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big data algorithms beyond machine learning

KUNSTLICHE INTELLIGENZ 2018年第1期32卷 9-17页

作者： Mnich, Matthias Maastricht Univ Dept Quantitat Econ POB 616 NL-6200 MD Maastricht Netherlands Univ Bonn Inst Informat Friedrich Ebert Allee 144 D-53113 Bonn Germany

The availability of big data sets in research, industry and society in general has opened up many possibilities of how to use this data. In many applications, however, it is not the data itself that is of interest but rather we want to answer some question about it. These answers may sometimes be phrased as solutions to an optimization problem. We survey some algorithmic methods that optimize over large-scale data sets, beyond the realm of machine learning.

关键词： big data algorithms Large-scale optimization Kernelization Dynamic algorithms

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big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2021年 231卷 107868-107868页

作者： Kaffash, Sepideh An Truong Nguyen Zhu, Joe Suffolk Univ Sawyer Business Sch Boston MA 02108 USA Worcester Polytech Inst Foisie Business Sch Worcester MA 01609 USA

The volume and availability of data in the Intelligent Transportation System (ITS) result in the need for data driven approaches. big data algorithms are applied to further enhance the intelligence of the applications in the transportation field. Applying big data algorithms has increasingly received attention in both the academic and industrial fields of ITS. big data algorithms in ITS have a wide range of applications including but not limited to signal recognition, object detection, traffic flow prediction, travel time planning, travel route planning and safety of vehicle and road. This survey aims to provide a bibliography, a comprehensive review of the application of ITS and a review of most recognized models with big data used in the context of ITS. 586 papers are reviewed over the period 1997-2019. This study provides a deep insight into applications of big data algorithms in ITS, revealing different areas of those applications and integrates models and applications. The result of the study identifies research gaps and direction for the future.

关键词： Intelligent transportation system big data algorithms Survey Traffic flow prediction

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Empirical and Experimental Perspectives on big data in Recommendation Systems:A Comprehensive Survey

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big data Mining and Analytics 2024年第3期7卷 964-1014页

作者： Kamal Taha Paul D.Yoo Chan Yeun Aya Taha Department of Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates(UAE) School of Computing and Mathematical Sciences Birkbeck CollegeUniversity of LondonLondon WC1E 7HUUK Department of Science Brighton CollegeDubai 122002United Arab Emirates

This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing *** proposes a two-pronged approach:a thorough analysis of current algorithms and a novel,hierarchical taxonomy for precise *** taxonomy is based on a tri-level hierarchy,starting with the methodology category and narrowing down to specific *** a framework allows for a structured and comprehensive classification of algorithms,assisting researchers in understanding the interrelationships among diverse algorithms and *** a wide range of algorithms,this taxonomy first categorizes algorithms into four main analysis types:user and item similarity based methods,hybrid and combined approaches,deep learning and algorithmic methods,and mathematical modeling methods,with further subdivisions into sub-categories and *** paper incorporates both empirical and experimental evaluations to differentiate between the *** empirical evaluation ranks the techniques based on four *** experimental assessments rank the algorithms that belong to the same category,sub-category,technique,and ***,the paper illuminates the future prospects of big data techniques in recommendation systems,underscoring potential advancements and opportunities for further research in this fields.

关键词： big data algorithms recommendation systems recommendation algorithms deep learning in recommendations

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big data Testing Framework for Recommendation Systems in e-Science and e-Commerce Domains

Big Data Testing Framework for Recommendation Systems in e-S...

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9th IEEE International Conference on big data (IEEE bigdata)

作者： Uzun-Per, Meryem Can, Ali Burak Gurel, Ahmet Volkan Aktas, Mehmet S. Akdeniz PE TUR AS BiletBank Res & Dev Ctr Istanbul Turkey Istanbul Hlth & Technol Univ Comp Engn Dept Istanbul Turkey Yildiz Tech Univ Comp Engn Dept Istanbul Turkey

ISBN: (纸本)9781665439022

Software testing is an important process to evaluate whether the developed software applications meet the required specifications. There is an emerging need for testing frameworks for big data software projects to ensure the quality of the big data applications and satisfy the user requirements. In this study, we propose a software testing framework that can be utilized in big data projects both in e-science and e-commerce. In particular, we design the proposed framework to test big data-based recommendation applications. To show the usability of the proposed framework, we provide a reference prototype implementation and use the prototype to test a big data recommendation application. We apply the prototype implementation to test both functional and non-functional methods of the recommendation application. The results indicate that the proposed testing framework is usable and efficient for testing the recommendation systems that use big data processing techniques.

关键词： testing framework testing for big data projects recommendation systems big data algorithms distributed systems Spark MLlib

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Automating Open Science for big data

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ANNALS OF THE AMERICAN ACADEMY OF POLITICAL AND SOCIAL SCIENCE 2015年第1期659卷 260-273页

作者： Crosas, Merce King, Gary Honaker, James Sweeney, Latanya Harvard Univ Data Sci Program Inst Quantitat Social Sci Cambridge MA 02138 USA Harvard Univ Inst Quantitat Social Sci Cambridge MA 02138 USA Harvard Univ Data Privacy Lab Cambridge MA 02138 USA Amer Coll Med Informat Indianapolis IN USA

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. These fixed-scale datasets are commonly downloaded to the researcher's computer where the analysis is performed. The data can be shared, archived, and cited with well-established technologies, such as the dataverse Project, to support the published results. The trend toward big dataincluding large-scale streaming datais starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher's computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. The increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

关键词： big data repository archive data privacy big data methods big data algorithms differential privacy

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Low-Carbon Intelligent Management System and Application of Garden Benefit Evaluation Based on big data Algorithm 2

Low-Carbon Intelligent Management System and Application of ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： You, Li Changchun University of Architecture and Civil Engineering China

ISBN: (纸本)9798350317558

With the increasingly serious global climate change, low-carbon development has become a common concern of all countries. As the largest developing country in the world, China actively responds to climate change and vigorously promotes the development of low-carbon economy. Under this background, low-carbon intelligent management system has been widely used in landscape benefit evaluation. In this paper, the application of low-carbon intelligent management system in landscape benefit evaluation will be discussed in depth. © 2024 IEEE.

关键词： big data algorithms ecological environment landscape architecture theory Landscape planning

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Analysis of the Application Status of big data Algorithm in Power Marketing 3

Analysis of the Application Status of Big Data Algorithm in ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Xu, Yukun Zhu, Zheng Jiang, Chao Liu, Chang State Grid Shanghai Customer Service Center Shanghai200300 China

With the continuous development of Internet and information technology, big data has become an important topic in today's society. In the power industry, big data algorithms are gradually being applied. This paper... 详细信息

ISBN: (纸本)9798350319125

关键词： big data big data algorithms electric power enterprises Electricity marketing

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Prediction of the Destruction of Materials with Inhomogeneous Structures

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PHYSICS OF METALS AND METALLOGRAPHY 2022年第12期123卷 1253-1264页

作者： Kudrya, A. V. Sokolovskaya, E. A. Natl Res Technol Univ MISiS Moscow 119049 Russia

Structural and metallurgical factors that cause the differences in the fracture resistance of steels and alloys are studied, which is necessary for predicting the destruction of media with an inhomogeneous structure. The prospects for digitalization of measuring the parameters of the structure and fracture surfaces using big data algorithms are considered.

关键词： heterogeneity of structure and fracture digitalization of measurements in metal science quantitative description of structures and destruction prediction of material destruction big data algorithms cognitive graphics techniques

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Fuzzy Association Rules Mining Using Spark 17th

Fuzzy Association Rules Mining Using Spark

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17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)

作者： Fernandez-Bassso, Carlos Dolores Ruiz, M. Martin-Bautista, Maria J. Univ Granada CITIC UGR Comp Sci & AI Dept Granada Spain Univ Cadiz Dept Comp Engn Cadiz Spain

ISBN: (纸本)9783319914763;9783319914756

Discovering new trends and co-occurrences in massive data is a key step when analysing social media, data coming from sensors, etc. Traditional data Mining techniques are not able, in many occasions, to handle such amount of data. For this reason, some approaches have arisen in the last decade to develop parallel and distributed versions of previously known techniques. Frequent itemset mining is not an exception and in the literature there exist several proposals using not only parallel approximations but also Spark and Hadoop developments following the MapReduce philosophy of big data. When processing fuzzy data sets or extracting fuzzy associations from crisp data the implementation of such big data solutions becomes crucial, since available algorithms increase their execution time and memory consumption due to the problem of not having Boolean items. In this paper, we first review existing parallel and distributed algorithms for frequent itemset and association rule mining in the crisp and fuzzy case, and afterwards we develop a preliminary proposal for mining not only frequent fuzzy itemsets but also fuzzy association rules. We also study the performance of the proposed algorithm in several datasets that have been conveniently fuzzyfied obtaining promising results.

关键词： big data algorithms Fuzzy frequent itemset Fuzzy association rules data Mining Apriori

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Scaling GMM Expectation Maximization Algorithm Using Bulk Synchronous Parallel Approach 1

Scaling GMM Expectation Maximization Algorithm Using Bulk Sy...

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International Conference on Green Computing and Internet of Things (ICGCIoT)

作者： Ratnaparkhi, Abhay A. Pilli, Emmanuel Joshi, R. C. Graph Era Univ Dept Comp Sci & Engn Dehra Dun Uttar Pradesh India Dept Comp Sci & Engn MNIT Jaipur Rajasthan India

ISBN: (纸本)9781467379106

We have provided a parallel implementation of Gaussian Mixture Model (GMM) Expectation Maximization algorithm using Apache Hama Bulk synchronous Parallel approach. Apache Hama is suitable for iterative, compute intensive tasks. EM is iterative algorithm which converges to local minimum after many iterations. We have provided approach for distributing workload for Expectation and Maximization tasks on cluster nodes in case of big data. The approach is compared with Hadoop MaprRduce and Apache Spark implementations, using different datasets.

关键词： expectation maximization bulk synchronous parallel big data algorithms parallel EM clustering

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