检索结果-内蒙古大学图书馆

IEEE International Conference on Big data and Smart Computing (BigComp)

作者： Duong Thi Thu Van Dolgorsuren, Batjargal Lee, Young-Koo Kyung Hee Univ Dept Comp Sci Suwon South Korea Mongolian Univ Sci & Technol Dept Informat Technol Ulan Bator Mongolia

ISBN: (纸本)9781665421973

Nowadays, graph data can be collected via more than one resource and in parallel. This involves not only changing the information but also immediately updating the outcomes of the data mining task. In this case, it is undesirable to recompute all the tasks once the changes occur to the original data. Therefore, we propose a streaming graph table (SGTable) to capture inserted/removed edges and an incremental graph matching algorithm to retrieve new results on only new data. Besides, we do the multi-streaming environment experiments using Spark that gather graphs from various sources.

关键词： subgraph/pattern matching streaming data processing multiple data resources

来源：评论

学校读者我要写书评

暂无评论

On the design of a framework integrating an optimization engine with streaming technologies

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 538-550页

作者： Barba-Gonzalez, Cristobal Nebro, Antonio J. Benitez-Hidalgo, Antonio Garcia-Nieto, Jose Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Computat Ada Byron Res Bldg E-29071 Malaga Spain

A number of streaming technologies have appeared in the last years as a result of the rising of Big data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Dynamic multi-objective optimization streaming data processing Big data Software framework

来源：评论

学校读者我要写书评

暂无评论

Applying spark based machine learning model on streaming big data for health status prediction

引用

COMPUTERS & ELECTRICAL ENGINEERING 2018年 65卷 393-399页

作者： Nair, Lekha R. Shetty, Sujala D. Shetty, Siddhanth D. BITS Pilani Dept Comp Sci Dubai CampusPOB 345055 Dubai U Arab Emirates

Machine learning is one of the driving forces of science and commerce, but the proliferation of Big data demands paradigm shifts from traditional methods in the application of machine learning techniques on this voluminous data having varying velocity. With the availability of large health care datasets and progressions in machine learning techniques, computers are now well equipped in diagnosing many health issues. This work aims at developing a real time remote health status prediction system built around open source Big data processing engine, the Apache Spark, deployed in the cloud which focus on applying machine learning model on streaming Big data. In this scalable system, the user tweets his health attributes and the application receives the same in real time, extracts the attributes and applies machine learning model to predict user's health status which is then directly messaged to him/her instantly for taking appropriate action. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Big data machine learning streaming data processing Tweet processing Apache spark Health informatics

来源：评论

学校读者我要写书评

暂无评论

Multistage identification method for real-time abnormal events of streaming data

引用

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第12期15卷

作者： Luo, Hao Sun, Kexin Wang, Junlu Liu, Chengfeng Ding, Linlin Song, Baoyan Liaoning Univ Sch Informat Shenyang 110036 Liaoning Peoples R China

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events ("the coal mine disaster events"). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms

关键词： streaming data processing micro-seismic signals real-time event monitoring and query data drift dynamic time warping anti-aliasing model multistage identification

来源：评论

学校读者我要写书评

暂无评论

Self-boosting first-order autonomous learning neuro-fuzzy systems

引用

APPLIED SOFT COMPUTING 2019年 77卷 118-134页

作者： Gu, Xiaowei Angelov, Plamen Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England Univ Lancaster Lancaster Intelligent Robot & Autonomous Syst Ctr Lancaster England Tech Univ Sofia 1000 Bulgaria

In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure-and parameter-optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and realworld problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Neuro-fuzzy systems Autonomous learning Local optimality Self-boosting streaming data processing

来源：评论

学校读者我要写书评

暂无评论

Big data streaming Analytics for QoE Monitoring in Mobile Networks: A Practical Approach

Big Data Streaming Analytics for QoE Monitoring in Mobile Ne...

引用

IEEE International Conference on Big data (Big data)

作者： Rueda, Diego F. Vergara, Dahyr Reniz, David Everis NTT Data Co Telecom & Business Analyt Bogota Colombia

ISBN: (纸本)9781538650356

Traditionally, Mobile Network Operators (MNOs) use a set of Key Performance Indicators (KPIs) to measure the quality offered to their customers. However, these KPIs do not reflect the quality perceived by the customers because they are high-level and network-based metrics. Instead, Quality of Experience (QoE) monitoring of the most common mobile applications can help MNOs to determine when and where customer experience is degraded. In this paper, a customized tool based on Big data streaming is proposed to solve the needs of customer experience monitoring in a real-life MNO and to overcome the challenges of processing a large amount of data collected in 3G and 4G mobile networks. Moreover, real-life case studies of value creation through Big data Analytics for telecommunication industry are also defined. Results show that the streaming data processing enables new opportunities for the MNO to take actions focused on customer experience improvement in near real-time.

关键词： Big data Analytics customer experience management mobile networks quality of experience streaming data processing

来源：评论

学校读者我要写书评

暂无评论

Monitoring of Road Transport Infrastructure for the Intelligent Environment "Smart Road" 2nd

Monitoring of Road Transport Infrastructure for the Intellig...

引用

2nd Conference on Creativity in Intelligent Technologies and data Science (CIT and DS)

作者： Finogeev, Alexey Finogeev, Anton Shevchenko, Sergey Penza State Univ Penza Russia Natl Tech Univ Kharkiv Polytech Inst Kharkov Ukraine

ISBN: (纸本)9783319655512;9783319655505

In the article, the issues of monitoring the road transport infrastructure based on a distributed network of photoradar complexes for fixing traffic accidents have been considered. This paper discusses tools for collection of road accident's photo and video data fixation, data mining and forecasting of transport incidents, depending on various factors (meteorological, social, operational, etc.). The connection between the complexes and the data processing center is established to ensure secure data transmission using a heterogeneous transport network. In the process of monitoring, tasks of spatial and intellectual analysis, evaluation, and forecasting of traffic accidents and transport situations for preventive response, notification and warning road users are solved. The monitoring system is an integral part of Smart Road intellectual environment within the framework of the Smart & Safe City concept. A multi-agent model of convergent data processing is implemented for big sensor data, which integrates cloud, fog, and mobile computing technologies.

关键词： Monitoring data mining Decision support streaming data processing Deep machine learning Wireless sensor networks Big sensor data Multiagent approach Smart Road Smart and safe city Intelligent Transportation System Convergent data processing

来源：评论

学校读者我要写书评

暂无评论

Model-based Scheduling for Stream processing Systems 19

Model-based Scheduling for Stream Processing Systems

引用

19th IEEE International Conference on High Performance Computing and Communications (HPCC) / 15th IEEE International Conference on Smart City (SmartCity) / 3rd IEEE International Conference on data Science and Systems (DSS)

作者： Wang, Yidan Tari, Zahir HoseinyFarahabady, M. Reza Zomaya, Albert Y. RMIT Univ Sch Sci Melbourne Vic Australia Univ Sydney Sch IT Ctr Distributed & High Performance Comp Sydney NSW Australia

ISBN: (纸本)9781538625880

Stream processing is emerging to react to the changing business situations of real-time processing. The main aim of this paradigm is to deal with the huge volume of data in the format of information flows originating from distributed devices. This consequently poses challenges to the scheduling problem in cloud data centers regarding the time-varying velocity of data ingesting and processing. In response to the uncertainties and complexities of streaming data, we propose a model-based scheduling scheme for stream processing systems, capturing the system behavior and providing an optimal allocation strategy to adapt to the changing work conditions. The proposed scheduling policy is implemented in Apache Storm, and micro-benchmarks with various shapes (e.g line, star, and diamond) were used in the evaluation. A topology that tracks trending topics on Twitter is also used, where the input is feeding with tweets in realtime. Experimental results show that the proposed solution can perform estimations that are well aligned with the system performance. The proposed scheduling policy achieves an improved performance with regards throughput and latency under varying ingesting rates.

关键词： streaming data processing Resource Allocation/Scheduling Apache Storm

来源：评论

学校读者我要写书评

暂无评论

Entity Resolution Approach of data Stream Management Systems

引用

WIRELESS PERSONAL COMMUNICATIONS 2016年第4期91卷 1621-1634页

作者： Kim, Taehong Hwang, Mi-Nyeong Kim, Young-Min Jeong, Do-Heon KISTI Disaster Management HPC Technol Res Ctr 245 Daehak Ro Daejeon South Korea

Owing to the technological advancements in Semantic Web and sensor networks, a large amount of data has been produced in association with the open data policy. However, data stream management systems that process stream data have focused on the processing of a large amount of data with little priority on data identification, integration, and external linkage. Furthermore, entity resolution is focused mainly on static database-based technologies. In this study, a real-time stream data processing architecture that can perform the integration and entity resolution of streaming-type heterogeneous input data and interlink with external data is designed. To achieve this goal, a light adapter to integrate heterogeneous data into standard scheme and blocking technique to reduce comparison candidates are applied. The implemented data adapters shows 4 times higher throughput than open source data parsers and the entity resolution results with streaming data shows similar performance with the static data sets. The proposed streaming data entity resolution architecture is expected to form the basis of data integration research that can integrate various information sources of data efficiently, enrich internal data.

关键词： IoT streaming data processing Dynamic entity resolution streaming linked data Entity resolution Blocking entity data stream management system

来源：评论

学校读者我要写书评

暂无评论

Optique: Zooming in on Big data

引用

COMPUTER 2015年第3期48卷 60-67页

作者： Giese, Martin Soylu, Ahmet Vega-Gorgojo, Guillermo Waaler, Arild Haase, Peter Jimenez-Ruiz, Ernesto Lanti, Davide Rezk, Martin Xiao, Guohui Oezcep, Oezguer Rosati, Riccardo Univ Oslo Dept Informat N-0316 Oslo Norway Univ Oxford Dept Comp Sci Oxford OX1 2JD England Free Univ Bozen Bolzano Fac Comp Sci Bolzano Italy Univ Lubeck Inst Informat Syst Lubeck Germany Univ Roma La Sapienza Dept Comp Control & Management Engn Rome Italy

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a useroriented query interface, semi-automated managing me... 详细信息

关键词： Big data ontologies (artificial intelligence) query processing user interfaces Optique big data installation overhead ontology-based data access systems query rewriting techniques semiautomated managing methods streaming data processing temporal data processing user-oriented query interface Big data database languages database management Ontologies Visualization Optique big data distributed databases graphical user interfaces ontology

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：