Big data analysis has become much popular in the present day scenario and the manipulation of
big data has gained the keen attention of researchers in the field of data analytics. Analysis of
big data is currently considered as an integral part of many computational and statistical
departments. As a result, novel approaches in data analysis are evolving on a daily basis.
Thousands of transaction requests are handled and processed everyday by different websites
associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog
analysis comes to play a crucial role in such situations where Hadoop can be suggested as an
efficient solution for processing the Netflow data collected from switches as well as website
access-logs during fixed intervals.
A comparative survey based on processing network traffic data using hadoop pi...ijcses
Big data analysis has now become an integral part of many computational and statistical departments.
Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data
manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every
minute by different websites related to e-commerce, shopping carts and online banking. Here comes the
need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can
efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Big data is a popular term used to describe the large volume of data which includes structured, semi-structured and unstructured
data. Now-a-days, unstructured data is growing in an explosive speed with the development of Internet and social networks like Twitter,Facebook
& Yahoo etc., In order to process such colossal of data a software is required that does this efficiently and this is where Hadoop steps in. Hadoop
has become one of the most used frameworks when dealing with big data. It is used to analyze and process big data. In this paper, Apache Flume
is configured and integrated with spark streaming for streaming the data from twitter application. The streamed data is stored into Apache
Cassandra. After retrieving the data, the data is going to be analyzed by using the concept of Apache Zeppelin. The result will be displayed on
Dashboard and the dashboard result is also going to be analyzed and validating using JSON
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
A comparative survey based on processing network traffic data using hadoop pi...ijcses
Big data analysis has now become an integral part of many computational and statistical departments.
Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data
manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every
minute by different websites related to e-commerce, shopping carts and online banking. Here comes the
need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can
efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Big data is a popular term used to describe the large volume of data which includes structured, semi-structured and unstructured
data. Now-a-days, unstructured data is growing in an explosive speed with the development of Internet and social networks like Twitter,Facebook
& Yahoo etc., In order to process such colossal of data a software is required that does this efficiently and this is where Hadoop steps in. Hadoop
has become one of the most used frameworks when dealing with big data. It is used to analyze and process big data. In this paper, Apache Flume
is configured and integrated with spark streaming for streaming the data from twitter application. The streamed data is stored into Apache
Cassandra. After retrieving the data, the data is going to be analyzed by using the concept of Apache Zeppelin. The result will be displayed on
Dashboard and the dashboard result is also going to be analyzed and validating using JSON
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Have a lot of data? Using or considering using Apache HBase (part of the Hadoop family) to store your data? Want to have your cake and eat it too? Phoenix is an open source project put out by Salesforce. Join us to learn how you can continue to use SQL, but get the raw speed of native HBase usage through Phoenix.
Analyzing Big data in R and Scala using Apache Spark 17-7-19Ahmed Elsayed
We can make a data mining to get the prediction about the future data, which is mined from an old data especially Big data using a machine learning algorithms based on two clusters. One is the intrinsic for managing the file system of Big data, which is called Hadoop. The other is essentially to make fast analysis of Big data which is called Apache Spark. In order to achieve this purpose we will use R based on Rstudio or Scala based on Zeppelin.
Hadoop add-on API for Advanced Content Based Search & Retrievaliosrjce
Unstructured data like doc, pdf is lengthy to search and filter for desired information. We need to go
through every file manually for finding information. It is very time consuming and frustrating. We can use
features of big data management system like Hadoop to organize unstructured data dynamically and return
desired information. Hadoop provides features like Map Reduce, HDFS, HBase to filter data as per user input.
Finally we can develop Hadoop Addon for content search and filtering on unstructured data.
Computer malfunctions can range from a minor setting that is incorrect, to spyware, viruses, and as far as replacing hardware and an entire operating system.
While computer hardware configurations vary widely, a "Computer OEM & Repair" technician will work with five general categories of hardware; desktop computers, laptops, servers, computer clusters and smartphones / mobile computing. Technicians also work with and occasionally repair a range of peripherals, including input devices (like keyboards, mice, and scanners), output devices (like displays, printers, and speakers), and data storage devices such as internal and external hard drives and disk arrays. Technicians involved in system administration might also work with networking hardware, including routers, switches, fiber optics, and wireless networks. OEM= Original Equipment Manufacturer.
Asserting that Big Data is vital to business is an understatement. Organizations have generated more and more data for years, but struggle to use it effectively. Clearly Big Data has more important uses than ensuring compliance with regulatory requirements. In addition, data is being generated with greater velocity, due to the advent of new pervasive devices (e.g., smartphones, tablets, etc.), social Web sites (e.g., Facebook, Twitter, LinkedIn, etc.) and other sources like GPS, Google Maps, heat/pressure sensors, etc.
Have a lot of data? Using or considering using Apache HBase (part of the Hadoop family) to store your data? Want to have your cake and eat it too? Phoenix is an open source project put out by Salesforce. Join us to learn how you can continue to use SQL, but get the raw speed of native HBase usage through Phoenix.
Analyzing Big data in R and Scala using Apache Spark 17-7-19Ahmed Elsayed
We can make a data mining to get the prediction about the future data, which is mined from an old data especially Big data using a machine learning algorithms based on two clusters. One is the intrinsic for managing the file system of Big data, which is called Hadoop. The other is essentially to make fast analysis of Big data which is called Apache Spark. In order to achieve this purpose we will use R based on Rstudio or Scala based on Zeppelin.
Hadoop add-on API for Advanced Content Based Search & Retrievaliosrjce
Unstructured data like doc, pdf is lengthy to search and filter for desired information. We need to go
through every file manually for finding information. It is very time consuming and frustrating. We can use
features of big data management system like Hadoop to organize unstructured data dynamically and return
desired information. Hadoop provides features like Map Reduce, HDFS, HBase to filter data as per user input.
Finally we can develop Hadoop Addon for content search and filtering on unstructured data.
Computer malfunctions can range from a minor setting that is incorrect, to spyware, viruses, and as far as replacing hardware and an entire operating system.
While computer hardware configurations vary widely, a "Computer OEM & Repair" technician will work with five general categories of hardware; desktop computers, laptops, servers, computer clusters and smartphones / mobile computing. Technicians also work with and occasionally repair a range of peripherals, including input devices (like keyboards, mice, and scanners), output devices (like displays, printers, and speakers), and data storage devices such as internal and external hard drives and disk arrays. Technicians involved in system administration might also work with networking hardware, including routers, switches, fiber optics, and wireless networks. OEM= Original Equipment Manufacturer.
The impact of innovation on travel and tourism industries (World Travel Marke...Brian Solis
From the impact of Pokemon Go on Silicon Valley to artificial intelligence, futurist Brian Solis talks to Mathew Parsons of World Travel Market about the future of travel, tourism and hospitality.
The Six Highest Performing B2B Blog Post FormatsBarry Feldman
If your B2B blogging goals include earning social media shares and backlinks to boost your search rankings, this infographic lists the size best approaches.
Each technological age has been marked by a shift in how the industrial platform enables companies to rethink their business processes and create wealth. In the talk I argue that we are limiting our view of what this next industrial/digital age can offer because of how we read, measure and through that perceive the world (how we cherry pick data). Companies are locked in metrics and quantitative measures, data that can fit into a spreadsheet. And by that they see the digital transformation merely as an efficiency tool to the fossil fuel age. But we need to stretch further…
NETWORK TRAFFIC ANALYSIS: HADOOP PIG VS TYPICAL MAPREDUCEcscpconf
Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of
big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis.
Thousands of transaction requests are handled and processed every day by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog
analysis comes to play a crucial role in such situations where Hadoop can be suggested as an efficient solution for processing the Netflow data collected from switches as well as website
access-logs during fixed intervals.
A Comparative Survey Based on Processing Network Traffic Data Using Hadoop Pi...IJCSES Journal
Big data analysis has now become an integral part of many computational and statistical departments. Analysis of peta-byte scale of data is having an enhanced importance in the present day scenario. Big data manipulation is now considered as a key area of research in the field of data analytics and novel
techniques are being evolved day by day. Thousands of transaction requests are being processed in every minute by different websites related to e-commerce, shopping carts and online banking. Here comes the need of network traffic and weblog analysis for which Hadoop comes as a suggested solution. It can efficiently process the Netflow data collected from routers, switches or even from website access logs at
fixed intervals.
Open source grid middleware packages – Globus Toolkit (GT4) Architecture , Configuration – Usage of Globus – Main components and Programming model - Introduction to Hadoop Framework - Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write.
HMR LOG ANALYZER: ANALYZE WEB APPLICATION LOGS OVER HADOOP MAPREDUCEijujournal
In today’s Internet world, log file analysis is becoming a necessary task for analyzing the customer’s
behavior in order to improve advertising and sales as well as for datasets like environment, medical,
banking system it is important to analyze the log data to get required knowledge from it. Web mining is the
process of discovering the knowledge from the web data. Log files are getting generated very fast at the
rate of 1-10 Mb/s per machine, a single data center can generate tens of terabytes of log data in a day.
These datasets are huge. In order to analyze such large datasets we need parallel processing system and
reliable data storage mechanism. Virtual database system is an effective solution for integrating the data
but it becomes inefficient for large datasets. The Hadoop framework provides reliable data storage by
Hadoop Distributed File System and MapReduce programming model which is a parallel processing
system for large datasets. Hadoop distributed file system breaks up input data and sends fractions of the
original data to several machines in hadoop cluster to hold blocks of data. This mechanism helps to
process log data in parallel using all the machines in the hadoop cluster and computes result efficiently.
The dominant approach provided by hadoop to “Store first query later”, loads the data to the Hadoop
Distributed File System and then executes queries written in Pig Latin. This approach reduces the response
time as well as the load on to the end system. This paper proposes a log analysis system using Hadoop
MapReduce which will provide accurate results in minimum response time.
Performance evaluation of Map-reduce jar pig hive and spark with machine lear...IJECEIAES
Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.
Performance evaluation and estimation model using regression method for hadoo...redpel dot com
Performance evaluation and estimation model using regression method for hadoop word count.
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Enablers, Platforms, & Early adopters for internet of things. How hadoop helps in enabling the technology to process data from sensors? What are the limitations in using Hadoop for internet of things?
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
2. 16
Computer Science & Information Technology (CS & IT)
Further customizations can also be done on NetFlow implementation as per the requirement in
different scenarios. It can even allow the observation and recording of IP packets on the egress
interface if accurately configured.
NetFlow data from dedicated probes can be efficiently utilized for the observation of critical
links, whereas the data from routers can provide a network-wide view of the traffic that which
will be of great use for capacity planning, accounting, performance monitoring, and security.
This paper depicts a survey regarding the network flow data analysis done using Apache Hadoop
MapReduce framework and a comparative study of typical MapReduce against Apache Pig, a
platform for analyzing large data sets and provides ease of programming, optimizing
opportunities and enhanced extensibility.
2. NETFLOW ANALYSIS USING TYPICAL HADOOP MAPREDUCE
FRAMEWORK
Hadoop can be considered as an efficient mechanism for big data manipulation in distributed
environment. Hadoop was developed by Apache foundation and is well-known for its scalability
and efficiency. Hadoop works on a computational paradigm called MapReduce which is
characterized by a Map and a Reduce phase. In Map phase, the computation is divided into many
fragments and is distributed among the cluster nodes. The individual results evolved at the end of
the Map phase is combined and reduced to a single output in the Reduce phase, thereby producing
the result in a short span of time.
Hadoop will store the data in its distributed file system popularly known as HDFS and will make
it easily available to the users when requested. HDFS is greatly advantageous in its scalability and
portability aspects. It achieves reliability by the replication of data across multiple nodes in the
cluster, thereby eliminating the need for RAID storage on the cluster nodes. High availability is
also incorporated in Hadoop cluster systems by means of a backup provision for the master
node[name node] known as secondary namenode. The major limitations of HDFS are the inability
to be mounted directly by an existing operating system and the inconvenience of moving data in
and out of the HDFS before and after the execution of a job.
Once Hadoop is deployed on a cluster, instances of these processes will be started – Namenode,
Datanode, Jobtracker, Tasktracker. Hadoop can process large amount of data such as website
logs, network traffic logs obtained from switches etc. The namenode, otherwise known as master
node is the main component of the HDFS which stores the directory tree of the file system and
keeps track of the data during execution. The datanode stores the data in HDFS as suggested by
its name. The jobtracker delegates the MapReduce tasks to different cluster nodes and the
tasktracker spawns and executes the job on the member nodes.
The Netflow analysis requires sufficient network flow data collected at a particular time or at
regular intervals for processing. Upon analyzing the incoming and outgoing network traffic
corresponding to various hosts, required precautions can be taken regarding server security,
firewall setup, packet filtering etc and here, the significance of network flow analysis cannot be
kept aside. Flow data can be collected from switches or routers using router-console commands
and are filtered on the basis of the required parameters such as IP address, Port numbers etc.
Similarly, every website will generate corresponding logs and such files can be collected from the
web-server for further analysis. Website access logs will contain the details of URLs accessed, the
time of access, number of hits etc which are unavoidable for a website manager for the proactive
detection and encounter of problems like Denial of Service attacks and IP address spoofing.
3. Computer Science & Information Technology (CS & IT)
17
The log files generated by a router or web-server every minute will accumulate to form a largesized file which needs a great deal of time to get processed. In such a situation, the advantage of
Hadoop MapReduce paradigm can be made use of. The big data is fed as input for a MapReduce
algorithm which provides an efficiently processed output file in return. Every map function
transforms a data into certain number of key/value pairs which later sorts them according to the
key values whereas a reduce function merges the values with similar key values into a single
result.
One of the main feature of Hadoop MapReduce is its coding complexity which is possible only by
programmers with highly developed skills. This programming model is highly restrictive and
Hadoop cluster management is also not much easy while considering aspects like debugging, log
collection, software distribution etc. In short, the effort to be put in MapReduce programming
restricts its acceptability to a remarkable extent. Here comes the need for a platform which can
provide ease of programming as well as enhanced extensibility and Apache Pig reveals itself as a
novel approach in data analytics. The key features and applications of Apache Pig deployed over
Hadoop framework is described in Section 3.
3. USING APACHE PIG TO PROCESS NETWORK FLOW DATA
Apache Pig is a platform that supports substantial parallelization which enables the processing of
huge data sets. The structure of Pig can be described in two layers – An infrastructure layer with
an in-built compiler that can produce MapReduce programs and a language layer with a textprocessing language called “Pig Latin”. Pig makes data analysis and programming easier and
understandable to novices in Hadoop framework using Pig Latin which can be considered as a
parallel data flow language. The pig setup has also got provisions for further optimizations and
user defined functionalities. Pig Latin queries are compiled into MapReduce jobs and are
executed in distributed Hadoop cluster environments. Pig Latin looks similar to SQL[Structured
Query Language] but is designed for Hadoop data processing environment just like SQL for
RDBMS environment. Another member of Hadoop ecosystem known as Hive is a query language
like SQL and needs data to be loaded into tables before processing. Pig Latin can work on
schema-less or inconsistent environments and can operate on the available data as soon as it is
loaded into the HDFS. Pig closely resembles scripting languages like Perl and Python in certain
aspects such as the flexibility in syntax and dynamic variables definitions. Pig Latin is efficient
enough to be considered as the native parallel processing language for distributed systems such as
Hadoop.
Figure 1. Data processing model in PIG
4. 18
Computer Science & Information Technology (CS & IT)
Pig is gaining popularity in different zones of computational and statistical departments. It is
widely used in processing weblogs and wiping off corrupted data from records. Pig can build
behavior prediction models based on the user interactions with a website and this feature can be
applied in displaying ads and news stories that keeps the user entertained. It also emphasizes on
an iterative processing model which can keep track of every new updates by combining the
behavioral model with the user data. This feature is largely used in social networking sites like
Facebook and micro-blogging sites like Twitter. When it comes to small data or scanning
multiple records in random order, pig cannot be considered as effective as MapReduce. The
aforementioned data processing model used by Pig is depicted in Figure 1.
In this paper, we are exploring the horizons of Pig Latin for processing the netflow obtained from
routers. The proposed method for implementing Pig for processing netflow data is described in
Section 4.
4. IMPLEMENTATION ASPECTS
Here we present the NetFlow analysis using Hadoop, which can manage large volume of data,
employ parallel processing and come up with required output in no time. A map-reduce algorithm
need to be deployed using Hadoop to get the result and writing such map-reduce programs for
analyzing huge flow data is a time consuming task. Here the Apache-Pig will help us to save a
good deal of time.
We need to analyze traffic at a particular time and Pig plays an important role in this regard. For
data analysis, we create buckets with pairs of (SrcIF, SrcIPaddress), (DstIF,DstIPaddress),
(Protocal,FlowperSec). Later, they are joined and categorized as SrcIF and FlowperSec;
SrcIPaddress and FlowperSec. Netflow data is collected from a busy Cisco switch using nfdump.
The output contains three basic sections-IP Packet section, Protocol Flow section and Source
Destination packets section.
Figure 2. Netflow Analysis using Hadoop Pig
Pig needs the netflow datasets in arranged format which is then converted to SQL-like schema.
Hence the three sections are created as three different schemas. A sample Pig Latin code is given
below:
Protocol = LOAD ‘NetFlow-Data1’ AS (protocol:chararray, flow:int, …)
Source = LOAD ‘NetFlow-Data2’ AS (SrcIF:chararray, SrcIPAddress:chararray, …)
Destination = LOAD ‘NetFlow-Data3’ AS (DstIF:chararray, DstIPaddress, …)
As shown in Figure 2, the network flow is deduced into schemas by Pig. Then these schemas are
combined for analysis and the traffic flow per node is the resultant output.
5. Computer Science & Information Technology (CS & IT)
19
5. ADVANTAGES OF USING HADOOP PIG OVER MAPREDUCE
The member of Hadoop ecosystem popularly known as Pig can be considered as a procedural
version of SQL. The most attractive feature and advantage of Pig is its simplicity. It doesn't
demand highly skilled Java professionals for implementation. The flexibility in syntax and
dynamic variable allocation makes it much more user-friendly and provisions for user-defined
functions add extensibility for the framework. The ability of Pig to work with un-structured data
greatly accounts to parallel data processing. The great difference comes when the computational
complexity is considered. Typical map-reduce programs will have large number of java code lines
whereas the task can be accomplished using Pig Latin in very fewer lines of code. Huge network
flow data input files can be processed with writing simple programs in Pig Latin which closely
resembles scripting languages like Perl/Python. In other words, the computational complexity of
Pig Latin is lesser than Hadoop MapReduce programs written in Java which made Pig suitable for
the implementation of this project. The procedure followed for arriving at such a conclusion is
described in Section 6.
6. EXPERIMENTS AND RESULTS
Hadoop Pig was selected as the programming language for Netflow analysis after conducting a
short test on a sample input file.
6.1 Experimental Setup
Apache Hadoop was initially installed and configured on a stand-alone node and Pig was
deployed on top of this system. Key-based authentication was setup on the machine as Hadoop
prompts for passwords while performing operations. Hadoop instances such as namenode,
datanode, jobtracker and tasktracker are started before running Pig and it shows a “grunt” console
upon starting. The grunt console is the command prompt specific to Pig and is used for data and
file system manipulation. Pig can work both in local mode and MapReduce mode which can be
selected as per the user requirement.
A sample Hadoop MapReduce word-count program written in Java was used for the comparison.
The program used to process a sample input file of enough size had one hundred thirty lines of
code and the time taken for execution was noted. The same operation was coded using Pig Latin
which contained only five lines of code and corresponding running time was also noted for
framing the results.
6.2. Experiment Results
At a particular instant, the size of input file is kept constant for MapReduce and Pig whereas the
lines of code are more for the former and less for the latter. For a good comparison, the input file
size was taken in the range of 13 kb to 208 kb and the corresponding time is recorded as in Figure
3.
6. 20
Computer Science & Information Technology (CS & IT)
Figure 3. Hadoop Pig vs. MapReduce
From this we can formulate an expression as follows: As the input file size increases in the
multiples of x, the execution time for typical MapReduce also increases proportionally whereas
Hadoop Pig can maintain a constant time at least for x upto 5 times. Graphs are plotted between
both and are given as Figure 4 and Figure 5.
Figure 4. Input file-size vs. execution time for MapReduce
Figure 5. Input file-size vs. execution time for Pig
6. CONCLUSION
In this paper, a detailed survey regarding the wide scope of Apache Pig platform is conducted.
The key features of the network flows are extracted for data analysis using Hadoop Pig which was
tested and proved to be advantageous in this aspect with a very low computational complexity.
Network flow capture is to be done with the help of easily available open-source tools. Once the
Pig-based flow analyzer is implemented, traffic flow analysis can be done with increased
convenience and easiness even without the help of highly developed programming skills. It
greatly reduces the time consumed for researching on the complex programming constructs and
7. Computer Science & Information Technology (CS & IT)
21
control loops for implementing a typical MapReduce paradigm using Java, but still enjoying the
advantages of Map/Reduce task division method with the help of the layered architecture and inbuilt compilers of Apache Pig.
ACKNOWLEDGEMENT
We are greatly indebted to the college management and the faculty members for providing
necessary facilities and hardware along with timely guidance and suggestions for implementing
this work.
REFERENCES
http://pig.apache.org/
http://orzota.com/blog/pig-for-beginners/
http://jugnu-life.blogspot.com/2012/03/installing-pig-apache-hadoop-pig.html
http://www.hadoop.apache.org
http://www.ibm.com/developerworks/library/l-hadoop-1/
http://www.ibm.com/developerworks/linux/library/l-apachepigdataquery/
http://ankitasblogger.blogspot.in/2011/01/hadoop-cluster-setup.html
http://icanhadoop.blogspot.in/2012/09/configuring-hadoop-is-very-if-you-just.html
http://developer.yahoo.com/hadoop/tutorial/module7.html
https://cyberciti.biz
http://www.quuxlabs.com/tutorials/running-hadoop-on-ubuntu-linux-multi-node-cluster/
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[13] http://www.cisco.com/en/US/docs/switches/lan/catalyst6500/ios/12.2SX/configuration/guide/netflow.h
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AUTHORS
Anjali P P is a MTech student of Information Technology Department in Rajagiri
college of engineering, Kochi, under MG University. She has received BTech degree in
Computer Science and Engineering from Cochin University of Science and
Technology. Her research interest includes Hadoop and Big data analysis.
Binu A is an Assistant Professor in Information Technology Department of Rajagiri
college of engineering, Kochi, under MG University.