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19th International Conference on Smart Business Technologies (ICSBT)

作者： Staegemann, Daniel Volk, Matthias Turowski, Klaus Otto von Guericke Univ Magdeburg Res & Competence Cluster VLBA Magdeburg Germany

ISBN: (纸本)9789897585876

Knowledge, information, and modern technologies have become some of the most influential drivers of today's society, consequently leading to a high popularity of the concepts of big data (BD). However, their actual harnessing is a demanding task that is accompanied by many barriers and challenges. To facilitate the realization of the corresponding projects, the (big) data science engineering process (BDSEP) has been devised to support researchers and practitioners in the planning and implementation of data intensive projects by outlining the relevant steps. However, the BDSEP is only geared towards a test last development approach. With recent works suggesting the application of test driven development (TDD) in the big data domain, it appears reasonable to also provide a corresponding TDD focused equivalent to the BDSEP. Therefore, in the publication at hand, using the BDSEP as a foundation, the test driven big data science engineering process (TDBDSEP) is proposed, facilitating the application of TDD in the big data domain and further enriching the discourse on BD quality assurance.

关键词： big data data Science Software engineering big data engineering Test Driven Development TDD Process BDSEP

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On the Cognitive and Theoretical Foundations of big data Science and engineering

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NEW MATHEMATICS AND NATURAL COMPUTATION 2017年第2期13卷 101-117页

作者： Wang, Yingxu Univ Calgary Schulich Sch Engn Dept Elect & Comp Engn Int Inst Cognit Informat & Cognit Comp ICIC 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Univ Calgary Hotchkiss Brain Inst 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Stanford Univ Informat Syst Lab Stanford CA 94305 USA

big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significantfinding towards big data science is that big data systems in nature are a recursive n-dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering.

关键词： big data mathematical models recursive hyperstructures typed quantification big data science big data engineering big data analytics big data systems cognitive informatics computational intelligence denotational mathematics applications

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data Mining Evoking Scintillators Auto-Discovery for Low-LoD High-Resolution Deep-Penetrating X-Ray Imaging of Portable Digital Radiography

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ADVANCED FUNCTIONAL MATERIALS 2025年第7期35卷

作者： Zhang, Shaoan Li, Zhenzhang Ye, Huacong An, Ximei Xu, Jialong Chen, Zhishan Chen, Yiqing Fang, Zaijin Sun, Shilian Qiu, Jianrong Li, Yang Ningbo Univ Inst Light X Sci & Technol Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Guangdong Polytech Normal Univ Coll Math & Syst Sci Guangzhou 510665 Peoples R China Guangzhou Med Univ Sch Biomed Engn Guangzhou 511436 Guangdong Peoples R China Zhongke Optoelect Technol Weihai Co LTD 81 Wenhua Middle Rd Weihai 264200 Peoples R China Zhejiang Univ Coll Opt Sci & Engn State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China

The advancement of portable X-ray digital radiography devices is currently hindered by the limited penetration depth, limit of detection (LoD), and spatial resolution of commercially available scintillators for X-ray imaging. In this study, data mining engineering is proposed to boom X-ray imaging quality via the auto-discovery of potentially overlooked inorganic scintillators. This mining criteria encompassed the comprehensive set of screening parameters, including equivalent substitution, structural rigidity, effective atomic number, and material density. Among 27 389 potential candidates identified in this analysis, Lu2WO6:Eu3+ scintillator is successfully mined with the feature of light yield at approximate to 27 600 photons/MeV. This scintillator exhibits a low LoD of approximate to 8.5 nGyair s-1- significantly lower than the standard dosage typically employed in X-ray diagnostics. It also demonstrats exceptional reliability in terms of penetration depth and spatial resolution for X-ray imaging at distances of 51.9 mm and line pairs per millimeter values of 18.2, respectively. The peripheral X-ray leakage dose of the portable DR device is restricted to only 682 uSv h-1, thus ensuring safe usage without necessitating additional protective measures while enabling various applications such as indoor or outdoor high-voltage wire damage detection and field first aid. We proposed data mining engineering to boom X-ray imaging quality via the auto-discovery of potentially overlooked inorganic scintillators. A overlooked scintillator, Lu2WO6:Eu3+, was mined with a light yield at 27600 photons/MeV and a low limit of detection at 8.5 nGyair/s. This scintillator film exhibited deep penetration depth (51.9 mm) and high spatial resolution (18.2 lp/mm) for X-ray imaging, respectively. image

关键词： big data engineering europium-doping scintillator machine learning

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Designing Fault-tolerant Modern data engineering Solutions with Reliability Theory as The Driving Force 24

Designing Fault-tolerant Modern Data Engineering Solutions w...

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9th International Conference on Machine Learning Technologies (ICMLT)

作者： Virmani, Ankit Kuppam, Manoj Inst Elect & Elect Engineers Piscataway NJ 08854 USA

ISBN: (纸本)9798400716379

This study has been undertaken to amalgamate the principles of Site Reliability engineering and data engineering to effectively measure, monitor and manage the reliability of petabyte-scale data engineering process from collection at source to processing, analyzing, and distributing the data for appropriate decision making to improve business outcomes and system performance. Modern data architectures increasingly leverage cloud platforms, low-code systems, and serverless technologies to enable scalable data engineering. However, these innovations also introduce new complexities regarding reliability assurance. As these failure-prone yet business-critical data infrastructures continue rapid adoption, it is vital to elucidate architectural paradigms, quantified benchmarks, and procedural methodologies tailored to safeguarding dependability across heterogeneous, distributed data ecosystems. This paper will equip end users with a reusable framework ingrained with best practices to develop a blueprint for data reliability across business units of an organization.

关键词： big data engineering Site Reliability engineering data Operations Platform Management Cloud Computing

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big data in the construction industry: A review of present status, opportunities, and future trends

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ADVANCED engineering INFORMATICS 2016年第3期30卷 500-521页

作者： Bilal, Muhammad Oyedele, Lukumon O. Qadir, Junaid Munir, Kamran Ajayi, Saheed O. Akinade, Olugbenga O. Owolabi, Hakeem A. Alaka, Hafiz A. Pasha, Maruf Univ West England Bristol Enterprise Res & Innovat Ctr Bristol Business Sch Frenchay CampusColdharbour Lane Bristol BS16 1QY Avon England Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad Pakistan Bahauddin Zakariya Univ Dept Informat Technol Multan Pakistan

The ability to process large amounts of data and to extract useful insights from data has revolutionised society. This phenomenon dubbed as big data has applications for a wide assortment of industries, including the construction industry. The construction industry already deals with large volumes of heterogeneous data;which is expected to increase exponentially as technologies such as sensor networks and the Internet of Things are commoditised. In this paper, we present a detailed survey of the literature, investigating the application of big data techniques in the construction industry. We reviewed related works published in the databases of American Association of Civil Engineers (ASCE), Institute of Electrical and Electronics Engineers (IEEE), Association of Computing Machinery (ACM), and Elsevier Science Direct Digital Library. While the application of data analytics in the construction industry is not new, the adoption of big data technologies in this industry remains at a nascent stage and lags the broad uptake of these technologies in other fields. To the best of our knowledge, there is currently no comprehensive survey of big data techniques in the context of the construction industry. This paper fills the void and presents a wide-ranging interdisciplinary review of literature of fields such as statistics, data mining and warehousing, machine learning, and big data Analytics in the context of the construction industry. We discuss the current state of adoption of big data in the construction industry and discuss the future potential of such technologies across the multiple domain-specific sub-areas of the construction industry. We also propose open issues and directions for future work along with potential pitfalls associated with big data adoption in the industry. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： big data engineering big data Analytics Construction industry Machine learning

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Professional Network Analytics Platform for Enterprise Collaboration

Professional Network Analytics Platform for Enterprise Colla...

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ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW)

作者： Gopalakrishnan, Gopakumar Benhur, Krupa Kaushik, Abhishek Passala, Anjaneyulu Infosys Ltd Bangalore Karnataka India

ISBN: (纸本)9781450346887

A fundamental challenge to effective coordination of work across various boundaries in large and geographically distributed organizations is the ability of employees to identify who knows what, who knows whom, and who is doing what. In this demo, we introduce a professional network analytics platform that analyzes large and diverse set of operational records in an enterprise and constructs professional networks between employees. Further, we illustrate certain collaboration support services built around these networks in a contextually relevant manner.

关键词： Professional social networks Enterprise network analysis Project Collaboration big data engineering

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A Rapid Prototyping Approach for High Performance Density-Based Clustering 6

A Rapid Prototyping Approach for High Performance Density-Ba...

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6th IEEE International Conference on data Science and Advanced Analytics (DSAA)

作者： Islam, Saiyedul Balasubramaniam, Sundar Goyal, Poonam Sultana, Ankit Bhutani, Lakshit Raje, Saurabh Goyal, Navneet Birla Inst Technol & Sci Pilani Adv Data Analyt & Parallel Technol Lab Dept Comp Sci & Informat Syst Pilani Campus Pilani Rajasthan India

ISBN: (纸本)9781728144931

big data has significantly increased the dependence of data analytics community on High Performance Computing (HPC) systems. However, efficiently programming an HPC system is still a tedious task requiring specialized skills in parallelization and the use of platform-specific languages as well as mechanisms. We present a framework for quickly prototyping new/existing density-based clustering algorithms while obtaining low running times and high speedups via automatic parallelization. The user is required only to specify the sequential algorithm in a Domain Specific Language (DSL) for clustering at a very high level of abstraction. The parallelizing compiler for the DSL does the rest to leverage distributed systems in particular, typical scale-out clusters made of commodity hardware. Our approach is based on recurring, parallelizable programming patterns known as Kernels, which are identified and parallelized by the compiler. We demonstrate the ease of programming and scalable performance for DBSCAN, SNN, and RECOME algorithms. We also establish that the proposed approach can achieve performance comparable to state-of-the-art manually parallelized implementations while requiring minimal programming effort that is several orders of magnitude smaller than those required on other parallel platforms like MPI/Spark.

关键词： Clustering Domain Specific Language big data engineering Programming Patterns Parallelizing Compiler

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Unification of Machine Learning Features 44

Unification of Machine Learning Features

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44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Patel, Jayesh Rockstar Games San Diego CA 92008 USA

ISBN: (纸本)9781728173030

In the Information Age, Machine learning (ML) provides a competitive advantage to any business. Machine learning applications are not limited to driverless cars or online recommendations but are widely used in healthcare, social services, government systems, telecommunications, and so on. As many enterprises are trying to step up machine learning applications, it is critical to have a long-term strategy. Most of the enterprises are not able to truly realize the fruits of ML capabilities due to its complexity. It is easier to access a variety of data today due to data democratization, distributed storage, technological advancements, and big data applications. Despite easier data access and recent advancements in ML, developers still spend most of the time in data cleansing, data preparation, and data modeling for ML applications. These steps are often repeated and result in identical features. As identical features can have inconsistent processing while testing and training, more issues pop up at later stages in ML application development. The unification of ML features is an effective way to address these issues. This paper presents details about numerous methods to achieve ML features unification.

关键词： ML Pipeline Feature engineering ML Development Feature store big data engineering

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Infrastructure-Aware Functional Testing of MapReduce programs 4

Infrastructure-Aware Functional Testing of MapReduce program...

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

作者： Moran, Jesus Rivas, Bibiano de la Riva, Claudio Tuya, Javier Caballero, Ismael Serrano, Manuel Univ Oviedo Dept Comp Gijon Spain Univ Castilla La Mancha Inst Technol & Informat Syst Ciudad Real Spain

ISBN: (纸本)9781509039463

Programs that process a large volume of data generally run in a distributed and parallel architecture, such as the programs implemented in the processing model MapReduce. In these programs, developers can abstract the infrastructure where the program will run and focus on the functional issues. However, the infrastructure configuration and its state cause different parallel executions of the program and some could derive in functional faults which are hard to reveal. In general, the infrastructure that executes the program is not considered during the testing, because the tests usually contain few input data and then the parallelization is not necessary. In this paper a testing technique is proposed to generate different infrastructure configurations for a given test input data, and then the program is executed in these configurations in order to reveal functional faults. This testing technique is automatized by using a test engine and is applied to a case study. As a result, several infrastructure configurations are automatically generated and executed for a test case revealing a functional fault that is then fixed by the developer.

关键词： Software testing MapReduce programs big data engineering Hadoop

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An Overview on Testing big data Applications 9th

An Overview on Testing Big Data Applications

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9th International Congress on Information and Communication Technology (ICICT)

作者： Staegemann, Daniel Ataei, Pouya Lautenschlaeger, Erik Pohl, Matthias Haertel, Christian Daase, Christian Volk, Matthias Abdallah, Mohammad Turowski, Klaus Otto von Guericke Univ Univ Pl 2 D-39106 Magdeburg Germany Auckland Univ Technol 55 Wellesley St East Auckland 1010 New Zealand IU Int Univ Appl Sci Mulheimer Str 38 D-53604 Bad Honnef Germany Al Zaytoonah Univ Jordan Queen Alia Airport St 594 Amman 11733 Jordan

ISBN: (纸本)9789819732982;9789819732999

big data respectively big data analytics is one of the defining technological developments of our time and directly or indirectly influences countless aspects of daily life in today's society. Yet, for organizations to reap the corresponding benefits of implementing big data analytics, besides other factors, it is necessary to assure that the respective applications are functioning as intended. Hence, their testing is a very important aspect that is, however, oftentimes somewhat overlooked. To increase its accessibility, in the publication at hand, this topic is addressed. For this purpose, in the first step, the typical components of big data applications are identified, and their general nature is outlined. Then, subsequently, suitable approaches for the components' testing are discussed. In doing so, researchers and practitioners alike shall be provided with a starting point when it comes to the discussion of big data quality assurance and how it can be implemented in their own endeavors.

关键词： big data Testing Quality assurance big data engineering Software engineering Overview

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