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Business Analytics: The Art and Science of Big Data Ventana Research - The Tech Museum A new wave of business analytics is upon us, and it's blowing the lid of old paradigms. With the explosion of information assets resulting in Big Data and the exponential appetite for metrics and forward looking key indicators such as predictive analytics, companies need to take a different approach. Will analysts step to the foreground as critical leaders of analytics for business or will the much-discussed data scientists come to the rescue? Learn more about critical new research on the next generation of business analytics and how real time data will impact the operational analytics of the future. This session will examine the reality of business and IT now and look ahead to 2013. Gain exclusive insight into the research agenda that will shape the way organizations assess and invest into these two essential business technologies. Tony Cosentino, VP & Research Director, Business Analytics, Ventana Research

With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques. 

With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques. 

With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques. 

With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques. 

With the inauguration of Big Data Analytics initiative nationally, many nations have participated and paved way for BDA ecosystem. The initiative is a catalyst to further encourage economic growth in Public Sectors. Some of the common key deliverables identified are increasing productivity involving information communications technology, cost savings, shared benefits, and encourage innovation. The objectives can be further elaborated by driving big data analytics demands in various public sectors agency, adopting big data analytics framework supporting the building of big data industry. This has encouraged talents and startup companies inspiring their capabilities by developing various technology platform, collaborate and innovate amongst public and private sectors, and further strengthen data governance by creating policy and procedures. With the establishment of big data analytics framework, performance measurement can be enforced effortlessly using the principles of business intelligence maturity model and the technological stack comes with it. Various data sources can be used to benchmark service quality using advanced analytics and data science techniques. 

Big Data e Analytics Profª. Rosangela Marquesone  Pesquisadora no Laboratório de Arquitetura e Redes de Computadores (LARC-USP), atuando nas áreas de computação em nuvem e Big Data. Atua como professora e palestrante de cursos de Big Data para empresas e programas de MBA, tendo ministrado mais de 400 horas de aula sobre o tema. Também atua como revisora de código no Nanodegree Analista de Dados da Udacity. É mestre e doutoranda em Engenharia de Computação pela Escola Politécnica da USP. Escreveu o livro Big Data – Técnicas e tecnologias para extração de valor dos dados, publicado pela editora Casa do Código. Seus principais interesses de pesquisa são: Big Data, computação em nuvem e people analytics. Também se interessa por temas como design thinking, vegetarianismo, mulheres na tecnologia e empreendedorismo social. 1) O que é Big Data ? 2) Que fatos são mais relevantes sobre Big Data? 3) Quais são as tendências no campo das profissões nessa área? 4) Qual é o futuro da Big Data?

Data rates are growing some 40% each year, and the sheer amount of data has outstripped common analytics and staffing levels — creating a serious data analysis gap. To move forward, organizations must transform big data into smart data, which means being able to analyze data on a massive scale and quickly use it to provide deeper insights, create new products, and differentiate services. New data science is built on velocity (fast moving data), variety (different kinds of data), and volume (large amounts of data). This panel will explore what kinds of approaches and analytic tools are now being used and to what end. It will also examine the opportunities, risks, and likely developments the near future holds as analysis helps us transform the commodities of big data into valuable smart data. Drake Baer Robert Schukai Don Tapscott Guruduth Banavar Bill Thoet, Moderator

Big data analytics (BDA) has increasingly become popular both in theory and practice in recent years. Globally, larger businesses have used BDA to collect, study, and evaluate vast volumes of data to identify market trends and insights that lead to sound and intelligent business decisions. However, its adoption in small and medium enterprises (SMEs) is not fully maximized because of a variety of factors, including a lack of expertise and financial repercussions. As such, this paper seeks to delve into the predictors of BDA adoption intention among SMEs in a developing nation by extending the technology acceptance model (TAM). The quantitative surveys obtained from 438 SMEs were analyzed using partial least squares and structural equation modeling (PLS-SEM). The results revealed that perceived benefits, namely system quality, information quality, and predictive analytics accuracy, had positive relationships with perceived ease of use and usefulness, subsequently leading to attitude towards using BDA. Likewise, perceived security significantly influences perceived benefits, perceived ease of use, and attitude towards use of BDA. Further, attitude towards use was the most significant predictor of intention to adopt BDA among SMEs. Generally, the study indicates a positive interest in adopting BDA among Philippine SMEs.

Big data is the new technology for healthcare management system. Present day's big data analytics are using in everywhere because of its good data management and its large storage capacity. In hospital managements the patients and doctors record keeping safe is the important role in healthcare system. In worldwide the big data method is extended use in the area of medicine and healthcare system. In this sector so many problems are there in implementing big data in healthcare system especially in relation to securities, privacy matters, standard records, good governance, managing of data, data storing and maintenance, etc. It is critical that these challenges to overcome before big data can be implemented successfully in healthcare. The amount of data being digitally collected and stored safely in big data Hadoop clusters. This paper introduces healthcare data, big data in healthcare systems, applications, advantages, issues of Big Data analytics in healthcare sector. Gagana H. S | Bhavani B. T | Gouthami H. S "Big Data Analytics using in Healthcare Management System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31014.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/31014/big-data-analytics-using-in-healthcare-management-system/gagana-h-s

With the emergence of cloud computing and sensor technologies, Big Data analytics for the Internet of Things (IoT) has become the main force behind many innovative solutions for our society's problems. This paper provides practical explanations for the question "why is the number of Big Data applications that succeed and have an effect on our daily life so limited, compared with all of the solutions proposed and tested in the literature?", with examples taken from Smart Grids. We argue that "noninvariants" are the most challenging issues in IoT applications, which can be easily revealed if we use the term "invariant" to replace the more common terms such as "information", "knowledge", or "insight" in any Big Data for IoT research. From our experience with developing Smart Grid applications, we produced a list of "noninvariants", which we believe to be the main causes of the gaps between Big Data in a laboratory and in practice in IoT applications. This paper also proposes Graph of Virtual Actors (GOVA) as a Big Data analytics architecture for IoT applications, which not only can solve the noninvariants issues, but can also quickly scale horizontally in terms of computation, data storage, caching requirements, and programmability of the system.

Big Data is a new term used to identify the datasets that due to their large size, we cannot manage them with the typical data mining software tools. Instead of defining “Big Data” as datasets of a concrete large size, for example in the order of magnitude of petabytes, the definition is related to the fact that the dataset is too big to be managed without using new algorithms or technologies. Big Data analytics – the process of analyzing and mining Big Data – can produce operational and business knowledge at an unprecedented scale and specificity. The need to analyze and leverage trend data collected by businesses is one of the main drivers for Big Data analysis tools. The technological advances in storage, processing, and analysis of Big Data include (a) the rapidly decreasing cost of storage and CPU power in recent years; (b) the flexibility and cost-effectiveness of datacenters and cloud computing for elastic computation and storage; and (c) the development of new frameworks such as Hadoop, which allow users to take advantage of these distributed computing systems storing large quantities of data through flexible parallel processing. 

Human smuggling organizations facilitating the smuggling of aliens into the United States have an unlawful network supporting their illicit transnational activities. Identifying those networks and the key facilitators is challenging due to high volumes of disparate data. This research focuses on how big data analytics can improve the effectiveness and efficiency of Homeland Security Investigations (HSI) targeting human smuggling networks. The purpose of this thesis is to determine whether applying big data analytics to data associated with human smuggling will make network identification of illegal aliens more efficient while producing the necessary articulable facts to substantiate enough probable cause for subsequent investigative actions. An experimental data analytics application called Citrus is used to examine the efficiency and effectiveness of data analytics supporting criminal investigations. Citrus revealed that big data analytics can effectively produce knowledge, including probable cause, more efficiently for HSI in targeting criminal networks. The implications are significant, as the application of data analytics may reshape analytical tradecraft, and compel HSI to revamp data systems. Increases in efficiencies through data analytics may be limited without changes in judicial processes. Upgrading processing capacities for obtaining warrants will become vital as analytics becomes more prevalent.

Human smuggling organizations facilitating the smuggling of aliens into the United States have an unlawful network supporting their illicit transnational activities. Identifying those networks and the key facilitators is challenging due to high volumes of disparate data. This research focuses on how big data analytics can improve the effectiveness and efficiency of Homeland Security Investigations (HSI) targeting human smuggling networks. The purpose of this thesis is to determine whether applying big data analytics to data associated with human smuggling will make network identification of illegal aliens more efficient while producing the necessary articulable facts to substantiate enough probable cause for subsequent investigative actions. An experimental data analytics application called Citrus is used to examine the efficiency and effectiveness of data analytics supporting criminal investigations. Citrus revealed that big data analytics can effectively produce knowledge, including probable cause, more efficiently for HSI in targeting criminal networks. The implications are significant, as the application of data analytics may reshape analytical tradecraft, and compel HSI to revamp data systems. Increases in efficiencies through data analytics may be limited without changes in judicial processes. Upgrading processing capacities for obtaining warrants will become vital as analytics becomes more prevalent.

It is about the Top 60 Big Data Analytics Singapore and Banking Companies that you would need to work with and help your business integrate big data analytics Singapore and banking for increased productivity.

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Big Data technologies uses a new generation of technologies and architectures, designed for organizations can extract value from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis. Big data is a massive amount of digital data being collected from various sources that are too large. Big data deals with challenges like complexity, security, risks to privacy. Big data is redefining as data management from extraction, transformation, cleaning and reducing. The size and variety of data lead us to think ahead and develop new and faster methods of mining data which uses the parallel computing capability of processors. The above is known as big data. S. Senthil Kumar | Ms.V.Kirthika"Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4673.pdf Article URL: http://www.ijtsrd.com/computer-science/other/4673/big-data-analytics-architecture-and-challenges-issues-of-big-data-analytics/s-senthil-kumar

Big Data technologies uses a new generation of technologies and architectures, designed for organizations can extract value from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis. Big data is a massive amount of digital data being collected from various sources that are too large. Big data deals with challenges like complexity, security, risks to privacy. Big data is redefining as data management from extraction, transformation, cleaning and reducing. The size and variety of data lead us to think ahead and develop new and faster methods of mining data which uses the parallel computing capability of processors. The above is known as big data. S. Senthil Kumar | Ms.V.Kirthika"Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4673.pdf Article URL: http://www.ijtsrd.com/computer-science/other/4673/big-data-analytics-architecture-and-challenges-issues-of-big-data-analytics/s-senthil-kumar

Social Media has wider scope in today’s World. There are over 900 social media sites available in the market. so that the massive information from that sites and problem with that info is storage. It is a popular way for people to expressing their thoughts and feelings and another important aspect in this study is Sentimental Analysis which is a study that include to analyze people’s opinion and importance is to decide the achievement of social network The main aim of this study is to find different technique for analyzing social media information. Jignesh Sunil Naik "Impact of Big Data Analytics on Social Media" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30223.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/30223/impact-of-big-data-analytics-on-social-media/jignesh-sunil-naik

This article introduces the special issue from the 2015 Learning Analytics and Knowledge conference. We describe the current state of the field and identify some of the trends in recent research. As the field continues to expand, there seem to be at least three directions of vigorous growth: (1) the inclusion of multimodal data (gesture, eye-tracking, biosensors), (2) the diversification of learning environments (MOOCs, classrooms, hands-on learning), and (3) new types of research questions considering a broader set of learning-related constructs (e.g., moving away from the focus on student retention).

This article introduces the special issue from the 2015 Learning Analytics and Knowledge conference. We describe the current state of the field and identify some of the trends in recent research. As the field continues to expand, there seem to be at least three directions of vigorous growth: (1) the inclusion of multimodal data (gesture, eye-tracking, biosensors), (2) the diversification of learning environments (MOOCs, classrooms, hands-on learning), and (3) new types of research questions considering a broader set of learning-related constructs (e.g., moving away from the focus on student retention).

In today’s competitive digital landscape, businesses are constantly seeking ways to improve their marketing performance. One of the most powerful tools at their disposal is Data Analytics in Marketing . By leveraging big data effectively, companies can gain deep insights into consumer behaviour, campaign performance, and market trends. This data-driven approach not only improves targeting and personalization but also significantly boosts marketing ROI. Here are 10 proven tips for using big data analytics to maximize your marketing return on investment.   1. Define Clear Marketing Objectives Before diving into analytics, clearly define what you want to achieve. Whether it's increasing website traffic, improving lead quality, or boosting conversions, setting measurable goals will help guide your data strategy. When goals are aligned with analytics, you're more likely to uncover actionable insights that impact your ROI.   2. Integrate All Your Data Sources To get a 360-degree view of your customer journey, integrate data from multiple platforms—social media, email, website analytics, CRM, and sales. Data Analytics in Marketing becomes far more effective when you're analysing unified data instead of fragmented pieces. A consolidated view helps identify patterns and gaps in performance.   3. Use Predictive Analytics to Forecast Trends Predictive analytics uses historical data and machine learning algorithms to forecast future outcomes. In marketing, it can help you anticipate customer behaviour, identify high-value leads, and allocate budgets more efficiently. Implementing predictive models can significantly improve the precision of your marketing strategy and ROI.   4. Segment Your Audience Intelligently One-size-fits-all marketing is outdated. Use Data Analytics in Marketing to segment your audience based on demographics, behaviour, purchase history, and engagement levels. Personalized marketing campaigns targeted to specific segments often perform better and reduce wasted spend.   5. Optimize Campaigns in Real-Time Big data enables real-time analytics, allowing marketers to track and tweak campaigns on the fly. Monitor metrics like click-through rates, bounce rates, and conversion rates continuously. With immediate insights, you can pivot strategies quickly and avoid pouring budget into underperforming campaigns.   6. Enhance Customer Experience Your marketing success hinges on customer satisfaction. Use big data to analyse feedback, social sentiment, and customer support interactions. By understanding what your customers like or dislike, you can tailor messaging and content that resonates with them, leading to higher engagement and ROI.   7. Leverage A/B Testing and Experimentation Data Analytics in Marketing allows for robust A/B testing. Whether you're testing email subject lines, ad creatives, or landing pages, data can tell you what’s working and what’s not. Running controlled experiments and measuring results helps you make informed decisions that improve performance over time.   8. Track the Entire Customer Journey Understanding every touchpoint of your customer journey is crucial. Big data tools can map the path from awareness to conversion. Identifying which channels contribute the most to ROI enables you to allocate resources wisely and cut back on low-performing strategies.   9. Utilize Attribution Modelling Not all conversions happen in one click. Attribution modelling helps you understand the impact of each channel and interaction. With multi-touch attribution, you can see which platforms (social, email, SEO, paid ads) are driving value, ensuring your budget is spent where it matters most.   10. Continuously Educate Your Marketing Team Technology and analytics tools are evolving rapidly. Make sure your team stays up-to-date with the latest in Data Analytics in Marketing . Invest in training and workshops so your marketers can leverage new tools and interpret data effectively to drive consistent ROI improvements.   Final Thoughts Big data isn’t just a buzzword—it’s a critical asset for today’s marketers. When used strategically, Data Analytics in Marketing empowers you to make smarter decisions, better understand your customers, and fine-tune your campaigns for maximum impact. From predictive modelling to real-time optimization, these 10 proven tips can help you harness the full potential of big data to drive measurable results. As competition continues to grow, marketers who embrace data-driven strategies will lead the charge in delivering exceptional ROI and sustainable business growth. Want to upskill in this high-demand field? Explore George Global Education’s Diploma in Business Analytics with Generative AI —a cutting-edge program designed to equip professionals with the latest tools and techniques in data analytics and artificial intelligence, tailored for modern marketing success. Ready to make smarter marketing decisions? Start implementing these data-backed strategies today and watch your ROI soar.

Big data, business analytics, business intelligence (BI), soluções data-driven… são termos que já se tornaram recorrentes. Mas, na prática, como resolver problemas concretos de negócios com dados estruturados? Debatemos esse e outros tópicos relacionados no PapoQuik 04 - Big data e business analytics na prática . Nosso mediador, co-fundador e CEO da QuikDev , João Paulo Geroldo , recebe os convidados Paulo de Sousa , diretor administrativo na Cory, indústria alimentícia, e Wellington Faria , engenheiro de dados sênior na Nubank. Eles conversaram sobre temas como benefícios da adoção de soluções em BI, o poder da agilidade nas informações em tempo real para tomada de decisão, como ser uma empresa com uma cultura orientada a dados e muito mais. Uma conversa sensacional, você não pode perder! O que você está esperando? Escute no podcast ou veja no vídeo ! https://www.youtube.com/watch?v=dQKiGipj8-Q Assista à gravação da transmissão ao vivo Ficha técnica PapoQuik é um encontro online e gratuito sobre inovação , tecnologia e empreendedorismo com quem adora fazer o impossível. Produzido pela QuikDev , uma corporate venture builder que constrói negócios e produtos digitais na velocidade da inovação. Somos especialistas em ideação, prototipação, desenvolvimento e escala de produtos e negócios para a nova economia , entregando soluções de ponta a ponta sob medida para o seu negócio. Apresentação : João Paulo Geroldo Direção e Produção : Alberto Geraissate Desenvolvimento de Negócios : Diego Flores e Natália Bessa Mídias Sociais : Nileide Garrido e Natalí Silva (Ro1 Digital) Identidade Visual e Design Gráfico : Rafael Angeruzzi Identidade Sonora e Edição de Áudio : Kaíque Figueiredo Co-Fundador : Thiago Fernandes CEO e Co-Fundador : João Paulo Geroldo Acompanhe nossas redes sociais para outros eventos incríveis: Siga a QuikDev no Instagram e no Facebook . Se conecte com a gente no LinkedIn . Assista as gravações das lives do PapoQuik em nosso canal no YouTube . Quer conhecer melhor a QuikDev? Entre em contato com um de nossos especialistas. Até o próximo episódio!

In this session, we'll discuss Google Cloud’s vision and engineering strategy that can help you move big data analytics solutions to the next level of benefits. Google Cloud Platform combines powerful serverless solutions for enterprise data warehousing, streaming analytics, managed Spark and Hadoop, modern BI, planet-scale data lake, and AI. We'll share how our customers are seamlessly integrating GCP services to innovative big data solutions, explain new partner solutions that are making it easy for you to capture value from big data, and demonstrate new solutions and product capabilities. SPTL108 Event schedule → http://g.co/next18 Watch more Spotlight sessions here → http://bit.ly/2Lby9sW Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub re_ty: Publish; product: Cloud - General; fullname: Sudhir Hasbe; event: Google Cloud Next 2018; Source: https://www.youtube.com/watch?v=-VBGZsSUKlg Uploader: Google Cloud Tech

In this session, we'll discuss Google Cloud’s vision and engineering strategy that can help you move big data analytics solutions to the next level of benefits. Google Cloud Platform combines powerful serverless solutions for enterprise data warehousing, streaming analytics, managed Spark and Hadoop, modern BI, planet-scale data lake, and AI. We'll share how our customers are seamlessly integrating GCP services to innovative big data solutions, explain new partner solutions that are making it easy for you to capture value from big data, and demonstrate new solutions and product capabilities. SPTL108 Event schedule → http://g.co/next18 Watch more Spotlight sessions here → http://bit.ly/2Lby9sW Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub re_ty: Publish; product: Cloud - General; fullname: Sudhir Hasbe; event: Google Cloud Next 2018; Source: https://www.youtube.com/watch?v=-VBGZsSUKlg Uploader: Google Cloud Tech

This study quantitatively analyzes the impact of a free trade agreement (FTA) among China, Japan, and South Korea using big data analytics. Using game theory and the computable general equilibrium approach, it proposes a compromise between the two countries for agricultural protection, to reduce possible divergences and confrontations. The findings show that game results differ among the three countries as per interest indexes. Compared to full tariff exemption, an FTA with agricultural protection not only stimulates economic growth in the three countries but also reduces Japan and South Korea's agricultural concerns and impact on employment. We also evaluate the impacts of the trilateral FTA on manufacturing and services industries. The results show that China will increase imports of energy-intensive products from Japan and South Korea, which may reduce domestic output and generate environmental benefits. Implementing the trilateral FTA with agricultural protection may reduce carbon emission in Northeast Asia by 6.53 million tons. This study can promote economic integration in Northeast Asia and coping with climate change. The analysis also highlights the importance of lifecycle management of energy-intensive industries in China, Japan, and South Korea.

This study quantitatively analyzes the impact of a free trade agreement (FTA) among China, Japan, and South Korea using big data analytics. Using game theory and the computable general equilibrium approach, it proposes a compromise between the two countries for agricultural protection, to reduce possible divergences and confrontations. The findings show that game results differ among the three countries as per interest indexes. Compared to full tariff exemption, an FTA with agricultural protection not only stimulates economic growth in the three countries but also reduces Japan and South Korea's agricultural concerns and impact on employment. We also evaluate the impacts of the trilateral FTA on manufacturing and services industries. The results show that China will increase imports of energy-intensive products from Japan and South Korea, which may reduce domestic output and generate environmental benefits. Implementing the trilateral FTA with agricultural protection may reduce carbon emission in Northeast Asia by 6.53 million tons. This study can promote economic integration in Northeast Asia and coping with climate change. The analysis also highlights the importance of lifecycle management of energy-intensive industries in China, Japan, and South Korea.

This study quantitatively analyzes the impact of a free trade agreement (FTA) among China, Japan, and South Korea using big data analytics. Using game theory and the computable general equilibrium approach, it proposes a compromise between the two countries for agricultural protection, to reduce possible divergences and confrontations. The findings show that game results differ among the three countries as per interest indexes. Compared to full tariff exemption, an FTA with agricultural protection not only stimulates economic growth in the three countries but also reduces Japan and South Korea's agricultural concerns and impact on employment. We also evaluate the impacts of the trilateral FTA on manufacturing and services industries. The results show that China will increase imports of energy-intensive products from Japan and South Korea, which may reduce domestic output and generate environmental benefits. Implementing the trilateral FTA with agricultural protection may reduce carbon emission in Northeast Asia by 6.53 million tons. This study can promote economic integration in Northeast Asia and coping with climate change. The analysis also highlights the importance of lifecycle management of energy-intensive industries in China, Japan, and South Korea.

In recent years, the adoption of big data analytics has revolutionized supply chain management by enhancing decision-making processes and optimizing operations. This paper explores the integration of big data analytics in supply chain optimization, emphasizing its impact on improving delivery time, cost efficiency, customer satisfaction, and flexibility. The study provides an overview of the foundational principles of big data analytics and its application in supply chain management, highlighting the challenges and opportunities associated with its implementation. Through a comprehensive review of existing literature and case studies, the paper discusses the potential benefits of big data-driven supply chain strategies and outlines future research directions aimed at addressing the dynamic complexities of modern supply chains. 

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The term 'œBig data' itself defines the voluminous data and analytics of the big data that requires smart collection, handling and utilization. With the evolution of technology this might look simple but by virtue of its existence and elegance in its availability it poses a big challenge. This paper aims to bring together the face of the past, present and future aspects big data analytics in the light of its various challenges. Abirami N | K. Veerasamy"Big Data Analytics Challenges and Opportunities Ahead '“ Beyond the Hype" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd8258.pdf  http://www.ijtsrd.com/computer-science/data-miining/8258/big-data-analytics-challenges-and-opportunities-ahead-'“-beyond-the-hype/abirami-n

In recent years, technology has brought us many advancements. One of them is integrating big data with smart grid/smart power. In this study, a scientific approach used to help the power system is studied. Additionally, with the help of previously published literature, different survey papers are reviewed to investigate the key challenges of integrating Big Data Analytics (BDA) with smart grid. Subsequently, BDA characteristics are also studied. Next, data analysis techniques and BDA applications in the domain of smart grids are studied. It is followed by a section discussing techniques such as Hadoop and Spark. Their framework is also briefly examined in order to know about their working. The last section provides a conclusion and future directions.

In recent years, technology has brought us many advancements. One of them is integrating big data with smart grid/smart power. In this study, a scientific approach used to help the power system is studied. Additionally, with the help of previously published literature, different survey papers are reviewed to investigate the key challenges of integrating Big Data Analytics (BDA) with smart grid. Subsequently, BDA characteristics are also studied. Next, data analysis techniques and BDA applications in the domain of smart grids are studied. It is followed by a section discussing techniques such as Hadoop and Spark. Their framework is also briefly examined in order to know about their working. The last section provides a conclusion and future directions.

Big Data & Data Analytics Kelas VII (Pertemuan 2)

Discover how AI and machine learning transform big data into actionable insights, driving innovation, automation, and smarter decision-making.

The proliferation of big data has significantly expanded the quantity and breadth of information throughout the DoD. The task of processing and analyzing this data has become difficult, if not infeasible, using traditional relational databases. The Navy has a growing priority for information processing, exploitation, and dissemination, which makes use of the vast network of sensors that produce a large amount of big data. This capstone report explores the feasibility of a scalable Tactical Cloud architecture that will harness and utilize the underlying open-source tools for big data analytics. A virtualized cloud environment was built and analyzed at the Naval Postgraduate School, which offers a test bed, suitable for studying novel variations of these architectures. Further, the technologies directly used to implement the test bed seek to demonstrate a sustainable methodology for rapidly configuring and deploying virtualized machines and provides an environment for performance benchmark and testing. The capstone findings indicate the strategies and best practices to automate the deployment, provisioning and management of big data clusters. The functionality we seek to support is a far more general goal: finding open-source tools that help to deploy and configure large clusters for on-demand big data analytics.

Big Data is becoming a critical component of the Information Systems curriculum. Educators are enhancing gradually the concentration curriculum for Big Data in schools of computer science and information systems. This paper proposes a creative curriculum design for Big Data Analytics for a program at a major metropolitan university. The design emphasizes expanded learning of business, mathematical and statistical, and presentation skills, in projects of teams, in addition to skills in technology. This paper will be beneficial to educators considering improvement of the curriculum for Big Data Analytics and to students desiring a more contemporary program.

An effective and scalable data analytics solution is  necessary due to the ever-increasing amount of digital data  generated by IoT devices, social media, and corporate systems.  Bigdata Analytics, when integrated with Cloud Computing,  provides a robust framework for managing and processing  massive datasets in a cost-effective and flexible manner. This  paper explores the scalability and performance considerations  essential for optimizing Big Data Analytics within cloud  environments. By leveraging virtualization, cloud resources  provide on-demand access to computing power, storage, and  analytics tools, addressing challenges such as data variety,  velocity, and volume.

A presentation about Samagraha Kumtumba Survey: A case Study of Big Data Analytics from Telangana State

The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era. Collecting MBD is unprofitable unless suitable analytics and learning methods are utilized for extracting meaningful information and hidden patterns from data. This article presents an overview and brief tutorial of deep learning in MBD analytics and discusses a scalable learning framework over Apache Spark. Specifically, a distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall MBD, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.

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Norman Nie of Revolution Analytics goes deep on the concept of big data. Source: https://youtube.com/watch?v=3RmLjXmzcN0 Uploader: gigaom Upload date: 2013-05-28

Organizations rely on computer software to aid in the accurate and timely analysis of data. Open source software (OSS) is becoming more prevalent in all organizations from academia to industry to government. Utilizing Python with proprietary compute power (e.g., SAS Viya) enables users of all levels to apply advanced analytics, data visualizations, and complex statistical modeling capabilities to aspects of the analytics lifecycle, producing descriptive and predictive data artifacts. This session will provide an overview of OSS with proprietary compute power as it pertains to business organizational analysis, the analytics lifecycle, and it gives participants a better understanding of how to leverage software for higher quality decision making. Speaker: Joseph Castle, PhD