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8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Spangenberg, Norman Wilke, Moritz Franczyk, Bogdan Univ Leipzig Informat Syst Inst Grimmaische Str 12 D-04109 Leipzig Germany Wroclaw Univ Econ Ul Komandorska 118-120 PL-53345 Wroclaw Poland

The operating room area is still one of the most expensive sections in the hospital due to the high resource requirements and the diverse uncertainties. However there are few solutions that support monitoring and decision-making in operating room management. But with new data sources and analytical methods of big data research more improvements could be achieved. In this work we utilize surgical phase events recognized in surgical device data to learn prediction models and trigger online predictions for remaining intervention times in operating rooms. To identify the best algorithm for prediction model computation with the existing data, we evaluate a set of regression algorithms. Based on this methods we propose an architecture approach for the integrated processing of real-time data and historic learning data. The evaluation and comparison with related work shows that our prototype is competitive regarding prediction accuracy. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Remaining Intervention Time Prediction big data architecture Predictive Analytics

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A big data architecture for intra-surgical remaining time predictions

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Procedia Computer Science 2017年 113卷 310-317页

作者： Norman Spangenberg Moritz Wilke Bogdan Franczyk University of Leipzig Information Systems Institute Grimmaische Straße 12 04109 Leipzig Germany Wroclaw University of Economics ul. Komandorska 118/120 53-345 Wroclaw Poland

关键词： Remaining Intervention Time Prediction big data architecture Predictive Analytics

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Towards a big data architecture for Facilitating Cyber Threat Intelligence 8

Towards a Big Data Architecture for Facilitating Cyber Threa...

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8th IFIP International Conference on New Technologies, Mobility and Security (NTMS)

作者： Wheelus, Charles Bou-Harb, Elias Zhu, Xingquan Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9781509029143

Internet and organizational network security is still threatened by devastating malicious activities. Given the continuous escalation of such attacks in terms of their frequency, sophistication and stealthiness, it is of paramount importance to generate effective cyber threat intelligence that aim at inferring, attributing, characterizing and mitigating such misdemeanors. Nevertheless, such imperative tasks are partially impeded by the lack of approaches that can produce prompt and accurate actionable intelligence by investigating various network traffic sources. In this paper, we propose and evaluate a big data architecture that is rooted in real-time network traffic processing, distributed messaging and scalable data storage. The key benefit of the proposed architecture is that it automates the aggregation and transformations of heterogeneous network data, allowing for greater focus on cyber threat intelligence analytics, rather than data management, aggregation, reconciliation and formatting. Empirical evaluations investigating the application of machine learning analytics by exploiting the artifacts resulting from a prototype of the proposed architecture and by using 100 GB of real network traffic, indeed demonstrate the practicality, effectiveness, and added-value of the proposed architecture.

关键词： big data architecture Hadoop Cyber security Machine learning Predictive analytics

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BASIS: A big data architecture for Smart Cities

BASIS: A Big Data Architecture for Smart Cities

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SAI Computing Conference (SAI)

作者： Costa, Carlos Santos, Maribel Yasmina Univ Minho ALGORITMI Res Ctr P-4719 Braga Guimaraes Portugal

ISBN: (纸本)9781467384605

Nowadays, cities are the common choice for living, representing a complex system where governments need to perform adequately, despite current restrictions, in order to satisfy the needs of the citizens and overcome economic, social and environmental sustainability challenges. The Smart City term emerges to conceptualize the need to understand citizens, namely their services demand and their relevance in a participatory government. Smart Cities are known for their human dynamics, which makes recurrent use of permanently connected devices, frequently known as Internet of Things (IoT). Consequently, since these new cities generate a vast volume of data with significant variety and velocity, they have the potential to be one of the richest and challenging systems to generate big data and to benefit from its adequate storage, processing, analysis and public availability. This paper presents a big data architecture for Smart Cities, entitled BASIS, whose specification pays particular attention to the creation of multiple abstraction layers, from the most conceptual to the most technological, fulfilling the lack of technological detail often observed in the literature. BASIS also pays particular attention to the public availability of data. Tested in a demonstration case, the obtained results reveal adequate capability to store, process, analyse and make available big data in the context of Smart Cities.

关键词： big data Smart Cities big data architecture big data Analytics

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A big data Analytics architecture for Industry 4.0 5th

A Big Data Analytics Architecture for Industry 4.0

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Santos, Maribel Yasmina Oliveira e Sa, Jorge Costa, Carlos Galvao, Joao Andrade, Carina Martinho, Bruno Lima, Francisca Vale Costa, Eduarda Univ Minho ALGORITMI Res Ctr Guimaraes Portugal

ISBN: (纸本)9783319565385;9783319565378

In an era in which people, devices, infrastructures and sensors can constantly communicate exchanging data and, also, generating new data that traces many of these exchanges, vast volumes of data is generated giving the context for the emergence of the big data concept. In particular, recent developments in Information and Communications Technology (ICT) are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining the volume and variety of data, arriving at high velocity, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being big data systems needed to link all the entities and data needs of the factory. In this context, this paper proposes a big data Analytics architecture that includes layers dedicated to deal with all data needs, from collection to analysis and distribution.

关键词： Industry 4.0 big data big data architecture big data Analytics

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big data Analysis architecture for Multi IDS Sensors using Memory based Processor 6

Big Data Analysis Architecture for Multi IDS Sensors using M...

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International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)

作者： Saputra, Ferry Astika Salman, Muhammad Ramli, Kalamullah Abdillah, Abid Syarif, Iwan Univ Indonesia Dept Elect Engn Jakarta Indonesia Politekn Elekt Negeri Surabaya Dept Informat & Comp Engn Surabaya Indonesia

ISBN: (纸本)9781538607169

The massive internet usage is followed by the rise of cyber-related crime such as information stealing, denial-of service (DoS) attack, trojan and malware. To cope with the threats, one of most popular choice is using Intrusion Detection System (IDS). The logs produced by IDS in a day is huge and the limitation of computing power is the main problem to process that logs files. In this paper, we propose a big data analysis architecture of multi IDS sensors using in-memory data processing. Deployed IDS sensors are taking an extra role as computation slave to build scalable data analysis platform for network security analysis. So, adding more sensors means expanding computational resources. Adding to three sensors are helping data computation of clustering algorithm faster up to 27% comparing to the computation by using only one sensor. This research also introduces the use of memory-based processor, this system provides 7,9 times faster data processing than conservative MapReduce operation. And moreover, we also have performed botnets classification over Spark RDD that give high accuracy result to 99%.

关键词： intrusion detection system big data architecture network data analysis Memory-based Processor

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BASIS: A big data architecture for Smart Cities

BASIS: A Big Data Architecture for Smart Cities

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SAI Computing Conference

作者： Carlos Costa Maribel Yasmina Santos ALGORITMI Research Centre University of Minho Guimar?es Portugal

ISBN: (纸本)9781467384612

关键词： big data Smart Cities big data architecture big data Analytics

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SmartLAK: A big data architecture for Supporting Learning Analytics Services 45

SmartLAK: A Big Data Architecture for Supporting Learning An...

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45th Annual Frontiers in Education Conference (FIE)

作者： Rabelo, Thomas Lama, Manuel Amorim, Ricardo R. Vidal, Juan C. Fac Ciencias Aplicadas & Sociais Petr FACAPE Belo Horizonte MG Brazil Univ Santiago Compostela Ctr Invest Tecnoloxias Informac CiTIUS Santiago De Compostela 15782 Spain Univ Estado Bahia Dept Educ UNEB DEDC 7 Serrinha BA Brazil

ISBN: (纸本)9781479984541

In this paper, we present a big data software architecture that uses an ontology, based on the Experience API specification, to semantically represent the data streams generated by the learners when they undertake the learning activities of a course, e.g., in a course. These data are stored in a RDF database to provide a high performance access so learning analytics services can process the large amount of data generated in a virtual learning environment. These services provide valuable information to teachers and instructors such as predict the learner's performance, discover the real learning paths, extract the learner's behavior patterns and so on. The proposed architecture has been validated in the Educational Technology undergraduate course of the Degree in Pedagogy at the Faculty of Education of the University of Santiago de Compostela.

关键词： big data architecture Learning analytics Experience API

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Integrating data Quality in Industrial big data architectures: An Action Design Research Study 18th

Integrating Data Quality in Industrial Big Data Architecture...

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18th European Conference on Software architecture (ECSA)

作者： Ustunboyacioglu, Ipek Kumara, Indika Di Nucci, Dario Tamburri, Damian Andrew van den Heuvel, Willem-Jan Tilburg Univ Jheronimus Acad Data Sci Shertogenbosch Netherlands Univ Salerno Salerno Italy Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783031707964;9783031707971

In today's data-driven business environments, organizations heavily rely on high-quality data to make informed decisions and gain a competitive advantage. Organizations typically use big data architectures to store, process, and manage their exponentially growing enterprise data. However, ensuring data quality in such scenarios remains a significant challenge for many organizations. Despite the vast number of data quality tools available, integrating such tools into big data architectures has not been fully explored. In this study, we aim to formulate design principles to support systematically incorporating data quality testing into big data architectures. For this purpose, we performed an action design research study at a large organization in the Netherlands. Finally, we employed the architecture trade-off analysis method (ATAM) to evaluate our solution design.

关键词： Action Design Research data Quality big data architecture architecture Trade-off Analysis ATAM

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big data analytics in telecommunications: Governance, architecture and use cases

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第6期34卷 2758-2770页

作者： Kastouni, Mohamed Zouheir Lahcen, Ayoub Ait Univ Ibn Tofail Ecole Natl Sci Appl ENSA Lab Sci Ingenieur Kenitra Morocco

With the upsurge of data traffic due to the change in customer behavior towards the use of telecommu-nications services, fostered by the current global health situation (mainly due to Covid-19), the telecom-munications operators have a golden opportunity to create new sources of revenues using big data Analytics (BDA) solutions. Looking to setting up a BDA project, we faced several challenges, notably, in terms of choice of the technical solution from the plethora of the existing tools, and the choice of the gov-ernance methodologies for governing the project and the data. The majority of research documents related to the telecommunications industry have not addressed BDA project implementation from start to finish. The purpose of this study focuses on a BDA telecommunications project, namely, Project's Governance, architecture, data Governance and the BDA Project's Team. The last part of this study pre-sents useful BDA use cases, in terms of applications enabling revenue creation and cost optimization. It appears that this work will facilitate the implementation of BDA projects, and enable telecommunica-tions operators to have a better understanding about the fundamental aspects to be focused on. It is therefore, a study that will contribute positively toward such goal.(c) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： big data analytics big data project?s governance methodology big data architecture data governance methodology big data project?s team big data telecommunications use cases

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