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2023 IEEE International Conference on big data, bigdata 2023

作者： Dhaouadi, Asma Paccoud, William Bousselmi, Khadija Monnet, Sebastien Gammoudi, Mohamed Mohsen Hammoudi, Slimane Usmb Listic Lab Annecy France Tunis El Manar University Riadi Lab Tunisia Savoie Mont Blanc University Listic Lab Polytech Annecy France Savoie Mont Blanc University Listic Lab Iut Annecy France La Manouba University Riadi Lab Isamm La Manouba Tunisia Angers University Eris ESEO-TECH Angers France

ISBN: (纸本)9798350324457

The technological revolution, the huge sharing of data via social networks, web and mobile applications and IoT devices are generating a huge volume of data every day, commonly referred to as 'big data'. To cope with big data and the challenges associated with their specific features, the last decade, a multitude of technologies and platforms have emerged to harness their potential. The community is still seeking a comprehensive and up-to-date comparative study of these tools. Such an experimentally-derived study is essential for enabling informed decision-making, fostering innovation, and ensuring that organizations can make the best choices when implementing big data solutions. In this paper, a multi-purpose experimental study was conducted. The primary objective is to provide an overview of today's most popular big data tools, and to evaluate their interoperability. The second is to test performance by varying different technical constraints. The aim of these tests is twofold: i) To compare the resource consumption requirements of these tools, ii) To evaluate the impact of resource variation of one tool on the performance of another one in the same big data pipeline. © 2023 IEEE.

关键词： big data tools Performance testing resource consumption Technologies tools Interoperability

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A proposal for Optimization of data Node by Horizontal Scaling of Name Node using big data tools 3

A proposal for Optimization of Data Node by Horizontal Scali...

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3rd International Conference for Convergence in Technology (I2CT)

作者： Roy, Chandrima Pandey, Manjusha SwarupRautaray, Siddharth KIIT Univ Sch Comp Engn Bhubaneswar Orissa India

ISBN: (纸本)9781538642733

The data which are beyond the storage space of the server and beyond to the processing power is called big data. It is not manageable by traditional RDBMS or conventional statistical tools. big data Increases the storage capacities as well as the processing power. Horizontal scaling or sharding is needed to divide the data set and distributes the data over multiple servers. Redundancy and fault tolerance is achieved by horizontal scaling. Optimization of horizontal scaling is an important aspect of big data technology. Instead of using vertical scaling that means upgrading to fancier computers when the current system becomes inadequate, we have to add more node (computers) to a cluster. It increases the parallelism, rather than the performance of any one node. This paper presents the fundamentals of big data analytics but directing towards an analysis of various optimization techniques used in the big data environment.

关键词： Optimization Horizontal scaling Scalability Hadoop big data tools

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JetBrains推出big data tools大数据开发工具

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智能制造 2019年第12期 8-9页

近日,软件开发公司JetBrains宣布推出big data tools大数据开发工具,即一款集成Spark且支持编辑和运行Zeppelin Notebooks的Intelli J IDEA插件,从而为开发者们带来更加高效、舒适的大数据处理和开发体验。Jet Brians大中华区负责人李... 详细信息

近日,软件开发公司JetBrains宣布推出big data tools大数据开发工具,即一款集成Spark且支持编辑和运行Zeppelin Notebooks的Intelli J IDEA插件,从而为开发者们带来更加高效、舒适的大数据处理和开发体验。Jet Brians大中华区负责人李萱表示:"近几年来,Jet Brains对数据分析、数据科学工具做了许多投入,包含针对数据处理最受欢迎的程序语言Python,在其Python集

关键词：开发工具 big data tools JetBrains

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The Hybrid Integration of BD & its Analytics in the Area of Corporate Sector: Systematic Review

The Hybrid Integration of BD & its Analytics in the Area of ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Singla, Abhishek Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research for Business and Technology Punjab India

ISBN: (纸本)9798350357769

The rise of big data has transformed the way corporations operate and make decisions. However, with the vast amounts of data available, identifying suitable big data strategies can be challenging. This paper aims to provide insights into the different big data methodologies that partnerships can take on to actually outfit the capability of big data. It features the significance of distinguishing business goals and choosing the proper big data advancements and instruments. Furthermore, it discusses the challenges and ethical considerations that corporations need to consider while implementing big data strategies. © 2023 IEEE.

关键词： big data technologies big data tools business objectives challenges corporate environment ethical considerations IndexTerms- big data

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Exploring Strategies to Leverage Open Source data Visualization Platforms in Developing big data Visual Analytics

Exploring Strategies to Leverage Open Source Data Visualizat...

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作者： Rashed, Tarek Colorado Technical University

学位级别：D.C.S., Doctor of Computer Science

Developing BDVA tools using Open Source data Visualization (OSDV) has become a popular approach for creating software tools due to the fact that it can be done at lower costs and at a faster pace. Embedding OSDV to develop Visual Analytics (VA) tools in order to work effectively with big data platforms is challenging. Thus, software production might not be valuable without establishing an effective combination of strategy, technology, and skills. This study aimed to learn from big data specialists' and developers’ standpoints how strategies are implemented to discover skills and methods for effectively leveraging OSDV to advance the development of BDVA tools. Through a qualitative exploratory approach, 10 big data professional were interviewed. Firstly, the study findings indicated that developing VA tools via OSDV requires many technologies. These include developing libraries to integrate data from various sources and interactively presenting data in various visual diagrams. Secondly, developing any successful BDVA requires a combination of accurate and clean data, analytical tasks, and graph types. Lastly, the study revealed that many existing OSDV, which are used to generate dashboards and charts, have a lack of technical documentations. Although this study explored the strategies, the findings motivate research recommendations: (a) identifying the best strategies to achieve interactive mechanisms, (b) exploring the capabilities of OSDV in developing visualization tools for the healthcare data, (c) exploring professionals' experiences regarding open-source data quality management tools, and (d) exploring experiences of practitioners and then developing a strategy that might mitigate the risks of reusing ***: Open-Source Libraries, big data, Visual Analytics, Visualization tools, big data tools, big data Technology.

关键词： big data technology big data big data tools Open-source libraries Visual analytics Visualization tools

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Aquaponics Revolution: Reinforcing performance by means of Apache Spark and Apache Kafka

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Procedia Computer Science 2024年 241卷 624-629页

作者： EZZAHOUI Ibtissame AIT ABDELOUAHID Rachida MARZAK Abdelaziz Hassan II University of Casablanca-Faculty of Sciences Ben M'sik Laboratory of Information of Technology and Modeling Cdt Driss El Harti BP 7955 Sidi Othman Casablanca Morocco

Many studies provide ways to monitor and manage the aquaponics system, a closed-loop ecosystem that combines hydroponics—growing plants without soil—and aquaculture—raising fish—using cloud computing, the Internet of Things (IoT), and smart sensing technologies. A system operating at peak efficiency yields organic, healthful food with little water and no chemical fertilizer usage, therefore contributing to food security and sustainability. In order to develop our proposed aquaponic system based on Internet of Things technologies, this paper will centralize our endeavor to integrate big data tools, namely Apache Spark and Kafka, into the previous architecture for data collection and processing, as well as for the prediction of any anomalies in the system.

关键词： Aquaponic System Hydroponic farming Aquaculture farming IoT big data tools Apache Spark Apache Kafka data Collection

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The state of the art and taxonomy of big data analytics: view from new big data framework

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第2期53卷 989-1037页

作者： Mohamed, Azlinah Najafabadi, Maryam Khanian Wah, Yap Bee Zaman, Ezzatul Akmal Kamaru Maskat, Ruhaila Univ Teknol MARA Adv Analyt Engn Ctr Fac Comp & Math Sci Shah Alam Selangor Malaysia INTI Int Univ & Coll Fac Informat Technol Nilai Negeri Sembilan Malaysia

big data has become a significant research area due to the birth of enormous data generated from various sources like social media, internet of things and multimedia applications. big data has played critical role in many decision makings and forecasting domains such as recommendation systems, business analysis, healthcare, web display advertising, clinicians, transportation, fraud detection and tourism marketing. The rapid development of various big data tools such as Hadoop, Storm, Spark, Flink, Kafka and Pig in research and industrial communities has allowed the huge number of data to be distributed, communicated and processed. big data applications use big data analytics techniques to efficiently analyze large amounts of data. However, choosing the suitable big data tools based on batch and stream data processing and analytics techniques for development a big data system are difficult due to the challenges in processing and applying big data. Practitioners and researchers who are developing big data systems have inadequate information about the current technology and requirement concerning the big data platform. Hence, the strengths and weaknesses of big data technologies and effective solutions for big data challenges are needed to be discussed. Hence, due to that, this paper presents a review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector. The goals of this paper are to (1) understand the trend of big data-related research and current frames of big data technologies;(2) identify trends in the use or research of big data tools based on batch and stream processing and big data analytics techniques;(3) assist and provide new researchers and practitioners to place new research activity in this domain appropriately. The findings of this study will provide insights

关键词： Parallel and distributed computing big data tools big data analytics techniques Domain area

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big data adoption challenges and benefits: the case of a South African bank

Big data adoption challenges and benefits: the case of a Sou...

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作者： Matsimbi, Johannes Chabane University of the Witwatersrand

学位级别：硕士

The origins of big data have not been confirmed; however, it is suspected that it originated in the mid-1990s. Since then, the adoption of big data has gained momentum in many industries. This study sought to establish how data is collected and highlight the challenges that are experienced during big data adoption. The research also sought to establish how the adoption of big data can benefit organisations. A qualitative study was conducted, consisting of a sample of 15 participants from the South Africa bank's Information Technology (IT) Department. Semi-structured interviews were conducted to collect the data. The data was analysed through the qualitative tool NVivo, to assist with the process of coming up with codes and patterns. Then these patterns were used to create themes to assist with analysis. The study revealed that the bank uses industry-appropriate tools that are also suitable for collecting and curating big data. The study further showed that skills scarcity could be a significant challenge when implementing big data projects. The architecture of the organisation, governance around how projects are implemented, as well as common understanding regarding how the data should be packaged, can be a challenge when implementing big data projects. Despite the challenges presented by big data, the study also found that there are benefits for big data adoption. The benefits for big data include, amongst others, enhancement of customer-centric marketing, data-driven product development and data monetisation, to mention, but a few. The study added to the body of knowledge (BoK) by demonstrating that the benefits of big data adoption outweigh the challenges presented by big data. The study suggests that more investments should be put in place for change management when implementing projects of this magnitude. Change management would assist in monitoring the impact of the projects on how people do their jobs and provide clarity on the required changes regarding how

关键词： big data big data Analytics big data tools Challenges of big data Benefits of big data Predictive data Prescriptive data data Monetisation Financial Sector Banking South African Environment

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Methodology for the Implementation of Knowledge Management Systems 2.0: A Case Study in an Oil and Gas Company

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BUSINESS & INFORMATION SYSTEMS ENGINEERING 2019年第2期61卷 195-213页

作者： Orenga-Rogla, Sergio Chalmeta, Ricardo Univ Jaume 1 Grp Invest Integrac & Reingn Sistemas IRIS Dept Lenguajes & Sistemas Informat Ave Sos Baynat S-N Castellon de La Plana 12071 Spain

Web 2.0 and big data tools can be used to develop knowledge management systems based on facilitating the participation and collaboration of people in order to enhance knowledge. The paper presents a methodology that can help organizations with the use of Web 2.0 and big data tools to discover, gather, manage and apply their knowledge by making the process of implementing a knowledge management system faster and simpler. First, an initial version of the methodology was developed and it was then applied to an oil and gas company in order to analyze and refine it. The results obtained show the effectiveness of the methodology, since it helped this company to carry out the implementation quickly and effectively, thereby allowing the company to gain the maximum benefits from existing knowledge.

关键词： Knowledge Knowledge management Web 2 0 Web 2 0 tools big data big data tools KMS 2 0

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An infrastructure model for smart cities based on big data

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2018年第4期9卷 322-332页

作者： Gomes, Eliza H. A. Dantas, Mario A. R. de Macedo, Douglas D. J. De Rolt, Carlos R. Dias, Julio Foschini, Luca Univ Fed Santa Catarina Dept Informat & Stat INE Technol Ctr CTC Grad Program Comp Sci PGCC Florianopolis SC Brazil Fed Univ Juiz de Fora UFJF Dept Comp Sci DCC Exact Sci Inst ICE Juiz De Fora MG Brazil Univ Fed Santa Catarina Dept Informat Sci CIN Florianopolis SC Brazil State Univ Santa Catarina UDESC Ctr Management & Socioecon Sci ESAG Florianopolis SC Brazil Univ Bologna Dept Comp Sci Engn DISI Bologna Italy

The massive amount of data generated in projects focused on smart cities creates a degree of complexity in how to manage all this information. In attention to solve this problem, several approaches have been developed in recent years. In this paper we propose an infrastructure model for big data for a smart city project. The goal of this model is to present the stages for the processing of data in the steps of extraction, storage, processing and visualisation, as well as the types of tools needed for each phase. To implement our proposed model, we used the ParticipACT Brazil, which is a project based in smart cities. This project uses different databases to compose its big data and uses this data to solve urban problems. We observe that our model provides a structured vision of the software to be used in big data server of ParticipACT Brazil.

关键词： big data smart city big data tools

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