream 被引量:1
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作者 NIE Guo-liang LU Zheng-ding 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第1期283-288,共6页
This paper presents two one-pass algorithms for dynamically computing frequency counts in sliding window over a data stream-computing frequency counts exceeding user-specified threshold ε. The first algorithm constru... This paper presents two one-pass algorithms for dynamically computing frequency counts in sliding window over a data stream-computing frequency counts exceeding user-specified threshold ε. The first algorithm constructs subwindows and deletes expired sub-windows periodically in sliding window, and each sub-window maintains a summary data structure. The first algorithm outputs at most 1/ε + 1 elements for frequency queries over the most recent N elements. The second algorithm adapts multiple levels method to deal with data stream. Once the sketch of the most recent N elements has been constructed, the second algorithm can provides the answers to the frequency queries over the most recent n ( n≤N) elements. The second algorithm outputs at most 1/ε + 2 elements. The analytical and experimental results show that our algorithms are accurate and effective. 展开更多
关键词 data stream sliding window approximation algorithms frequency counts
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SCMR:a semantic-based coherence micro-cluster recognition algorithm for hybrid web data stream 被引量:2
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作者 王珉 Wang Yongbin Li Ying 《High Technology Letters》 EI CAS 2016年第2期224-232,共9页
Data aggregation from various web sources is very significant for web data analysis domain. In ad- dition, the recognition of coherence micro cluster is one of the most interesting issues in the field of data aggregat... Data aggregation from various web sources is very significant for web data analysis domain. In ad- dition, the recognition of coherence micro cluster is one of the most interesting issues in the field of data aggregation. Until now, many algorithms have been proposed to work on this issue. However, the deficiency of these solutions is that they cannot recognize the micro-cluster data stream accurately. A semantic-based coherent micro-cluster recognition algorithm for hybrid web data stream is nronosed.Firstly, an objective function is proposed to recognize the coherence micro-cluster and then the coher- ence micro-cluster recognition algorithm for hybrid web data stream based on semantic is raised. Fi- 展开更多
关键词 hybrid web data stream coherence micro-clustering entity unified object coher-ence semantic computing
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THRFuzzy:Tangential holoentropy-enabled rough fuzzy classifier to classification of evolving data streams 被引量:1
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作者 Jagannath E.Nalavade T.Senthil Murugan 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1789-1800,共12页
The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is conside... The rapid developments in the fields of telecommunication, sensor data, financial applications, analyzing of data streams, and so on, increase the rate of data arrival, among which the data mining technique is considered a vital process. The data analysis process consists of different tasks, among which the data stream classification approaches face more challenges than the other commonly used techniques. Even though the classification is a continuous process, it requires a design that can adapt the classification model so as to adjust the concept change or the boundary change between the classes. Hence, we design a novel fuzzy classifier known as THRFuzzy to classify new incoming data streams. Rough set theory along with tangential holoentropy function helps in the designing the dynamic classification model. The classification approach uses kernel fuzzy c-means(FCM) clustering for the generation of the rules and tangential holoentropy function to update the membership function. The performance of the proposed THRFuzzy method is verified using three datasets, namely skin segmentation, localization, and breast cancer datasets, and the evaluated metrics, accuracy and time, comparing its performance with HRFuzzy and adaptive k-NN classifiers. The experimental results conclude that THRFuzzy classifier shows better classification results providing a maximum accuracy consuming a minimal time than the existing classifiers. 展开更多
关键词 data stream classification fuzzy rough set tangential holoentropy concept change
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Data Stream Subspace Clustering for Anomalous Network Packet Detection 被引量:1
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作者 Zachary Miller Wei Hu 《Journal of Information Security》 2012年第3期215-223,共9页
As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer netw... As the Internet offers increased connectivity between human beings, it has fallen prey to malicious users who exploit its resources to gain illegal access to critical information. In an effort to protect computer networks from external attacks, two common types of Intrusion Detection Systems (IDSs) are often deployed. The first type is signature-based IDSs which can detect intrusions efficiently by scanning network packets and comparing them with human-generated signatures describing previously-observed attacks. The second type is anomaly-based IDSs able to detect new attacks through modeling normal network traffic without the need for a human expert. Despite this advantage, anomaly-based IDSs are limited by a high false-alarm rate and difficulty detecting network attacks attempting to blend in with normal traffic. In this study, we propose a StreamPreDeCon anomaly-based IDS. StreamPreDeCon is an extension of the preference subspace clustering algorithm PreDeCon designed to resolve some of the challenges associated with anomalous packet detection. Using network packets extracted from the first week of the DARPA '99 intrusion detection evaluation dataset combined with Generic Http, Shellcode and CLET attacks, our IDS achieved 94.4% sensitivity and 0.726% false positives in a best case scenario. To measure the overall effectiveness of the IDS, the average sensitivity and false positive rates were calculated for both the maximum sensitivity and the minimum false positive rate. With the maximum sensitivity, the IDS had 80% sensitivity and 9% false positives on average. The IDS also averaged 63% sensitivity with a 0.4% false positive rate when the minimal number of false positives is needed. These rates are an improvement on results found in a previous study as the sensitivity rate in general increased while the false positive rate decreased. 展开更多
关键词 ANOMALY DETECTION INTRUSION DETECTION System Network Security PREFERENCE SUBSPACE Clustering stream data Mining
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A graph-based sliding window multi-join over data stream 被引量:1
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作者 ZHANG Liang Byeong-Seob You +2 位作者 GE Jun-wei LIU Zhao-hong Hae-Young Bae 《重庆邮电大学学报(自然科学版)》 2007年第3期362-366,共5页
Join operation is a critical problem when dealing with sliding window over data streams. There have been many optimization strategies for sliding window join in the literature, but a simple heuristic is always used fo... Join operation is a critical problem when dealing with sliding window over data streams. There have been many optimization strategies for sliding window join in the literature, but a simple heuristic is always used for selecting the join sequence of many sliding windows, which is ineffectively. The graph-based approach is proposed to process the problem. The sliding window join model is introduced primarily. In this model vertex represent join operator and edge indicated the join relationship among sliding windows. Vertex weight and edge weight represent the cost of join and the reciprocity of join operators respectively. Then good query plan with minimal cost can be found in the model. Thus a complete join algorithm combining setting up model, finding optimal query plan and executing query plan is shown. Experiments show that the graph-based approach is feasible and can work better in above environment. 展开更多
关键词 数据流 查询优化 图论 可调整窗口
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