2013 IEEE International Congress on Big Data最新文献

Towards a Quality-centric Big Data Architecture for Federated Sensor Services 面向联邦传感器服务的以质量为中心的大数据架构

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.21

Lakshmish Ramaswamy, Victor J. Lawson, Siva Venkat Gogineni

{"title":"Towards a Quality-centric Big Data Architecture for Federated Sensor Services","authors":"Lakshmish Ramaswamy, Victor J. Lawson, Siva Venkat Gogineni","doi":"10.1109/BigData.Congress.2013.21","DOIUrl":"https://doi.org/10.1109/BigData.Congress.2013.21","url":null,"abstract":"As the Internet of Things (IoT) paradigm gains popularity, the next few years will likely witness 'servitization' of domain sensing functionalities. We envision a cloud-based eco-system in which high quality data from large numbers of independently-managed sensors is shared or even traded in real-time. Such an eco-system will necessarily have multiple stakeholders such as sensor data providers, domain applications that utilize sensor data (data consumers), and cloud infrastructure providers who may collaborate as well as compete. While there has been considerable research on wireless sensor networks, the challenges involved in building cloud-based platforms for hosting sensor services are largely unexplored. In this paper, we present our vision for data quality (DQ)-centric big data infrastructure for federated sensor service clouds. We first motivate our work by providing real-world examples. We outline the key features that federated sensor service clouds need to possess. This paper proposes a big data architecture in which DQ is pervasive throughout the platform. Our architecture includes a markup language called SDQ-ML for describing sensor services as well as for domain applications to express their sensor feed requirements. The paper explores the advantages and limitations of current big data technologies in building various components of the platform. We also outline our initial ideas towards addressing the limitations.","PeriodicalId":402135,"journal":{"name":"2013 IEEE International Congress on Big Data","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115633321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 62

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.32

Huiqi Hu, Guoliang Li, Jianhua Feng

引用次数: 5

Characterization of 3G Data-Plane Traffic and Application towards Centralized Control and Management for Software Defined Networking 3G数据平面流量特性及其在软件定义网络集中控制和管理中的应用

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.44

Long Qian, Bin Wu, Renbo Zhang, Weiyu Zhang, Min Luo

{"title":"Characterization of 3G Data-Plane Traffic and Application towards Centralized Control and Management for Software Defined Networking","authors":"Long Qian, Bin Wu, Renbo Zhang, Weiyu Zhang, Min Luo","doi":"10.1109/BigData.Congress.2013.44","DOIUrl":"https://doi.org/10.1109/BigData.Congress.2013.44","url":null,"abstract":"With the wide deployment of 3G/4G cellular data networks there is a tremendous growth of mobile Internet access worldwide. We conduct a detailed measurement study about user behaviors, application usage and location patterns of users. We present a methodology that correlates different network attributes and user information together. For user behaviors we classify all users into four groups by time division and investigate the behaviors of different types of users. For example, we find that midnight users consume tremendous network bandwidth. We categorize applications into several groups and provide insights of each type of application usage about up/down flow user numbers and user request times. We also analyze the location information of users that reveal the relationship between network usages with user mobility. Our study successfully provides network operational insights with the details of network usage or the user remarkable behaviors that can help network planners and operators to plan and adaptively adjust network capacity assignment and flow or application management. Most importantly it will help service providers to reduce their operating cost and offer more flexible and variety of services to meet the needs of today's mobile clients without the need of increasing capacity in congested areas. Finally, our analysis will enable much more efficient routing and resource allocation management applications in the recent endeavor for software defined networking that will put such responsibility into the logically centralized controllers.","PeriodicalId":402135,"journal":{"name":"2013 IEEE International Congress on Big Data","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Benchmarking Apache Accumulo BigData Distributed Table Store Using Its Continuous Test Suite 使用其连续测试套件对Apache Accumulo BigData分布式表存储进行基准测试

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.51

R. Sen, Andrew L. Farris, Peter Guerra

引用次数: 31

The Knowledge Service Project in the Era of Big Data 大数据时代的知识服务工程

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.70

Dongfeng Cai, Yu Bai, Guiping Zhang, Fang Cai

引用次数: 5

Countering the Concept-Drift Problem in Big Data Using iOVFDT 利用iOVFDT对抗大数据中的概念漂移问题

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.25

Yang Hang, S. Fong

{"title":"Countering the Concept-Drift Problem in Big Data Using iOVFDT","authors":"Yang Hang, S. Fong","doi":"10.1109/BigData.Congress.2013.25","DOIUrl":"https://doi.org/10.1109/BigData.Congress.2013.25","url":null,"abstract":"How to efficiently uncover the knowledge hidden within massive and big data remains an open problem. One of the challenges is the issue of 'concept drift' in streaming data flows. Concept drift is a well-known problem in data analytics, in which the statistical properties of the attributes and their target classes shift over time, making the trained model less accurate. Many methods have been proposed for data mining in batch mode. Stream mining represents a new generation of data mining techniques, in which the model is updated in one pass whenever new data arrive. This one-pass mechanism is inherently adaptive and hence potentially more robust than its predecessors in handling concept drift in data streams. In this paper, we evaluate the performance of a family of decision-tree-based data stream mining algorithms. The advantage of incremental decision tree learning is the set of rules that can be extracted from the induced model. The extracted rules, in the form of predicate logics, can be used subsequently in many decision-support applications. However, the induced decision tree must be both accurate and compact, even in the presence of concept drift. We compare the performance of three typical incremental decision tree algorithms (VFDT [2], ADWIN [3], iOVFDT [4]) in dealing with concept-drift data. Both synthetic and real-world drift data are used in the experiment. iOVFDT is found to produce superior results.","PeriodicalId":402135,"journal":{"name":"2013 IEEE International Congress on Big Data","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129322948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

SPOAN: Load Balancing Replica Placement Strategy for Large Scale Biometric Identification Service 大规模生物识别服务的负载平衡副本放置策略

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.50

T. Kitano, Leiming Su

{"title":"SPOAN: Load Balancing Replica Placement Strategy for Large Scale Biometric Identification Service","authors":"T. Kitano, Leiming Su","doi":"10.1109/BigData.Congress.2013.50","DOIUrl":"https://doi.org/10.1109/BigData.Congress.2013.50","url":null,"abstract":"Large-scale identification system is a distributed data processing system that requires both high throughput and high reliability. In large-scale identification systems, not only the volume of computing is extremely high but also the I/O volume per CPU second is extremely large. Therefore, how one places the data replicas in the computing nodes can have a significant impact on the performance of identification and on the availability of the biometric data sets. However, the traditional replica placement method used in the distributed processing frameworks and distributed storage technologies are unable to rebalance loads among nodes when the specific nodes are down and the throughput of identification slows. To address this problem, we propose a replica placement strategy, called SPOAN, that enables us to rebalance the loads among computing nodes and maintain data more reliably. We also developed the architecture of the large scale identification systems using SPOAN. As the result of evaluations involving emulation of biometric database containing about 1.2 billion individuals, we found that we could rebalance the loads even when specific computing nodes were slowed down unlike traditional placing systems of replicas and improve throughput by up to 46%.","PeriodicalId":402135,"journal":{"name":"2013 IEEE International Congress on Big Data","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Big Data Infrastructure for Active Situation Awareness on Social Network Services 面向社交网络服务主动态势感知的大数据基础设施

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.61

Incheon Paik, Takazumi Tanaka, Hiroki Ohashi, Wuhui Chen

引用次数: 14

Point and Interval Estimation Method for Auto-regressive Model with Nonnormal Error 非正态误差自回归模型的点和区间估计方法

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.57

B. Lim, Jongwoo Kim, Sung-Shick Kim, Jun-Geol Baek

引用次数: 0

Distributed Stochastic Aware Random Forests -- Efficient Data Mining for Big Data 分布式随机感知随机森林——面向大数据的高效数据挖掘

2013 IEEE International Congress on Big Data Pub Date : 2013-06-27 DOI: 10.1109/BigData.Congress.2013.68

Joaquim Assunção, Paulo Fernandes, Lucelene Lopes, Silvio Normey

引用次数: 14