In this presentation how cloud is useful in big data analytics.It givers brief introduction to cloud service models and Big data 4V's.Here I'm describing how cloud is used in telecom and finance domain. How it is better than traditional methods.
Cloud computing is the on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. It evolved from earlier technologies like grid computing and utility computing by providing greater ease of use and on-demand scaling. A cloud broker acts as an intermediary between cloud service providers and customers, providing a unified interface and moving workloads between public and private clouds for improved performance and redundancy.
The document is a question bank for the cloud computing course CS8791. It contains 26 multiple choice or short answer questions related to key concepts in cloud computing including definitions of cloud computing, characteristics of clouds, deployment models, service models, elasticity, horizontal and vertical scaling, live migration techniques, and dynamic resource provisioning.
Cloud Migration: Moving Data and Infrastructure to the CloudSafe Software
The movement to the cloud is accelerating across industries. This is driven by the maturing of cloud technology, and by the sudden shift to a more distributed and remote workforce.
The cloud has many strengths from no longer having to purchase and manage infrastructure to its ability to grow seamlessly and to scale up and down to meet demands.
With all these benefits, many organizations are preparing cloud migration strategies (such as on-premise to the cloud) and are finding themselves overwhelmed by the process.
There are many things to consider when planning a cloud migration but the process does not have to be complicated or costly due to private services. Join this webinar to learn how you get started with your cloud migration today!
The document discusses cloud computing and data security. It provides an overview of cloud computing including deployment models, service models, and sub-service models. It also discusses key aspects of cloud data security such as authentication using OTP, encryption of data using strong algorithms, and ensuring data integrity through hashing. The proposed cloud data security model uses three levels of defense - strong authentication through OTP, automatic encryption of data using a fast and strong algorithm, and fast recovery of user data.
The document discusses the benefits of moving business applications and workloads to the cloud. It notes that the cloud powers many modern businesses by providing flexibility and allowing organizations to pay only for the computing resources they use. The cloud offers advantages like horizontal scaling to meet spikes in demand, quick provisioning without lengthy purchase orders, abundant file storage, and a hybrid cloud model combining public and private resources. Common workloads that can benefit from the cloud include email, large media files, and web servers.
Cloud architectures can be thought of in layers, with each layer providing services to the next. There are three main layers: virtualization of resources, services layer, and server management processes. Virtualization abstracts hardware and provides flexibility. The services layer provides OS and application services. Management processes support service delivery through image management, deployment, scheduling, reporting, etc. When providing compute and storage services, considerations include hardware selection, virtualization, failover/redundancy, and reporting. Network services require capacity planning, redundancy, and reporting.
This document provides an overview of Google Cloud Platform (GCP) services. It discusses computing services like App Engine and Compute Engine for hosting applications. It covers storage options like Cloud Storage, Cloud Datastore and Cloud SQL. It also mentions big data services like BigQuery and machine learning services like Prediction API. The document provides brief descriptions of each service and highlights their key features. It includes code samples for using Prediction API to train a model and make predictions on new data.
Cloud computing is the on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. It evolved from earlier technologies like grid computing and utility computing by providing greater ease of use and on-demand scaling. A cloud broker acts as an intermediary between cloud service providers and customers, providing a unified interface and moving workloads between public and private clouds for improved performance and redundancy.
The document is a question bank for the cloud computing course CS8791. It contains 26 multiple choice or short answer questions related to key concepts in cloud computing including definitions of cloud computing, characteristics of clouds, deployment models, service models, elasticity, horizontal and vertical scaling, live migration techniques, and dynamic resource provisioning.
Cloud Migration: Moving Data and Infrastructure to the CloudSafe Software
The movement to the cloud is accelerating across industries. This is driven by the maturing of cloud technology, and by the sudden shift to a more distributed and remote workforce.
The cloud has many strengths from no longer having to purchase and manage infrastructure to its ability to grow seamlessly and to scale up and down to meet demands.
With all these benefits, many organizations are preparing cloud migration strategies (such as on-premise to the cloud) and are finding themselves overwhelmed by the process.
There are many things to consider when planning a cloud migration but the process does not have to be complicated or costly due to private services. Join this webinar to learn how you get started with your cloud migration today!
The document discusses cloud computing and data security. It provides an overview of cloud computing including deployment models, service models, and sub-service models. It also discusses key aspects of cloud data security such as authentication using OTP, encryption of data using strong algorithms, and ensuring data integrity through hashing. The proposed cloud data security model uses three levels of defense - strong authentication through OTP, automatic encryption of data using a fast and strong algorithm, and fast recovery of user data.
The document discusses the benefits of moving business applications and workloads to the cloud. It notes that the cloud powers many modern businesses by providing flexibility and allowing organizations to pay only for the computing resources they use. The cloud offers advantages like horizontal scaling to meet spikes in demand, quick provisioning without lengthy purchase orders, abundant file storage, and a hybrid cloud model combining public and private resources. Common workloads that can benefit from the cloud include email, large media files, and web servers.
Cloud architectures can be thought of in layers, with each layer providing services to the next. There are three main layers: virtualization of resources, services layer, and server management processes. Virtualization abstracts hardware and provides flexibility. The services layer provides OS and application services. Management processes support service delivery through image management, deployment, scheduling, reporting, etc. When providing compute and storage services, considerations include hardware selection, virtualization, failover/redundancy, and reporting. Network services require capacity planning, redundancy, and reporting.
This document provides an overview of Google Cloud Platform (GCP) services. It discusses computing services like App Engine and Compute Engine for hosting applications. It covers storage options like Cloud Storage, Cloud Datastore and Cloud SQL. It also mentions big data services like BigQuery and machine learning services like Prediction API. The document provides brief descriptions of each service and highlights their key features. It includes code samples for using Prediction API to train a model and make predictions on new data.
Cloud Computing offers an on-demand and scalable access to a shared pool of resources hosted in a data center at providers’ site. It reduces the overheads of up-front investments and financial risks for the end-user. Regardless of the fact that cloud computing offers great advantages to the end users, there are several challenging issues that are mandatory to be addressed.
Cloud computing provides a way for organizations to share distributed resources over a network. However, data security is a major concern in cloud computing since data is stored remotely. The document discusses several techniques used for data security in cloud computing including authentication, encryption, data masking, and data traceability. The latest technologies discussed are a cloud information gateway that can control data transmission and secure logic migration that transfers applications to an internal sandbox for secure execution.
This document discusses cloud computing, big data, Hadoop, and data analytics. It begins with an introduction to cloud computing, explaining its benefits like scalability, reliability, and low costs. It then covers big data concepts like the 3 Vs (volume, variety, velocity), Hadoop for processing large datasets, and MapReduce as a programming model. The document also discusses data analytics, describing different types like descriptive, diagnostic, predictive, and prescriptive analytics. It emphasizes that insights from analyzing big data are more valuable than raw data. Finally, it concludes that cloud computing can enhance business efficiency by enabling flexible access to computing resources for tasks like big data analytics.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
Streaming data involves the continuous analysis of data as it is generated in real-time. It allows for data to be processed and transformed in memory before being stored. Popular streaming technologies include Apache Storm, Apache Flink, and Apache Spark Streaming, which allow for processing streams of data across clusters. Each technology has its own approach such as micro-batching but all aim to enable real-time analysis of high-velocity data streams.
The document outlines a lecture on privacy preserving data mining. It discusses the motivation for privacy preserving data mining, including the need to analyze sensitive individual data for applications like detecting fraud or disease outbreaks while maintaining privacy. It covers the scope, typical architecture involving modifying original data, common techniques like data perturbation and cryptographic methods, advantages like enabling large data sharing, and applications like securing medical databases. The conclusion emphasizes that privacy preserving data mining has become important for conducting analytics while respecting individuals' privacy rights.
The document discusses several security challenges related to cloud computing. It covers topics like data breaches, misconfiguration issues, lack of cloud security strategy, insufficient identity and access management, account hijacking, insider threats, and insecure application programming interfaces. The document emphasizes that securing customer data and applications is critical for cloud service providers to maintain trust and meet compliance requirements.
Viet-Trung Tran presents information on big data and cloud computing. The document discusses key concepts like what constitutes big data, popular big data management systems like Hadoop and NoSQL databases, and how cloud computing can enable big data processing by providing scalable infrastructure. Some benefits of running big data analytics on the cloud include cost reduction, rapid provisioning, and flexibility/scalability. However, big data may not always be suitable for the cloud due to issues like data security, latency requirements, and multi-tenancy overhead.
Fault tolerance is important for distributed systems to continue functioning in the event of partial failures. There are several phases to achieving fault tolerance: fault detection, diagnosis, evidence generation, assessment, and recovery. Common techniques include replication, where multiple copies of data are stored at different sites to increase availability if one site fails, and check pointing, where a system's state is periodically saved to stable storage so the system can be restored to a previous consistent state if a failure occurs. Both techniques have limitations around managing consistency with replication and overhead from checkpointing communications and storage requirements.
This is basically about the hybrid cloud and steps to implement them, starting from what is cloud, hybrid cloud to its implementation. Hybrid Cloud is nowadays implemented by many organisations and transitioning a traditional IT setup to a hybrid cloud model is no small undertaking. So, one should know about it and how it is implemented.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
Cloud computing delivers computing resources over a network and includes three service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Security threats to cloud computing include hackers abusing cloud resources to conduct denial of service attacks and brute force attacks at low cost. Data breaches are also a risk as sensitive data stored in the cloud has been targeted by online theft. Malware injection attacks and wrapping attacks that change the execution of web applications are additional security risks. Countermeasures include access management, data protection techniques, and implementing security policies and technologies.
The document discusses cloud security from the perspective of Wen-Pai Lu, a technical leader at Cisco. It defines cloud security as security products and solutions deployed within cloud computing environments ("in the cloud") or targeted at securing other cloud services ("for the cloud"). It also discusses security services delivered by cloud computing services ("by the cloud"). The document outlines many considerations for cloud security, including infrastructure security, applications and software, physical security, human risks, compliance, disaster recovery, threats, and perspectives from both enterprises and service providers.
Virtualization is a technique, which allows to share single physical instance of an application or resource among multiple organizations or tenants (customers)..
Virtualization is a proved technology that makes it possible to run multiple operating system and applications on the same server at same time.
Virtualization is the process of creating a logical(virtual) version of a server operating system, a storage device, or network services.
The technology that work behind virtualization is known as a virtual machine monitor(VM), or virtual manager which separates compute environments from the actual physical infrastructure.
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
This document discusses cloud security and provides an overview of McAfee's cloud security solutions. It summarizes McAfee's cloud security program, strengths, weaknesses, opportunities, threats, and competitors in the cloud security market. It also discusses Netflix's migration to the cloud for its infrastructure and content delivery and outlines Netflix's cloud security strategy.
The practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer.
Optimistic concurrency control in Distributed Systemsmridul mishra
This document discusses optimistic concurrency control, which is a concurrency control method that assumes transactions can frequently complete without interfering with each other. It operates by allowing transactions to access data without locking and validating for conflicts before committing. The validation checks if other transactions have read or written the same data. If a conflict is found, the transaction rolls back and restarts. The document outlines the basic algorithm, phases of transactions (read, validation, write), and advantages like low read wait time and easy recovery from deadlocks and disadvantages like potential for starvation and wasted resources if long transactions abort.
This document discusses methods for harnessing big data. It describes how sensors collect Internet of Things (IoT) data and how Volvo applies analytics. It also summarizes three methods: 1) The US Air Force uses an integrated data warehouse and geospatial analysis to track assets globally. 2) Siemens uses data discovery processes to predict train failures by analyzing sensor and failure report data. 3) Yahoo uses Hadoop as a data lake to store and analyze large amounts of user data from various sources like social media and clickstreams. The document emphasizes that no single technology is a silver bullet for big data.
Kaushal Amin & Big 5 IT trends in the worldQuang PM
Kaushal Amin, CTO of KMS Technology, presented the top 6 technology trends for 2013:
1. Mobile apps will continue to dominate, with businesses shifting to complex mobile business applications. Native apps will remain preferred but HTML5/hybrid will gain ground.
2. Big data will continue growing exponentially from various sources such as social media and sensors. NOSQL databases are better suited than SQL for massive scalability and unstructured data.
3. Cloud computing adoption will increase, with more organizations using cloud-based disaster recovery and personal cloud storage.
4. In-memory computing allows orders of magnitude faster analytics on large datasets and will see further adoption through cloud services.
5. Action
Cloud Computing offers an on-demand and scalable access to a shared pool of resources hosted in a data center at providers’ site. It reduces the overheads of up-front investments and financial risks for the end-user. Regardless of the fact that cloud computing offers great advantages to the end users, there are several challenging issues that are mandatory to be addressed.
Cloud computing provides a way for organizations to share distributed resources over a network. However, data security is a major concern in cloud computing since data is stored remotely. The document discusses several techniques used for data security in cloud computing including authentication, encryption, data masking, and data traceability. The latest technologies discussed are a cloud information gateway that can control data transmission and secure logic migration that transfers applications to an internal sandbox for secure execution.
This document discusses cloud computing, big data, Hadoop, and data analytics. It begins with an introduction to cloud computing, explaining its benefits like scalability, reliability, and low costs. It then covers big data concepts like the 3 Vs (volume, variety, velocity), Hadoop for processing large datasets, and MapReduce as a programming model. The document also discusses data analytics, describing different types like descriptive, diagnostic, predictive, and prescriptive analytics. It emphasizes that insights from analyzing big data are more valuable than raw data. Finally, it concludes that cloud computing can enhance business efficiency by enabling flexible access to computing resources for tasks like big data analytics.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
Streaming data involves the continuous analysis of data as it is generated in real-time. It allows for data to be processed and transformed in memory before being stored. Popular streaming technologies include Apache Storm, Apache Flink, and Apache Spark Streaming, which allow for processing streams of data across clusters. Each technology has its own approach such as micro-batching but all aim to enable real-time analysis of high-velocity data streams.
The document outlines a lecture on privacy preserving data mining. It discusses the motivation for privacy preserving data mining, including the need to analyze sensitive individual data for applications like detecting fraud or disease outbreaks while maintaining privacy. It covers the scope, typical architecture involving modifying original data, common techniques like data perturbation and cryptographic methods, advantages like enabling large data sharing, and applications like securing medical databases. The conclusion emphasizes that privacy preserving data mining has become important for conducting analytics while respecting individuals' privacy rights.
The document discusses several security challenges related to cloud computing. It covers topics like data breaches, misconfiguration issues, lack of cloud security strategy, insufficient identity and access management, account hijacking, insider threats, and insecure application programming interfaces. The document emphasizes that securing customer data and applications is critical for cloud service providers to maintain trust and meet compliance requirements.
Viet-Trung Tran presents information on big data and cloud computing. The document discusses key concepts like what constitutes big data, popular big data management systems like Hadoop and NoSQL databases, and how cloud computing can enable big data processing by providing scalable infrastructure. Some benefits of running big data analytics on the cloud include cost reduction, rapid provisioning, and flexibility/scalability. However, big data may not always be suitable for the cloud due to issues like data security, latency requirements, and multi-tenancy overhead.
Fault tolerance is important for distributed systems to continue functioning in the event of partial failures. There are several phases to achieving fault tolerance: fault detection, diagnosis, evidence generation, assessment, and recovery. Common techniques include replication, where multiple copies of data are stored at different sites to increase availability if one site fails, and check pointing, where a system's state is periodically saved to stable storage so the system can be restored to a previous consistent state if a failure occurs. Both techniques have limitations around managing consistency with replication and overhead from checkpointing communications and storage requirements.
This is basically about the hybrid cloud and steps to implement them, starting from what is cloud, hybrid cloud to its implementation. Hybrid Cloud is nowadays implemented by many organisations and transitioning a traditional IT setup to a hybrid cloud model is no small undertaking. So, one should know about it and how it is implemented.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
Cloud computing delivers computing resources over a network and includes three service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Security threats to cloud computing include hackers abusing cloud resources to conduct denial of service attacks and brute force attacks at low cost. Data breaches are also a risk as sensitive data stored in the cloud has been targeted by online theft. Malware injection attacks and wrapping attacks that change the execution of web applications are additional security risks. Countermeasures include access management, data protection techniques, and implementing security policies and technologies.
The document discusses cloud security from the perspective of Wen-Pai Lu, a technical leader at Cisco. It defines cloud security as security products and solutions deployed within cloud computing environments ("in the cloud") or targeted at securing other cloud services ("for the cloud"). It also discusses security services delivered by cloud computing services ("by the cloud"). The document outlines many considerations for cloud security, including infrastructure security, applications and software, physical security, human risks, compliance, disaster recovery, threats, and perspectives from both enterprises and service providers.
Virtualization is a technique, which allows to share single physical instance of an application or resource among multiple organizations or tenants (customers)..
Virtualization is a proved technology that makes it possible to run multiple operating system and applications on the same server at same time.
Virtualization is the process of creating a logical(virtual) version of a server operating system, a storage device, or network services.
The technology that work behind virtualization is known as a virtual machine monitor(VM), or virtual manager which separates compute environments from the actual physical infrastructure.
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
This document discusses cloud security and provides an overview of McAfee's cloud security solutions. It summarizes McAfee's cloud security program, strengths, weaknesses, opportunities, threats, and competitors in the cloud security market. It also discusses Netflix's migration to the cloud for its infrastructure and content delivery and outlines Netflix's cloud security strategy.
The practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer.
Optimistic concurrency control in Distributed Systemsmridul mishra
This document discusses optimistic concurrency control, which is a concurrency control method that assumes transactions can frequently complete without interfering with each other. It operates by allowing transactions to access data without locking and validating for conflicts before committing. The validation checks if other transactions have read or written the same data. If a conflict is found, the transaction rolls back and restarts. The document outlines the basic algorithm, phases of transactions (read, validation, write), and advantages like low read wait time and easy recovery from deadlocks and disadvantages like potential for starvation and wasted resources if long transactions abort.
This document discusses methods for harnessing big data. It describes how sensors collect Internet of Things (IoT) data and how Volvo applies analytics. It also summarizes three methods: 1) The US Air Force uses an integrated data warehouse and geospatial analysis to track assets globally. 2) Siemens uses data discovery processes to predict train failures by analyzing sensor and failure report data. 3) Yahoo uses Hadoop as a data lake to store and analyze large amounts of user data from various sources like social media and clickstreams. The document emphasizes that no single technology is a silver bullet for big data.
Kaushal Amin & Big 5 IT trends in the worldQuang PM
Kaushal Amin, CTO of KMS Technology, presented the top 6 technology trends for 2013:
1. Mobile apps will continue to dominate, with businesses shifting to complex mobile business applications. Native apps will remain preferred but HTML5/hybrid will gain ground.
2. Big data will continue growing exponentially from various sources such as social media and sensors. NOSQL databases are better suited than SQL for massive scalability and unstructured data.
3. Cloud computing adoption will increase, with more organizations using cloud-based disaster recovery and personal cloud storage.
4. In-memory computing allows orders of magnitude faster analytics on large datasets and will see further adoption through cloud services.
5. Action
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services over the internet. It has seen rapid growth in recent years. There are different service models like Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) depending on what capabilities are provided to the user. Cloud computing can be deployed using private, public, hybrid or community models depending on who manages the infrastructure and who has access to it. While cloud computing provides benefits like flexibility, scalability and cost savings, concerns around security, privacy and reliability remain challenges to adoption.
EVOLVING PATTERNS IN BIG DATA - NEIL AVERYBig Data Week
The document discusses evolving patterns in big data usage, including enterprise data caching using massive key-value stores, enterprise messaging pipes using Kafka, and NoSQL as a service. It also covers data lakes for centralized raw data storage and Lambda architecture for near real-time and batch processing. Current trends include growing Cassandra usage, Kafka for scalable messaging, and containerization and cloud adoption. Future areas may include graph databases, Spark evolution, and data virtualization.
Denodo DataFest 2017: Conquering the Edge with Data VirtualizationDenodo
Watch the live session on-demand: https://goo.gl/qAL3Q7
No time like the present! That's one reason why edge analytics continues to grow in value and importance. With the right analytic architecture in place, companies can not only identify opportunities at the edge, they can take appropriate actions.
Watch this Denodo DataFest 2017 session to discover:
• The growing importance of edge computing in IoT
• How data virtualization plays a critical role in enabling edge analytics
• How Denodo’s innovative customers exploit edge for a winning business model
This document discusses streaming data processing and the adoption of scalable frameworks and platforms for handling streaming or near real-time analysis and processing over the next few years. These platforms will be driven by the needs of large-scale location-aware mobile, social and sensor applications, similar to how Hadoop emerged from large-scale web applications. The document also references forecasts of over 50 billion intelligent devices by 2015 and 275 exabytes of data per day being sent across the internet by 2020, indicating challenges around data of extreme size and the need for rapid processing.
This document discusses data science, big data, and big data architecture. It begins by defining data science and describing what data scientists do, including extracting insights from both structured and unstructured data using techniques like statistics, programming, and data analysis. It then outlines the cycle of big data management and functional requirements. The document goes on to describe key aspects of big data architecture, including interfaces, redundant physical infrastructure, security, operational data sources, performance considerations, and organizing data services and tools. It provides examples of MapReduce, Hadoop, and BigTable - technologies that enabled processing and analyzing massive amounts of data.
SMAC - Social, Mobile, Analytics and Cloud - An overview Rajesh Menon
In this presentation, all the aspects of SMAC are covered in as much detail as possible. You will find some ideas worth sharing and also get attuned to Social, Mobile, Analytics and Cloud
Big data analytics (BDA) involves examining large, diverse datasets to uncover hidden patterns, correlations, trends, and insights. BDA helps organizations gain a competitive advantage by extracting insights from data to make faster, more informed decisions. It supports a 360-degree view of customers by analyzing both structured and unstructured data sources like clickstream data. Businesses can leverage techniques like machine learning, predictive analytics, and natural language processing on existing and new data sources. BDA requires close collaboration between IT, business users, and data scientists to process and analyze large datasets beyond typical storage and processing capabilities.
Insurtech, Cloud and Cybersecurity - Chartered Insurance InstituteHenrique Centieiro
Nov. 2020 presentation on Insurtech, how cloud is enabling insurtech and cybersecurity for cloud and insurtech.
Prepared by Henrique Centieiro for CII - Chartered Insurance Institute Hong Kong
Big data refers to large, complex datasets that are difficult to process using traditional database management tools. It is characterized by the 3 V's - volume, referring to the large scale of data; variety, referring to different data types; and velocity, referring to the speed at which data is generated and processed. Common sources of big data include social media, sensors, and scientific instruments. Hadoop and Spark are commonly used to process and analyze big data in distributed, parallel systems. Cloud computing provides on-demand access to computing resources and is well-suited for flexible big data applications.
Cloud computing provides on-demand access to shared computing resources that can be rapidly provisioned with minimal management effort. It has characteristics of on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Cloud computing provides advantages like cost reduction, universal access, flexibility, and potential environmental benefits. Factors driving adoption include consumerization of IT, economic pressures, globalization, workforce trends, and the rise of data and analytics. Concerns include technology maturity, lack of standards, and security concerns.
Cloud computing allows users to access computing resources like servers, storage, databases, networking, software and more over the internet. It delivers these resources as modular services through a pay-per-use model. Key characteristics include on-demand access, elastic scaling, and utility pricing. The document traces the evolution of cloud computing from mainframes to clusters to grids and discusses technologies like virtualization, web services, and utility computing that enabled cloud computing. It also covers cloud service models like IaaS, PaaS and SaaS and both benefits and challenges of cloud computing.
1) Cloud computing will drive fundamental changes in IT infrastructure, programming, application design, and business models.
2) Future cloud infrastructure will focus on power efficiency and density through technologies like ARM processors and container-based data centers. Data centers will also move nearer to power sources and colder climates.
3) Interoperability between clouds will be key, allowing for tasks like virtual machine migration and service invocation across platforms. Standardization of security practices will also be important.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services over the internet. It allows users to access resources without needing to manage physical infrastructure. Resources in the cloud are pooled and allocated dynamically based on demand. Cloud computing delivers scalability, resilience, homogeneity and low costs through virtualization, broad network access, and rapid elasticity of resources.
The document provides an overview of cloud computing and discusses issues and risks related to securing apps and data in the cloud. It defines cloud computing and its essential characteristics such as on-demand self-service, rapid elasticity, and resource pooling. The document also discusses deployment models, service models, issues around regulatory compliance, data breaches, and provides best practices for risk mitigation including conducting legal reviews of contracts and having data breach response plans.
Speaker Presention by Irena Bojanova of the University of Maryland University...Tim Harvey
Irena Bojanova, Professor & Program Director in Information and Technology Systems at the University of Maryland University College, spoke at the Federal Cloud Computing Summit on Dec. 17, 2013 at the Ronald Reagan Building in Washington, D.C.
The most trusted, proven enterprise-class Cloud:Closer than you think Uni Systems S.M.S.A.
The Big Decision – What, when, and why?
Enterprises are aware that the Cloud is changing IT, but security and performance remain a concern. Each cloud model has potential risks: reliability, adaptability, application compatibility, efficiency, scaling, lock- in, security and compliance. Companies must select an enterprise cloud solution to suit a complex mix of applications; these decisions require great care. Uni Systems’ Uni|Cloud was built to be enterprise class. The essential reason that many businesses today are using Uni Systems Cloud for their enterprise IT, is because it offers the only enterprise-class cloud solution in the Greek market, designed for mission-critical applications, coupled with application performance SLAs and security built for the enterprise, combined with cloud efficiency and consumption-based pricing/chargeback.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
2. INTRODUCTION:
• With the advent of the digital age, the amount of data being
generated, stored and shared has been on the rise. From data
warehouses, social media, webpages and blogs to audio/video
streams, all of these are sources of massive amounts of data.
• This data has huge potential, ever-increasing complexity,
insecurity and risks, and irrelevance.
3. • Big data, by definition, is a term used to
describe a variety of data -structured, semi-
structured and unstructured, which makes it a
complex data infrastructure.
• Big data includes variety, volume, velocity
and veracity
• The different types of data available on a dataset
determine variety while the rate at which data is
produced determines Velocity.
• Predictably, the size of data is called Volume.
• Veracity indicates data reliability.
INTRODUCTION: CNTD…
4. INTRODUCTION: CNTD…
• The cloud computing environment offers
development, installation and
implementation of software and data
applications ‘as a service’.
• software as a service(SaaS)
• Platform as a service(PaaS)
• Infrastructure as a service(IaaS)
• Infrastructure-as-a-service is a model that
provides computing and storage resources as
a service.
• in case of PaaS and SaaS, the cloud services
provide software platform or software itself
5. LITERATURE SURVEY:
• Traditional data management tools and data processing or data
mining techniques cannot be used for Big Data Analytics for the
large volume and complexity of the datasets that it includes.
• Conventional business intelligence applications make use of
methods, which are based on traditional analytics methods and
techniques and make use of OLAP, BPM, Mining and database
systems like RDBMS.
• One of the most popular models used for data processing on
cluster of computers is MapReduce.
• Hadoop is simply an open-source implementation of the
MapReduce framework, which was originally created as a
distributed file system.
6. PROBLEM STATEMENT:
• In order to move beyond the existing techniques and strategies
used for machine learning and data analytics, some challenges
need to be overcome. NESSI identifies the following
requirements as critical.
• In order to select an adequate method or design, a solid scientific
foundation needs to be developed.
• New efficient and scalable algorithms need to be developed.
• For proper implementation of devised solutions, appropriate
development skills and technological platforms must be identified and
developed.
• Lastly, the business value of the solutions must be explored just as
much as the data structure and its usability.
7. PROBLEM STATEMENT:CNTD…
• This section, describes two example applications where large
scale data management over cloud is used. These are specific
use-case examples in telecom and finance.
• In the telecom domain, massive amount of call detail records
can be processed to generate near real-time network usage
information.
• In finance domain it can be describe the fraud detection
application.
8. DESIGN, IMPLEMENTATION AND RESULT
ANALYSIS DETAILS:
1.Dashboard for CDR Processing:
• Telecom operators are interested in building a dashboard that would
allow the analysts and architects to understand the traffic flowing
through the network along various dimensions of interest.
• The traffic is captured using Call Detail Records (CDRs) whose volume
runs into a terabyte per day.
• CDR is a structured stream generated by the telecom switches to
summarize various aspects of individual services like voice, SMS, MMS,
etc.
• The dashboard include determining the cell site used most for each
customer, identifying whether users are mostly making calls within cell
site calls, and for cell sites in rural areas identifying the source of traffic
i.e. local versus routed calls.
9. DESIGN, IMPLEMENTATION AND RESULT
ANALYSIS DETAILS:
1.Dashboard for CDR Processing: CNTD…
• Given the huge and ever growing customer base and large call volumes,
solutions using traditional warehouse will not be able to keep-up with
the rates required for effective operation.
• The need is to process the CDRs in near real-time, mediate them (i.e.,
collect CDRs from individual switches, stitch, validate, filter, and
normalize them), and create various indices which can be exploited by
dashboard among other applications.
• An IBM Stream Processing Language (SPL) based system leads to
mediating 6 billion CDRs per day.
• CDRs can be loaded periodically over cloud data management solution.
As cloud provides flexible storage, depending on traffic one can decide
on the storage required.
10. DESIGN, IMPLEMENTATION AND RESULT
ANALYSIS DETAILS:
2. Credit Card Fraud Detection:
• More than one-tenth of world’s population is shopping online. Credit
card is the most popular mode of online payments. As the number of
credit card transactions rise, the opportunities for attackers to steal
credit card details and commit fraud are also increasing.
• As the attacker only needs to know some details about the card (card
number, expiration date, etc.), the only way to detect online credit card
fraud is to analyze the spending patterns and detect any inconsistency
with respect to usual spending patterns.
• The companies keep tabs on the geographical locations where the credit
card transactions are made—if the area is far from the card holder’s area
of residence, or if two transactions from the same credit card are made
in two very distant areas within a relatively short timeframe, — then the
transactions are potentially fraud transactions.
11. DESIGN, IMPLEMENTATION AND RESULT
ANALYSIS DETAILS:
2. Credit Card Fraud Detection:CNTD…
• Various data mining algorithms are used to detect patterns within the
transaction data. Detecting these patterns requires the analysis of large
amount of data.
• Using tuples of the transactions, one can find the distance between
geographic locations of two consecutive transactions, amount of these
transactions, etc. By these parameters, one can find the potential
fraudulent transactions. Further data mining, based on a particular
user’s spending profile can be used to increase the confidence whether
the transaction is indeed fraudulent.
12. DESIGN, IMPLEMENTATION AND RESULT
ANALYSIS DETAILS:
2. Credit Card Fraud Detection:CNTD…
• As number of credit card transactions is huge and the kind of processing
required is not a typical relational processing (hence, warehouses are not
optimized to do such processing), one can use Hadoop based solution
for this purpose as depicted.
• Using Hadoop one can create customer profile as well as creating
matrices of consecutive transactions to decide whether a particular
transaction is a fraud transaction. As one needs to find the fraud with-in
some specified time, stream processing can help.
• By employing massive resources for analyzing potentially fraud
transactions one can meet the response time guarantees.
13. DESIGN, IMPLEMENTATION AND RESULT
ANALYSIS DETAILS:
3. Result Analysis:
• Several open source data mining techniques, resources
and tools exist. Some of these include R, Gate, Rapid-
Miner and Weka, in addition to many others.
• Cloud-based big data analytics solutions must provide
a provision for the availability of these affordable data
analytics on the cloud so that cost-effective and
efficient services can be provided.
• The fundamental reason why cloud-based analytics are
such a big thing is their easy accessibility, cost-
effectiveness and ease of setting up and testing.
14.
15. CONCLUSION AND FUTURE RESEARCH
DIRECTION:
• This is an age of big data and the emergence of this field of
study has attracted the attention of many practitioners and
researchers.
• Considering the rate at which data is being created in the
digital world, big data analytics and analysis have become all
the more relevant.
• The cloud infrastructure suffices the storage and computing
requirements of data analytics algorithms. On the other hand,
open issues like security, privacy and the lack of ownership and
control exist.
• Research studies in the area of cloud-based big data analytics