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IEEE International Congress on big data (bigdata Congress)

作者： Ishizuka, Yuji Chen, Wuhui Paik, Incheon Univ Aizu Tsuruga Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan

ISBN: (纸本)9781509026227

With the explosion of big data, processing and analyzing large numbers of continuous data streams in real-time, such as social media stream, sensor data streams, log streams, stock exchanges streams, etc., has become a crucial requirement for many scientific and industrial applications in recent years. Increased volume of streaming data as well as the demand for more complex real-time analytics require for execution of processing pipelines among heterogeneous event processing engines as a workflow. In this paper, we propose a workflow transformation for cost minimization in real-time big data processing on the heterogeneous systems. We first give the definition of stream-based workflow;and then we define eight different patterns as rules for workflow transformation;next, we give our workflow transformation algorithm based on our designed rules. Finally, our experiment shows that our proposed workflow transformation method can reduce the communication and computation cost effectively.

关键词： big data processing stream data workflow transformation geo-distributed data centers

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Schedule or Wait: Age-Minimization for IoT big data processing in MEC via Online Learning 41

Schedule or Wait: Age-Minimization for IoT Big Data Processi...

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41st IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Xu, Zichuan Ren, Wenhao Liang, Weifa Xu, Wenzheng Xia, Qiufen Zhou, Pan Li, Mingchu Dalian Univ Technol Sch Software Dalian 116621 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Dalian Univ Technol Int Sch Informat Sci & Engn Dalian 116621 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781665458221

The age of data (AoD) is identified as one of the most novel and important metrics to measure the quality of big data analytics for Internet-of-Things (IoT) applications. Meanwhile, mobile edge computing (MEC) is envisioned as an enabling technology to minimize the AoD of IoT applications by processing the data in edge servers close to IoT devices. In this paper, we study the AoD minimization problem for IoT big data processing in MEC networks. We first propose an exact solution for the problem by formulating it as an Integer Linear Program (ILP). We then propose an efficient heuristic for the offline AoD minimization problem. We also devise an approximation algorithm with a provable approximation ratio for a special case of the problem, by leveraging the parametric rounding technique. We thirdly develop an online learning algorithm with a bounded regret for the online AoD minimization problem under dynamic arrivals of IoT requests and uncertain network delay assumptions, by adopting the Multi-Armed Bandit (MAB) technique. We finally evaluate the performance of the proposed algorithms by extensive simulations and implementations in a real test-bed. Results show that the proposed algorithms outperform existing approaches by reducing the AoD around 10%.

关键词： Mobile edge cloud big data processing Age of data Approximation algorithm Online learning

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Security Intelligence Centers for big data processing 5

Security Intelligence Centers for Big Data Processing

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IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Miloslavskaya, Natalia Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Moscow Russia

ISBN: (纸本)9781538632819

Today numerous information security (IS) incidents in organizations' networks have become not only more sophisticated but also damaging. Hence the systems with proper security services in place to mitigate and promptly respond to IS threats by helping organizations better understand their current network situation, as well as to perform routine work in big IS-related data processing in automatic mode are needed as never before. They are known as Security Operations Centers (SOCs) and Security Intelligence Centers (SICs) as their next evolution step. The key features of SICs are summarized. The SIC business logic and data architecture are proposed. These results lead to the main area of further research.

关键词： big data IT big data processing Information Security Security Ingelligence Security Intelligence Center

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An Architecture for big data processing on Intelligent Transportation Systems An application scenario on highway traffic flows 8

An Architecture for Big Data Processing on Intelligent Trans...

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8th IEEE International Conference on Intelligent Systems (IS)

作者： Guerreiro, Guilherme Figueiras, Paulo Silva, Ricardo Costa, Ruben Jardim-Goncalves, Ricardo Univ Nova Lisboa Fac Ciencias & Tecnol UNINOVA CTSDept Engn Electrotecn P-2829516 Caparica Portugal

ISBN: (纸本)9781509013531

\The transportation sector, and in particularly intelligent transportation systems, generate large volumes of realtime data that needs to be managed, communicated, interpreted, aggregated, and analyzed. To this end, innovative big data processing and mining as well as optimization techniques, need to be developed and applied in order to support real-time decision-making capabilities. Towards this end, this paper presents an ETL (extract, transform and load) architecture for intelligent transportation systems, addressing an application scenario on dynamic toll charging for highways. The ETL approach presented here, is responsible for preparing the data to be used by traffic prediction services, which will dynamically affect toll prices within different contexts. The proposed architecture relies on the adoption of "big data" technologies, to process and store large volumes of data from heterogeneous sources, provided by different highway operators. The proposed architecture is capable of handling real-time and historical data using big data technologies such as Spark on Hadoop and MongoDB. The DATEX-II data model is adopted, in order to harmonize traffic data provided by the highway operators. The work presented here, is still part of ongoing work currently addressed under the EU H2020 OPTIMUM project. Preliminary results achieved so far do not address the final conclusions of the project, but enabled us to demonstrate considerable gains in performance, when compared to other traditional ETL approaches, and also form the basis for pointing out and discuss future work directions and opportunities in the area of the development of big data processing and mining methods under the ITS domain.

关键词： ITS Tolling Systems big data processing data Mining ETL

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big data processing with Apache Spark in university institutions: spark streaming and machine learning algorithm

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INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING 2019年第1-2期29卷 5-20页

作者： Boachie, Emmanuel Li, Chunlin Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430063 Hubei Peoples R China Kumasi Tech Univ Dept Comp Sci Box 854 Kumasi Ghana

data processing is an effective tool for educational sector, which can improve admission selection procedures and decisions. Most research papers focus on computational and theoretical aspect of education though little effort have been put on technological aspect of applying data mining techniques on students admission process. We therefore design a simple spark streaming framework together with machine learning algorithm to guide admission processing. We implement the spark streaming model and the proposed machine learning algorithm in a selected university using its admissions' data. The focus is on the number of students that can be admitted and those that should be rejected to reduce time and cost. The case study we evaluated show the practical usefulness of Spark streaming and machine learning algorithm for data processing in a real-time to reduce time and cost. The experiment results also confirm meaningful graphical interpretation of data using spark streaming and machine learning algorithm for students' selection for admissions.

关键词： spark streaming big data processing machine learning algorithm

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An exploratory study on big data processing: a case study from a biomedical informatics 3

An exploratory study on Big data processing: a case study fr...

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3rd MEC International Conference on big data and Smart City (ICBDSC)

作者： Nair, Smitha Sunil Kumaran Ganesh, Nandita Middle East Coll Dept Comp Knowledge Oasis Muscat Oman Middle East Coll Qual Assurance Off Knowledge Oasis Muscat Oman

ISBN: (纸本)9781467395847

Recent studies confirm that big data processing will be a leading trend in the next several years in the biomedical field. Technical advancements in the field of medicine and life science demand for high storage and processing power. This paper presents an exploratory study on big data processing from a biomedical informatics perspective. In an attempt to perform this task, two case studies are considered from medical imaging and bioinformatics specifically proteomics aspect. It has been identified that there is scope for the growing medical imaging data and proteomic data be efficiently stored and processed through the use of big data technology. This study will also benefit the researchers working on big data tools in conjunction with data mining tools.

关键词： big data big data processing Bioinformatics Medical imaging Neurodegenerative diseases

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Multiple big data processing Platforms

Multiple Big Data Processing Platforms

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Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Chang, Bao Rong Tsai, Hsiu-Fen Chang, Yi-Sheng Huang, Chien-Feng Natl Univ Kaohsiung Dept Comp Sci & Informat Engn 700 Kaohsiung Univ Rd Kaohsiung 811 Taiwan Shu Te Univ Dept Mkt Management 59 Hun Shang Rd Kaohsiung 824 Taiwan

ISBN: (纸本)9781509057320

The integration of Hive, Impala and Spark SQL platforms has achieved to perform rapid data retrieval using SQL query in big data environment. This paper is to design the optimized platform selection for highly improving the response of data retrieval. It can automatically choose the best-perform platform to best perform SQL commands. In addition, the distributed memory storage systems using Memcached and the distributed file system Hadoop HDFS have implemented the caching so that the fastest data retrieval has done once the repeated SQL command has applied.

关键词： big data processing Multi-Platform data processing System Optimized Platform Selection Distributed Memory Cache and Distributed File System Cache

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Sketching-based High-Performance Biomedical big data processing Accelerator

Sketching-based High-Performance Biomedical Big Data Process...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Kulkarni, Amey Jafari, Ali Sagedy, Chris Mohsenin, Tinoosh Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21228 USA

ISBN: (纸本)9781479953417

Multi-Sensor health monitoring systems are used to predict near future events of our health system. Each sensor generates humongous amount of data per second and needs to be processed in real-time. At the same time health monitoring systems are battery operated, thus they have rigid constraints on power and area of processing platform. Additionally, health monitoring systems should be accurate, thus we adapt machine learning techniques to improve detection accuracy. We propose a programmable big data processing framework to reduce on-chip communications and computations, thus reducing energy of the processing. We integrate a low-overhead sketching framework with a low-power programmable PENC many-core platform. The sketching technique reduces the data communications and computations, additionally processing time is scaled down by parallel processing on the many-core platform. For demonstration we show seizure detection application with 22-channel of electroencephalograph (EEG), each channel generates 256 samples per second requiring total of 88 Kbps data rate. The computations are reduced by 16x while energy consumption of processing is reduced up to 68%. For compression rates of 2-16x, the seizure detection performance for sensitivity and specificity is degraded by 2.07% and 2.97%, respectively for Logistic Regression classifier.

关键词： big data processing Sketching Technique Many-Core Seizure Detection

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A Case Study of ICT Used by big data processing in Education: Discuss on Visualization of RE Research Paper 18

A Case Study of ICT Used by Big Data Processing in Education...

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6th International Conference on Information and Education Technology (ICIET)

作者： Tsujioka, Keiko Osaka Univ Suita Osaka Japan

ISBN: (纸本)9781450353595

The latest technologies and information have been introduced one after another at the present field of education. On the other hand, however, it is questionable, whether those technologies and information are understood conveniently. In the case of research papers, for example, is really it true that the paper has been written well about her/his idea, in which the author would like to express? From those questions, we have carried out a survey of research papers. In this paper, we have an awareness especially concerning with academic writing as our core research question. As one of the problem solving, we have extracted the main problems of writing and found out the solution by referring to visualized RE research paper in the field of mechanical technologies. We consider that the contents of the visualized RE (Rotary Engine) research paper are written clearly and logically about the latest technologies and information.

关键词： big data processing visualization methodology of clear writing paper human information processing cognitive processing conceptual metaphor

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Taxonomy for Unsecure big data processing in Security Operations Centers 4

Taxonomy for Unsecure Big Data Processing in Security Operat...

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IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

作者： Miloslavskaya, Natalia Tolstoy, Alexander Zapechnikov, Sergey Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Moscow Russia

ISBN: (纸本)9781509039463

While the media constantly describes new attacks, the organizations seriously concerned about their business protection need to be prepared for such sophisticated attacks against their IT infrastructures. Hence a properly designed and formalized information security (IS) management system with Security Operations Center (SOC) as its centric part is required as never before. Among the most important documents for SOC there are two policies: IS policy and IS incident management policy. In order to create a truly effective policy it is vital to adequately describe SOC's operational environment from the IS viewpoint. The paper presents the most demand for these purposes classifications (taxonomy) of IS threats, vulnerabilities, attacks and IS incidents as the negative elements that should be avoided.

关键词： big data processing security operations center taxonomy information security information security incidents information security threats vulnerabilities attacks

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