检索结果-内蒙古大学图书馆

data summarization ontology-based query processing

EXPERT SYSTEMS WITH APPLICATIONS 2013年第6期40卷 2109-2116页

作者： Wang, Hai Wang, Shouhong St Marys Univ Sobey Sch Business Halifax NS B3H 2W3 Canada Univ Massachusetts Charlton Coll Business Dartmouth MA 02747 USA

data summarization has recently received considerable attention in the knowledge systems community. This paper discusses the design of data summarization query system. Based on an initial analysis of requirement representations in data summarization, the study develops a generic organization of ontology for data summarization query system. Furthermore, this paper proposes a framework of ontology-based query language of data summarization based on the proposed ontology structure. A prototype project of data summarization ontology-based Query by Examples (QBE) for summarizing the data incompleteness demonstrates the effectiveness of the proposed framework. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： data summarization Ontology Query Ontology-based query data mining

来源：评论

学校读者我要写书评

暂无评论

data summarization with Social Contexts 16

Data Summarization with Social Contexts

引用

25th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhuang, Hao Rahman, Rameez Hu, Xia Guo, Tian Hui, Pan Aberer, Karl Ecole Polytech Fed Lausanne LSIR Lausanne Switzerland Texas A&M Univ Comp Sci & Engn College Stn TX USA Hong Kong Univ Sci & Technol SyMLab Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781450340731

While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summarization which transforms the original dataset into a smaller, yet still useful, subset. Existing methods find such subsets with objective functions based on data properties such as representativeness or informativeness but do not exploit social contexts, which are distinct characteristics of social data. Further, till date very little work has focused on topic preserving data summarization, despite the abundant work on topic modeling. This is a challenging task for two reasons. First, since topic model is based on latent variables, existing methods are not well-suited to capture latent topics. Second, it is difficult to find such social contexts that provide valuable information for building effective topic-preserving summarization model. To tackle these challenges, in this paper, we focus on exploiting social contexts to summarize social data while preserving topics in the original dataset. We take Twitter data as a case study. Through analyzing Twitter data, we discover two social contexts which are important for topic generation and dissemination, namely (i) CrowdExp topic score that captures the influence of both the crowd and the expert users in Twitter and (ii) Retweet topic score that captures the influence of Twitter users' actions. We conduct extensive experiments on two real-world Twitter datasets using two applications. The experimental results show that, by leveraging social contexts, our proposed solution can enhance topic-preserving data summarization and improve application performance by up to 18%.

关键词： data summarization Social Context Submodular Optimization Topic Model

来源：评论

学校读者我要写书评

暂无评论

data summarization and Distributed Computation 18

Data Summarization and Distributed Computation

引用

37th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC)

作者： Cormode, Graham Univ Warwick Comp Sci Data Management Privacy & Big Data Anal Warwick England

ISBN: (纸本)9781450357951

The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function. In this talk, I'll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.

关键词： data summarization distributed computation

来源：评论

学校读者我要写书评

暂无评论

data summarization for Federated Learning 1

引用

6th International Conference on Machine Learning for Networking (MLN)

作者： Devillers, Julianna Brun, Olivier Prabhu, Balakrishna J. ISAE SUPAERO Toulouse France Univ Toulouse CNRS LAAS CNRS Toulouse France

ISBN: (数字)9783031599330

ISBN: (纸本)9783031599323;9783031599330

We explore data summarization techniques as a mean to reduce the energy footprint of Federated Learning (FL). We formulate the problem of selecting a small subset of data points that best represent the gradient of each local dataset as a submodular maximization problem and provide sufficient conditions under which the FL training is guaranteed to converge to the same global model as if the whole local datasets have been used on each client. Experimental results on IID and non-IID datasets show that this approach yields a similar accuracy as training on the full local datasets, but with a significant reduction of runtimes. There is however no clear advantage of data summarization over random sampling.

关键词： data summarization FedAvg convergence

来源：评论

学校读者我要写书评

暂无评论

FEATURE TRANSFORMATION: A GENETIC-BASED FEATURE CONSTRUCTION METHOD FOR data summarization

引用

COMPUTATIONAL INTELLIGENCE 2010年第3期26卷 337-357页

作者： Alfred, Rayner Univ Malaysia Sabah Sch Engn & Informat Technol Kota Kinabalu 88999 Sabah Malaysia

The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic-based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the descriptive accuracy of the DARA algorithm. The DARA algorithm is designed to summarize data stored in the nontarget tables by clustering them into groups, where multiple records stored in nontarget tables correspond to a single record stored in a target table. This article addresses the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. This involves solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. This work also evaluates several scoring measures used as fitness functions to find the best set of constructed features.

关键词： feature construction feature transformation data summarization genetic algorithm clustering

来源：评论

学校读者我要写书评

暂无评论

Fast fuzzy trajectory clustering strategy based on data summarization and rough approximation

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2016年第3期19卷 1411-1420页

作者： Hu, Chunchun Luo, Nianxue Zhao, Qiansheng Wuhan Univ Sch Geodesy & Geomat 129 Luoyu Rd Wuhan 430079 Peoples R China

More technologies available for collecting large dataset of trajectory of moving objects make it more essential to perform clustering over these trajectory data. Trajectory clustering presents more complication than traditional approaches due to the nature of trajectories which is temporal, massive and related to the location. Meanwhile, the uncertainty in trajectory clustering also appears when we determine which cluster each trajectory belongs to. However, the computing performance of many clustering algorithms sharply declines as data size increases. In this paper, we study the fuzzy clustering approach for extracting potential spatial patterns by introducing rough set and fuzzy set theory. First, we propose the fast similarity measure method by employing the rough approximation distances between trajectories. Especially for the long trajectory sequences, the computing time would be reduced greatly. We also introduce a summarization technique to reduce the number of distance computations required in similarity measure. Second, an appropriate function of the membership degree is redefined for clustering quality and performance purpose. Third, we modify the fuzzy C-means algorithm by embodying a new similarity measure and the membership degree function. The experimental results conducted on two real datasets of trajectories show the effectiveness of our methods by evaluating clustering validity and computing performance for large datasets. The computing performance of the proposed fuzzy clustering is obviously improved as the dataset size of trajectories increases.

关键词： Fuzzy clustering Trajectory dataset Rough set data summarization The membership degree

来源：评论

学校读者我要写书评

暂无评论

Robust and efficient rule extraction through data summarization and its application in welding fault diagnosis

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2008年第3期4卷 198-206页

作者： Gong, Rongsheng Huang, Samuel H. Chen, Tien-Ting Univ Cincinnati Intelligent Syst Lab Dept Mech Ind & Nucl Engn Cincinnati OH 45221 USA Zhejiang Univ Technol Coll Software Engn Hangzhou 310014 Zhejiang Peoples R China

This paper presents a robust and efficient method to discover knowledge for classification problems through data summarization. It discretizes continuous features and then summarizes the data using a contingency table. Inconsistency rate for different subsets of features can then be easily calculated from the contingency table. Sequential search is then used to find the best feature subset. After the number of features is reduced to a certain extent, easy-to-understand knowledge can be intuitively derived from data summary. Another desirable feature of the proposed method is its capability to learn incrementally;namely, knowledge can be updated quickly whenever new data are obtained. Moreover, the proposed method is capable of handling missing values when used for prediction. The method is applied on two benchmark data sets showing its effectiveness on selecting discriminative features. The practical usefulness of this method in manufacturing is demonstrated through an application on welding fault diagnosis.

关键词： data summarization fault diagnosis feature selection incremental learning knowledge discovery rule extraction welding

来源：评论

学校读者我要写书评

暂无评论

Intelligent Big data summarization for Rare Anomaly Detection

引用

IEEE ACCESS 2019年 7卷 68669-68677页

作者： Ahmed, Mohiuddin Edith Cowan Univ Sch Sci Acad Ctr Cyber Secur Excellence Joondalup WA 6027 Australia

Identifying interesting patterns from a huge amount of data is a challenging task across a wide range of application domain Especially, for cyber security being able to identify rare types of network activities or anomalies from network traffic data (a.k.a. Big data!) is an important but time-consuming data analysis task having moderate computing resources. Existing research has shown that it is possible to detect rare anomalies from the summarized version of big data. Therefore, summarization is an effective preprocessing function before applying anomaly detection techniques. This aim of this paper is to improve and quantify the scalability and accuracy of the anomaly detection techniques by using summarization. Hence, we propose a sampling-based summarization technique (SUCh: summarization Using Chernoff-Bound) which is computationally effective than the existing techniques and also performs better in identifying rare anomalies from twelve benchmark network traffic datasets. The experimental results show that, instead of using original dataset, a summary of the data yields better performance in terms of true positive and false positive rates, when used for anomaly detection with less time required.

关键词： Anomaly detection data summarization sampling clustering SCADA network traffic

来源：评论

学校读者我要写书评

暂无评论

Towards reducing false alarms in network intrusion detection systems with data summarization technique

引用

SECURITY AND COMMUNICATION NETWORKS 2013年第3期6卷 275-285页

作者： Hubballi, Neminath Biswas, Santosh Nandi, Sukumar Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati Assam India

Anomaly based intrusion detection systems (IDSs) create a benign behavior profile of the network, and any deviation from this profile is considered as an attack. Many of the algorithms proposed in the literature for anomaly IDS fall into cluster analysis category. As networks become faster in operation, the amount of data that needs to be analyzed becomes huge. Many clustering techniques require more than one pass on the dataset;thus, when used as anomaly IDSs, these algorithms becomes computationally expensive and cannot work for such high-speed networks. To handle voluminous data, anomaly IDS schemes have been proposed that use data summarization techniques. data summarization techniques found in the literature suffer from false alarms due to improper clustering when used as anomaly IDS. In this paper, an anomaly IDS is proposed that is capable of handling large dataset yet minimizing false alarms. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： false alarm minimization intrusion detection data summarization clustering anomaly detection

来源：评论

学校读者我要写书评

暂无评论

ONTOLOGY-BASED data summarization ENGINE: A DESIGN METHODOLOGY

引用

JOURNAL OF COMPUTER INFORMATION SYSTEMS 2012年第1期53卷 48-56页

作者： Wang, Hai Wang, Shouhong St Marys Univ Halifax NS B3H 2W3 Canada Univ Massachusetts Dartmouth Dartmouth MA 02747 USA

data summarization has recently received considerable attention in the information technology community. This design research paper discusses the methodology and principles of the design of data summarization engine. Based on an initial analysis of requirement representations in data summarization, it proposes a generic organization of ontology for data summarization engine. Furthermore, this paper proposes a design framework for data summarization engine that uses the ontology as the fundamental structure of the interactive user-computer interface. A prototype project of ontology-based data summarization engine for summarizing the data incompleteness demonstrates the effectiveness of the design of ontology-based data summarization engine.

关键词： Design research ontology data summarization data summarization engine database systems analysis and design business intelligence data mining incomplete data

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：