This document discusses the need for a new paradigm in big data analytics using algorithms. It begins by describing the limitations of traditional analytics approaches like statistical analysis, data mining, visualization and business intelligence tools when applied to big data. These approaches are query-based and labor intensive. Emerging big data tools like Hadoop and in-memory databases help with storage and queries but do not provide automated insights. The document argues that the new paradigm should focus on algorithms that can automatically surface insights from data in seconds, replacing the need for data analysts to manually query databases. This represents a shift from humans digging for insights to algorithms surfacing insights for humans to evaluate.
The document discusses the field of data mining. It begins by defining data mining and describing its branches including classification, clustering, and association rule mining. It then discusses the growth of data in various domains that has created opportunities for data mining applications. The document outlines the history and development of data mining from empirical science to computational science to data science. It provides examples of data mining applications in various domains like healthcare, energy, climate science, and agriculture. Finally, it discusses future directions and challenges for the field of data mining.
This document discusses big data and its applications in various industries. It begins by defining big data and its key characteristics of volume, velocity, variety and veracity. It then discusses how big data can be used for log analytics, fraud detection, social media analysis, risk modeling and other applications. The document also outlines some of the major challenges faced in the banking and financial services industry, including increasing competition, regulatory pressures, security issues, and adapting to digital shifts. It concludes by noting how big data analytics can help eCommerce businesses make fact-based, quantitative decisions to gain competitive advantages and optimize goals.
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
Australia bureau of statistics some initiatives on big data - 23 july 2014noviari sugianto
This document discusses the opportunities and challenges of using Big Data in official statistics. It outlines several potential applications of Big Data, including sample frame creation, full or partial data substitution, imputation, and generating new insights. However, the decision to use a Big Data source should be based on a strong business case and cost-benefit analysis. The document provides an example cost-benefit analysis for using satellite imagery to replace agricultural survey data. It also emphasizes that Big Data sources must meet validity criteria for statistical inferences.
Data mining involves extracting useful patterns from large amounts of data. It involves defining a problem, preparing data, exploring data, building models, and deploying models. Some common applications of data mining include analyzing customer purchasing patterns, detecting fraud, predicting disease outbreaks, and analyzing financial/business data. While data warehousing provides insights into past trends, data mining can discover hidden patterns to predict future trends and behaviors from data.
A study on web analytics with reference to select sports websitesBhanu Prakash
This document is a project report submitted by Y. Bhanu Prakash to GITAM Institute of Management in partial fulfillment of the degree of Bachelor of Business Administration in Business Analytics. The report is on the topic of web analytics with reference to select sports websites. It includes declarations by the student and certification by the guide, as well as acknowledgements. The report will consist of 5 chapters - an introduction to analytics, a profile of Alexa.com, methodology, analysis and interpretation of data, and observations and conclusions.
This report examines the rise of big data and analytics used to analyze large volumes of data. It is based on a survey of 302 BI professionals and interviews. Most organizations have implemented analytical platforms to help analyze growing amounts of structured data. New technologies also analyze semi-structured data like web logs and machine data. While reports and dashboards serve casual users, more advanced analytics are needed for power users to fully leverage big data.
The document discusses the field of data mining. It begins by defining data mining and describing its branches including classification, clustering, and association rule mining. It then discusses the growth of data in various domains that has created opportunities for data mining applications. The document outlines the history and development of data mining from empirical science to computational science to data science. It provides examples of data mining applications in various domains like healthcare, energy, climate science, and agriculture. Finally, it discusses future directions and challenges for the field of data mining.
This document discusses big data and its applications in various industries. It begins by defining big data and its key characteristics of volume, velocity, variety and veracity. It then discusses how big data can be used for log analytics, fraud detection, social media analysis, risk modeling and other applications. The document also outlines some of the major challenges faced in the banking and financial services industry, including increasing competition, regulatory pressures, security issues, and adapting to digital shifts. It concludes by noting how big data analytics can help eCommerce businesses make fact-based, quantitative decisions to gain competitive advantages and optimize goals.
Data Scientist has been regarded as the sexiest job of the twenty first century. As data in every industry keeps growing the need to organize, explore, analyze, predict and summarize is insatiable. Data Science is creating new paradigms in data driven business decisions. As the field is emerging out of its infancy a wide range of skill sets are becoming an integral part of being a Data Scientist. In this talk I will discuss the different driven roles and the expertise required to be successful in them. I will highlight some of the unique challenges and rewards of working in a young and dynamic field.
Australia bureau of statistics some initiatives on big data - 23 july 2014noviari sugianto
This document discusses the opportunities and challenges of using Big Data in official statistics. It outlines several potential applications of Big Data, including sample frame creation, full or partial data substitution, imputation, and generating new insights. However, the decision to use a Big Data source should be based on a strong business case and cost-benefit analysis. The document provides an example cost-benefit analysis for using satellite imagery to replace agricultural survey data. It also emphasizes that Big Data sources must meet validity criteria for statistical inferences.
Data mining involves extracting useful patterns from large amounts of data. It involves defining a problem, preparing data, exploring data, building models, and deploying models. Some common applications of data mining include analyzing customer purchasing patterns, detecting fraud, predicting disease outbreaks, and analyzing financial/business data. While data warehousing provides insights into past trends, data mining can discover hidden patterns to predict future trends and behaviors from data.
A study on web analytics with reference to select sports websitesBhanu Prakash
This document is a project report submitted by Y. Bhanu Prakash to GITAM Institute of Management in partial fulfillment of the degree of Bachelor of Business Administration in Business Analytics. The report is on the topic of web analytics with reference to select sports websites. It includes declarations by the student and certification by the guide, as well as acknowledgements. The report will consist of 5 chapters - an introduction to analytics, a profile of Alexa.com, methodology, analysis and interpretation of data, and observations and conclusions.
This report examines the rise of big data and analytics used to analyze large volumes of data. It is based on a survey of 302 BI professionals and interviews. Most organizations have implemented analytical platforms to help analyze growing amounts of structured data. New technologies also analyze semi-structured data like web logs and machine data. While reports and dashboards serve casual users, more advanced analytics are needed for power users to fully leverage big data.
The document discusses big data analytics. It begins by defining big data as large datasets that are difficult to capture, store, manage and analyze using traditional database management tools. It notes that big data is characterized by the three V's - volume, variety and velocity. The document then covers topics such as unstructured data, trends in data storage, and examples of big data in industries like digital marketing, finance and healthcare.
This document discusses data analytics and related concepts. It defines data and information, explaining that data becomes information when it is organized and analyzed to be useful. It then discusses how data is everywhere and the value of data analysis skills. The rest of the document outlines the methodology of data analytics, including data collection, management, cleaning, exploratory analysis, modeling, mining, and visualization. It provides examples of how data analytics is used in healthcare and travel to optimize processes and customer experiences.
Applications of Big Data Analytics in BusinessesT.S. Lim
The document discusses big data and big data analytics. It begins with definitions of big data from various sources that emphasize the large volumes of structured and unstructured data. It then discusses key aspects of big data including the three Vs of volume, variety, and velocity. The document also provides examples of big data applications in various industries. It explains common analytical methods used in big data including linear regression, decision trees, and neural networks. Finally, it discusses popular tools and frameworks for big data analytics.
Reporting involves building, organizing, and summarizing raw data into reports that raise questions about what is happening in the business. Analysis transforms this information into insights by interpreting the data at a deeper level to answer questions and provide actionable recommendations about why things are happening and what can be done. Both reporting and analysis play important roles in driving actions that create greater value for organizations, with reporting providing information to identify issues and analysis providing explanations and solutions to help bridge the gap between data and actions.
This document summarizes a survey on data mining. It discusses how data mining helps extract useful business information from large databases and build predictive models. Commonly used data mining techniques are discussed, including artificial neural networks, decision trees, genetic algorithms, and nearest neighbor methods. An ideal data mining architecture is proposed that fully integrates data mining tools with a data warehouse and OLAP server. Examples of profitable data mining applications are provided in industries such as pharmaceuticals, credit cards, transportation, and consumer goods. The document concludes that while data mining is still developing, it has wide applications across domains to leverage knowledge in data warehouses and improve customer relationships.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
This document provides an introduction to big data and analytics. It discusses the topics of data processing, big data, data science, and analytics and optimization. It then provides a historic perspective on data and describes the data processing lifecycle. It discusses aspects of data including metadata and master data. It also discusses different data scenarios and the processing of data in serial versus parallel formats. Finally, it discusses the skills needed for a data scientist including business and domain knowledge, statistical modeling, technology stacks, and more.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
The document discusses challenges in analytics for big data. It notes that big data refers to data that exceeds the capabilities of conventional algorithms and techniques to derive useful value. Some key challenges discussed include handling the large volume, high velocity, and variety of data types from different sources. Additional challenges include scalability for hierarchical and temporal data, representing uncertainty, and making the results understandable to users. The document advocates for distributed analytics from the edge to the cloud to help address issues of scale.
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
BIAM 410 Final Paper - Beyond the Buzzwords: Big Data, Machine Learning, What...Thomas Rones
This document discusses big data, machine learning, and NoSQL databases. It defines big data as referring to large or complex datasets that require techniques like NoSQL, MapReduce, and machine learning for analysis. Machine learning is made possible by large amounts of publicly available unstructured data and advances in computing. NoSQL databases are used to store big data because they allow for more flexibility than structured SQL databases for applications that need to scale.
A Model Design of Big Data Processing using HACE TheoremAnthonyOtuonye
This document presents a model for big data processing using the HACE theorem. It proposes a three-tier data mining structure to provide accurate, real-time social feedback for understanding society. The model adopts Hadoop's MapReduce for big data mining and uses k-means and Naive Bayes algorithms for clustering and classification. The goal is to address challenges of big data and assist governments and businesses in using big data technology.
An introduction to Data Mining by Kurt ThearlingPim Piepers
An Introduction to Data Mining Discovering hidden value in your data warehouse By Kurt Thearling Overview Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?" This white paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today’s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.
The document provides an overview of data science. It defines data science as a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, and deep learning. It explains that data science uses both traditional structured data stored in databases as well as big data from various sources. The document also describes how data scientists preprocess and analyze data to gain insights into past behaviors using business intelligence and then make predictions about future behaviors.
The document discusses big data analytics. It begins by defining big data as large datasets that are difficult to capture, store, manage and analyze using traditional database management tools. It notes that big data is characterized by the three V's - volume, variety and velocity. The document then covers topics such as unstructured data, trends in data storage, and examples of big data in industries like digital marketing, finance and healthcare.
This document discusses data analytics and related concepts. It defines data and information, explaining that data becomes information when it is organized and analyzed to be useful. It then discusses how data is everywhere and the value of data analysis skills. The rest of the document outlines the methodology of data analytics, including data collection, management, cleaning, exploratory analysis, modeling, mining, and visualization. It provides examples of how data analytics is used in healthcare and travel to optimize processes and customer experiences.
Applications of Big Data Analytics in BusinessesT.S. Lim
The document discusses big data and big data analytics. It begins with definitions of big data from various sources that emphasize the large volumes of structured and unstructured data. It then discusses key aspects of big data including the three Vs of volume, variety, and velocity. The document also provides examples of big data applications in various industries. It explains common analytical methods used in big data including linear regression, decision trees, and neural networks. Finally, it discusses popular tools and frameworks for big data analytics.
Reporting involves building, organizing, and summarizing raw data into reports that raise questions about what is happening in the business. Analysis transforms this information into insights by interpreting the data at a deeper level to answer questions and provide actionable recommendations about why things are happening and what can be done. Both reporting and analysis play important roles in driving actions that create greater value for organizations, with reporting providing information to identify issues and analysis providing explanations and solutions to help bridge the gap between data and actions.
This document summarizes a survey on data mining. It discusses how data mining helps extract useful business information from large databases and build predictive models. Commonly used data mining techniques are discussed, including artificial neural networks, decision trees, genetic algorithms, and nearest neighbor methods. An ideal data mining architecture is proposed that fully integrates data mining tools with a data warehouse and OLAP server. Examples of profitable data mining applications are provided in industries such as pharmaceuticals, credit cards, transportation, and consumer goods. The document concludes that while data mining is still developing, it has wide applications across domains to leverage knowledge in data warehouses and improve customer relationships.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
This document provides an introduction to big data and analytics. It discusses the topics of data processing, big data, data science, and analytics and optimization. It then provides a historic perspective on data and describes the data processing lifecycle. It discusses aspects of data including metadata and master data. It also discusses different data scenarios and the processing of data in serial versus parallel formats. Finally, it discusses the skills needed for a data scientist including business and domain knowledge, statistical modeling, technology stacks, and more.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
The document discusses challenges in analytics for big data. It notes that big data refers to data that exceeds the capabilities of conventional algorithms and techniques to derive useful value. Some key challenges discussed include handling the large volume, high velocity, and variety of data types from different sources. Additional challenges include scalability for hierarchical and temporal data, representing uncertainty, and making the results understandable to users. The document advocates for distributed analytics from the edge to the cloud to help address issues of scale.
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
This document introduces data science, big data, and data analytics. It discusses the roles of data scientists, big data professionals, and data analysts. Data scientists use machine learning and AI to find patterns in data from multiple sources to make predictions. Big data professionals build large-scale data processing systems and use big data tools. Data analysts acquire, analyze, and process data to find insights and create reports. The document also provides examples of how Netflix uses data analytics, data science, and big data professionals to optimize content caching, quality, and create personalized streaming experiences based on quality of experience and user behavior analysis.
BIAM 410 Final Paper - Beyond the Buzzwords: Big Data, Machine Learning, What...Thomas Rones
This document discusses big data, machine learning, and NoSQL databases. It defines big data as referring to large or complex datasets that require techniques like NoSQL, MapReduce, and machine learning for analysis. Machine learning is made possible by large amounts of publicly available unstructured data and advances in computing. NoSQL databases are used to store big data because they allow for more flexibility than structured SQL databases for applications that need to scale.
A Model Design of Big Data Processing using HACE TheoremAnthonyOtuonye
This document presents a model for big data processing using the HACE theorem. It proposes a three-tier data mining structure to provide accurate, real-time social feedback for understanding society. The model adopts Hadoop's MapReduce for big data mining and uses k-means and Naive Bayes algorithms for clustering and classification. The goal is to address challenges of big data and assist governments and businesses in using big data technology.
An introduction to Data Mining by Kurt ThearlingPim Piepers
An Introduction to Data Mining Discovering hidden value in your data warehouse By Kurt Thearling Overview Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?" This white paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today’s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.
The document provides an overview of data science. It defines data science as a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, and deep learning. It explains that data science uses both traditional structured data stored in databases as well as big data from various sources. The document also describes how data scientists preprocess and analyze data to gain insights into past behaviors using business intelligence and then make predictions about future behaviors.
This document provides a summary of a group project report on big data analytics. It discusses how big data and analytics can help companies optimize supply chains by improving decision making and handling risks. It defines big data as large, diverse, and rapidly growing datasets that are difficult to manage with traditional tools. It also discusses data sources, management, quality dimensions, and using statistical process control methods to monitor and control data quality throughout the production process.
Data Mining is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. It is very important to understand the importance and need of data mining in todays situation.
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
Data mining involves using algorithms to automatically find patterns in large datasets. It is used to make predictions about future trends and behaviors to help companies make proactive decisions. The document discusses the history and evolution of data mining, from early data collection and storage to today's powerful algorithms and massive databases. Common data mining techniques are also outlined.
Unit 1 Introduction to Data Analytics .pptxvipulkondekar
The document provides an introduction to the concepts of data analytics including:
- It outlines the course outcomes for ET424.1 Data Analytics including discussing challenges in big data analytics and applying techniques for data analysis.
- It discusses what can be done with data including extracting knowledge from large datasets using techniques like analytics, data mining, machine learning, and more.
- It introduces concepts related to big data like the three V's of volume, variety and velocity as well as data science and common big data architectures like MapReduce and Hadoop.
This document provides an overview of data mining. It defines data mining as a process that takes data as input and outputs knowledge. The data mining process involves preparing data, applying data mining algorithms to identify patterns, and evaluating the results. The document discusses the motivation for data mining, including the growth of data and need to analyze unstructured data. It outlines common data mining tasks like classification, regression, association rule mining, clustering, and text and link analysis. The tasks of classification and regression are described in more detail.
This document provides an overview of data mining, including definitions, processes, tasks, and algorithms. It defines data mining as a process that takes data as input and outputs knowledge. The main steps in the data mining process are data preparation, data mining (applying algorithms to identify patterns), and evaluation/interpretation. Common data mining tasks are classification, regression, association rule mining, clustering, and text/link mining. Popular algorithms described are decision trees, rule-based classifiers, artificial neural networks, and nearest neighbor methods. Each have advantages and disadvantages related to predictive power, speed, and interpretability.
This document discusses data mining with big data. It begins with an agenda that covers problem definition, objectives, literature review, algorithms, existing systems, advantages, disadvantages, big data characteristics, challenges, tools, and applications. It then goes on to define the problem, objectives, provide a literature review summarizing several papers, and describe the architecture, algorithms, existing systems, HACE theorem that models big data characteristics, advantages of the proposed system, challenges, and characteristics of big data. It concludes that formalizing big data analysis processes will be important as data volumes continue increasing.
The document discusses big data, including the different units used to measure data size like bytes, kilobytes, megabytes, etc. It notes that big data is difficult to store and process using traditional tools due to its large size and complexity. Big data is growing rapidly in volume, velocity and variety. Some challenges in analyzing big data include its unstructured nature, size that exceeds capabilities of conventional tools, and need for real-time insights. Security, access control, data classification and performance impacts must be considered when protecting big data.
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
This document provides an introduction to the concepts of data analytics and the data analytics lifecycle. It discusses big data in terms of the 4Vs - volume, velocity, variety and veracity. It also discusses other characteristics of big data like volatility, validity, variability and value. The document then discusses various concepts in data analytics like traditional business intelligence, data mining, statistical applications, predictive analysis, and data modeling. It explains how these concepts are used to analyze large datasets and derive value from big data. The goal of data analytics is to gain insights and a competitive advantage through analyzing large and diverse datasets.
The Power of Data: Understanding Supply Chain AnalyticsXeneta
This is why supply chain analytics matters: Every day, every hour and every minute, countless packages and shipments are being moved around the world within never-ending flows of supply chains. These supply chains serve as the backbone of the world economy and, in truth, are what keeps the world moving. But consider for a moment the amount of data, information, and decisions that are required to make a supply chain not only operate but to do so effectively. While the end goal is to get the package from A to B as quickly and efficiently as possible, there’s a lot more going on behind the scenes that make it all work.
The document discusses tools and techniques for big data analytics, including A/B testing, crowdsourcing, machine learning, and data mining. It provides an overview of the big data analysis pipeline, including data acquisition, information extraction, integration and representation, query processing and analysis, and interpretation. The document also discusses fields where big data is relevant like industry, healthcare, and research. It analyzes tools like A/B testing, machine learning, and data mining techniques in more detail.
A New Analytics Paradigm in the Age of Big Data: How Behavioral Analytics Will Help You Understand Your Customers and Grow Your Business Regardless of Data Sizes
Bda assignment can also be used for BDA notes and concept understanding.Aditya205306
Big data refers to large and complex datasets that are difficult to analyze using traditional methods. It is characterized by high volume, velocity, and variety of data from numerous sources. Big data analytics uses tools like Hadoop and Spark to extract meaningful insights from large, unstructured datasets in real-time. This allows companies to gain valuable business insights, reduce costs, enhance customer experience, innovate products, and make faster decisions.
A Comparative Study of Various Data Mining Techniques: Statistics, Decision T...Editor IJCATR
In this paper we focus on some techniques for solving data mining tasks such as: Statistics, Decision Trees and Neural
Networks. The new approach has succeed in defining some new criteria for the evaluation process, and it has obtained valuable results
based on what the technique is, the environment of using each techniques, the advantages and disadvantages of each technique, the
consequences of choosing any of these techniques to extract hidden predictive information from large databases, and the methods of
implementation of each technique. Finally, the paper has presented some valuable recommendations in this field.
Mining Big Data using Genetic AlgorithmIRJET Journal
This document discusses using genetic algorithms to mine big data through clustering. It begins by introducing big data and the challenges of analyzing large and complex data sets using traditional methods. It then proposes using a combination of genetic algorithms and existing clustering algorithms to more efficiently process big data. Specifically, it suggests genetic algorithms can optimize clustering results for big data by combining advantages of genetic algorithms and clustering. The document provides an overview of concepts like data mining, genetic algorithms and big data, and how genetic algorithms may be applied to clustering large data sets.
Your LinkedIn Success Starts Here.......SocioCosmos
In order to make a lasting impression on your sector, SocioCosmos provides customized solutions to improve your LinkedIn profile.
https://www.sociocosmos.com/product-category/linkedin/
EASY TUTORIAL OF HOW TO USE G-TEAMS BY: FEBLESS HERNANEFebless Hernane
Using Google Teams (G-Teams) is simple. Start by opening the Google Teams app on your phone or visiting the G-Teams website on your computer. Sign in with your Google account. To join a meeting, click on the link shared by the organizer or enter the meeting code in the "Join a Meeting" section. To start a meeting, click on "New Meeting" and share the link with others. You can use the chat feature to send messages and the video button to turn your camera on or off. G-Teams makes it easy to connect and collaborate with others!
This tutorial presentation provides a step-by-step guide on how to use Facebook, the popular social media platform. In simple and easy-to-understand language, this presentation explains how to create a Facebook account, connect with friends and family, post updates, share photos and videos, join groups, and manage privacy settings. Whether you're new to Facebook or just need a refresher, this presentation will help you navigate the features and make the most of your Facebook experience.
The Evolution of SEO: Insights from a Leading Digital Marketing AgencyDigital Marketing Lab
Explore the latest trends in Search Engine Optimization (SEO) and discover how modern practices are transforming business visibility. This document delves into the shift from keyword optimization to user intent, highlighting key trends such as voice search optimization, artificial intelligence, mobile-first indexing, and the importance of E-A-T principles. Enhance your online presence with expert insights from Digital Marketing Lab, your partner in maximizing SEO performance.
Lifecycle of a GME Trader: From Newbie to Diamond Handsmediavestfzllc
Your phone buzzes with a Reddit notification. It's the WallStreetBets forum, a cacophony of memes, rocketship emojis, and fervent discussions about Gamestop (GME) stock. A spark ignites within you - a mix of internet bravado, a rebellious urge to topple the hedge funds (remember Mr. Mayo?), and maybe that one late-night YouTube rabbit hole about tendies. You decide to YOLO (you only live once, right?).
Ramen noodles become your new best friend. Every spare penny gets tossed into the GME piggy bank. You're practically living on fumes, but the dream of a moonshot keeps you going. Your phone becomes an extension of your hand, perpetually glued to the GME ticker. It's a roller-coaster ride - every dip a stomach punch, every rise a shot of adrenaline.
Then, it happens. Roaring Kitty, the forum's resident legend, fires off a cryptic tweet. The apes, as the GME investors call themselves, erupt in a frenzy. Could this be it? Is the rocket finally fueled for another epic launch? You grip your phone tighter, heart pounding in your chest. It's a wild ride, but you're in it for the long haul.
Telegram is a messaging platform that ushers in a new era of communication. Available for Android, Windows, Mac, and Linux, Telegram offers simplicity, privacy, synchronization across devices, speed, and powerful features. It allows users to create their own stickers with a user-friendly editor. With robust encryption, Telegram ensures message security and even offers self-destructing messages. The platform is open, with an API and source code accessible to everyone, making it a secure and social environment where groups can accommodate up to 200,000 members. Customize your messenger experience with Telegram's expressive features.
Project Serenity is an innovative initiative aimed at transforming urban environments into sustainable, self-sufficient communities. By integrating green architecture, renewable energy, smart technology, sustainable transportation, and urban farming, Project Serenity seeks to minimize the ecological footprint of cities while enhancing residents' quality of life. Key components include energy-efficient buildings, IoT-enabled resource management, electric and autonomous transportation options, green spaces, and robust waste management systems. Emphasizing community engagement and social equity, Project Serenity aspires to serve as a global model for creating eco-friendly, livable urban spaces that harmonize modern conveniences with environmental stewardship.
EASY TUTORIAL OF HOW TO USE REMINI BY: FEBLESS HERNANEFebless Hernane
Using Remini is easy and quick for enhancing your photos. Start by downloading the Remini app on your phone. Open the app and sign in or create an account. To improve a photo, tap the "Enhance" button and select the photo you want to edit from your gallery. Remini will automatically enhance the photo, making it clearer and sharper. You can compare the before and after versions by swiping the screen. Once you're happy with the result, tap "Save" to store the enhanced photo in your gallery. Remini makes your photos look amazing with just a few taps!
This tutorial presentation offers a beginner-friendly guide to using THREADS, Instagram's messaging app. It covers the basics of account setup, privacy settings, and explores the core features such as close friends lists, photo and video sharing, creative tools, and status updates. With practical tips and instructions, this tutorial will empower you to use THREADS effectively and stay connected with your close friends on Instagram in a private and engaging way.
UR BHatti Academy dedicated to providing the finest IT courses training in the world. Under the guidance of experienced trainer Usman Rasheed Bhatti, we have established ourselves as a professional online training firm offering unparalleled courses in Pakistan. Our academy is a trailblazer in Dijkot, being the first institute to officially provide training to all students at their preferred schedules, led by real-world industry professionals and Google certified staff.
STUDY ON THE DEVELOPMENT STRATEGY OF HUZHOU TOURISMAJHSSR Journal
ABSTRACT: Huzhou has rich tourism resources, as early as a considerable development since the reform and
opening up, especially in recent years, Huzhou tourism has ushered in a new period of development
opportunities. At present, Huzhou tourism has become one of the most characteristic tourist cities on the East
China tourism line. With the development of Huzhou City, the tourism industry has been further improved, and
the tourism degree of the whole city has further increased the transformation and upgrading of the tourism
industry. However, the development of tourism in Huzhou City still lags far behind the tourism development of
major cities in East China. This round of research mainly analyzes the current development of tourism in
Huzhou City, on the basis of analyzing the specific situation, pointed out that the current development of
Huzhou tourism problems, and then analyzes these problems one by one, and put forward some specific
solutions, so as to promote the further rapid development of tourism in Huzhou City.
KEYWORDS:Huzhou; Travel; Development
1. The Big Data Paradigm Shift:
Insight Through Automation
2. In this white paper, you will learn about:
• Big Data’s combinatorial explosion
• Current and emerging technologies
• Automation as the new way to leverage
insight within Big Data
• An algorithmic approach to the Big
Data revolution
2
w w w. e m c i e n . c o m
3. CONTENTS
01 / EXECUTIVE SUMMARY
page 5
02 / THE BIG DATA
PARADIGM SHIFT
page 6
03 / LANDSCAPE OF EXISTING
METHODS AND TOOLS
page 8
04 / EMERGENCE OF
BIG DATA TOOLS
page 10
05 / THE NEED FOR A NEW BIG
DATA ANALYTICS APPROACH
page 11
06 / EMCIEN’S ALGORITHMIC
APPROACH TO BIG DATA
page 13
07 / NOT JUST THEORY:
SOLVING REAL-WORLD
PROBLEMS
page 15
08 / CONCLUSION
page 16
w w w. e m c i e n . c o m
3
4. ... a study of progress over a 15-year span on a
benchmark production-planning task. Over that
time, the speed of completing the calculations
improved by a factor of 43 million. Of the
total, a factor of roughly 1,000 was attributable
to faster processor speeds. Yet a factor of
43,000 was due to improvements in the
efficiency of software algorithms.”
Martin Grotschel, a German scientist and mathematician
White House Advisory Report
DECEMBER 2010
4
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5. Data used to be scarce, and
tiny bits of it were extremely
valuable. Tiny bits of it are
still extremely valuable, but
now there is so much data
that finding the valuable bits
can be extremely difficult.”
Executive Summary
Big Data promises greater insight, competitive advantage, and the possibility of solving problems that
have yet to be imagined. These insights will come from software applications that automate the analytics
process. While infrastructure may be an important building block, this paper focuses on the algorithms
that will deliver the insights organizations need.
This paper does the following:
• Proposes a paradigm shift away from analysts’ one-to-one relationship with data toward a relationship
with algorithms.
• Explains the combinatorial explosion that makes Big Data Analytics impossible for old data analytics tools
and methodologies.
• Examines current and emerging technologies.
• Suggests that methods which accommodate Big Data users in the same way that smaller data sets are
handled are missing the potential of Big Data.
• Proposes that rather than search and crunch data, organizations need the ability to automate the
process of analyzing, visualizing and ultimately leveraging the insight within their data.
• Introduces an algorithmic approach that provides an efficient, sustainable, automated way to delve
into Big Data, detect patterns, and discover insights hidden within that data.
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5
6. The Big Data Paradigm Shift
The Need for a Paradigm Shift
Humankind has always possessed a love for data.
We can do remarkable things with data and have
built remarkable tools to collect, store, sift, sort,
splice, dice, chart, report, predict, and visualize it.
Data can change the way we perceive the world and
how we interact with it. But the world is changing.
Data used to be scarce, and tiny bits of it were
extremely valuable. Tiny bits of it are still extremely
valuable, but now there is so much data that finding
the valuable bits can be extremely difficult.
Big Data demands that organizations change the
way they interact with data. In the past, analysts
could stand by the data faucet and collect what was
needed in a paper cup, but now data is the ocean
in which they are floating. Paper cups are useless
here. Why? Because the search for data is over.
Data is everywhere. It creates noise. Now analysts
are searching for the signal amidst the noise. They
are looking for the important bits. And in this vast
sea of information, that task is overwhelming.
Current tools and methodologies are failing when it
comes to finding the most critical information in a
time-sensitive, cost-effective manner. The reason?
Many of these emerging tools and technologies are
trying to approach this challenge with new ways of
doing the same old things. But a bigger cup is not
what’s needed. That won’t solve it. What is needed
is a completely new approach to Big Data Analytics.
In this new approach, the only way to find the
signal is to automate the process of data-to-insight
conversion. And automation requires algorithms—
fast, sophisticated, highly optimized algorithms.
The volume of Big Data demands a change in the
human relationship with data, from human to
machine. The algorithms have to do the work, not
the humans. In this brave new world, the machines
and algorithms are the protagonists. The role of
the analysts will be to select the best algorithms
and approve the quality of results based on speed,
quality and economics.
6
The Promise of Data and the
Search for Insight
Why is the world obsessed with data? Because
the promise of data is insight. In the last few
years, organizations have become exceptionally
good at collecting data, and as the cost of storage
has dropped, companies are now drowning
in that “Big Data.” However, the business
world has hit a wall where the amount of
data
available
far
exceeds
the
human
capacity to process it. The amount of data
also
exceeds
the
capabilities
of
existing
analytics and intelligence tools, which have served
as mere data-shovels or pick axes in the search for
the gold that is insight.
Acquiring insight inevitably involves querying a
database, or, most likely, several databases. Many
analysts are mashing up data across data silos in an
attempt to discover the connections between data
points. For example, marketers are aggregating
customer demographics, purchase data and social
media data, while purchasers are aggregating
supplier data with procurement and pricing data.
This process produces a variety of data sets of
different types and qualities.
A simple query, for example, might ask for specific
values within a subset of columns. So the real
question becomes, “How many queries will it take
to answer even one of these questions?” Consider
how many queries one might make into even a
small set of data, such as a table containing just
ten columns:
• If each column has 2 possible values, there are
59,048 possible queries.
• If each column has 3 possible values, there are
1,048,575 possible queries.
To think of it another way, a database with 100 columns
and 6 choices per column yields more possible queries
than there are atoms in the universe.
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7. The Limitations of Search and
Query-based Approaches
Data becomes unwieldy due to the number of rows
or the number of columns, or both. Data mash-ups,
described previously, create a lot of columns. High
volume transactional systems have lots of rows or
records. However, having millions of records is not
the problem. The depth of the data—the number of
rows—merely impacts processing time in a linear
fashion and can be reduced with fast or parallel
computing. Thus, executing a query is simple
enough. The problem, however, is the width of the
data because the query explodes exponentially
based on the number of columns.
Number of Queries
As a result, the real task of extracting insight from
data is formulating the right queries. And manually
laboring through thousands of queries to find the
ones that deliver insight is not an efficient way to
derive value from data. Therefore, when it comes to
Big Data, the big challenge is knowing the right query.
30 Billion
Exponential
Explosion
of Queries
10 Billion
1 Billion
1 Million
0
2
4
6
8
10
12
14
Number of Variables per Column
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7
8. Landscape of Existing Methods and Tools
Over 85% of all data is unstructured. 1 However,
existing methods and tools are designed to analyze
structured data. A high level categorization of
analytics tools is critical to understanding the state
of Big Data Analytics.
Statistical Tool Kits
The purpose of statistical analysis is to make
inferences from samples of data, especially when
data is scarce. In the era of Big Data, scarcity is not
the problem. Traditional statistical methods have
severe limitations in the realm of Big Data for the
following reasons:
• Statistical
methods
break
down
as
dimensionality increases.
• In unstructured data, dimensions are not
well defined.
• Attempts to define dimension for unstructured
data result in millions of dimensions.
Data Mining
Data mining is a catchall phrase for a very broad
category of methods. Essentially, it is a method
for sifting through very large amounts of data
in attempt to find useful information. It implies
“digging through tons of data” to uncover patterns
and relationships contained within the business
activity and history. Data mining involves manually
slicing and dicing the data until a pattern becomes
obvious or by using software that analyzes the
data automatically.
The first limitation of data mining is that the data
has to be put in a structured format first, such as a
database. The second limitation is that most forms
of data mining require that the analyst knows
what to look for. For example, in classification and
clustering analysis, the analyst is trying to find
instances of known categories, such as people
who have a high probability of defaulting on their
mortgages. In anomaly detection, the analyst is
looking for instances that do not match the known
normal patterns or known suspicious patterns, such
as people who pay cash for one-way plane tickets.
8
The overwhelming shortcoming
of all these methods is that
they are query-based and
labor intensive.”
Data Visualization
Data visualization is the study of the visual representation
of data, meaning information that has been abstracted
in some schematic form, including attributes or variables
for the units of information.2 Humans are better
equipped to consume visual data than text. As we know,
a picture is worth a thousand words.
While visualization tools are interesting, they rely on
human evaluation to extract insight and knowledge. The
more severe limitation of visualization is that the visuals
can only focus on two or three dimensions at the most
before the amount of information is overwhelming. The
most common limitation of visualization is that while it
is a good test for small samples, it is not a sustainable
method to gain insight into large volumes of higher
dimensionality data.
Consider a scenario in which there aren’t enough pixels
on the screen to represent each item. An analyst can
easily inspect a friendship network with 10-100 people,
but not on billion.
Business Intelligence & Analytics
Business Intelligence (BI) is a catchall phrase for ad
hoc reports created in a database. These are typically
pre-canned reports based on metrics that users
are comfortable reporting. “Analytics” includes any
computation performed for reporting. Hence, BI tools
are now called analytics. BI was created as a way to
extract data from the database. While it continues to
serve that purpose, it is time and labor intensive and is
not intended to surface insights.
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9. Limitations of Existing Tools
The overwhelming shortcoming of all these methods
is that they are query-based and labor-intensive.
Big Data offers an infinite number of queries, causing
all these methods to rely on analysts to produce
questions. Any method that puts the burden on the
user is a game-stopper.
Although search remains the go-to information access
interface, reliance on search needs to end. Search is
not enough. A new type of information-processing
focus is needed.
The major shortcomings of the existing tools are as follows:
• Because search helps you discover insights you already know
about, it doesn’t help you discover things about which you’re
completely unaware.
• Query-based tools are time-consuming because search-based
approaches require a virtually infinite number of queries.
• Statistical methods are largely limited to numerical data; over
85% of data is unstructured.
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10. Emergence of Big Data Tools
Because Big Data includes data sets with sizes beyond the ability of commonly-used software tools to
capture, curate, manage, and process within a tolerable elapsed time, new technologies are emerging to
address the challenges brought on by these large quantities of data. These technologies can be categorized
into two groups: Hadoop-based solutions and In-Memory based solutions.
Hadoop and Hadoop-based Tools
While Hadoop is not an analytics tool per se, it is
often confused as being one. Apache Hadoop is
an open-source software framework that supports
data-intensive distributed applications. It supports
the running of applications on large clusters of
commodity hardware.
Hadoop is used to break big tasks into smaller
ones so that they can be run in parallel to gain
speed and efficiency. This is great for a query on
a large volume data set. The data set can be cut
into smaller pieces, and the same query can be run
on each smaller set. Hadoop aims to lower costs
by storing data in chunks across many inexpensive
servers and storage systems. The software can
help speed up certain types of simple calculations
by sending many queries to multiple machines at
the same time. The technology has spawned a set
of new start-ups, such as Hortonworks Inc. and
Cloudera Inc., which help companies implement it.
Hadoop helps companies store large amounts of
data but doesn’t provide critical insights based on
the naturally occurring connections within the data.
The impulse to store lots of data because it is cheap
to do so can lead to storing too much data, which
can make answering simple questions more difficult.
10
IT professionals and analysts are asking the
following questions:
• Where is the insight? What is the data telling us?
• How can I prove the return of investment?
As a result, in-memory databases are gaining
attention in an attempt to come closer to the goal
of real-time business processing. 3,4
In-Memory-based Appliance
Some of these approaches have been around for a
long time in areas such as telecommunications or
fields related to embedded databases. An example
is SAP’s HANA (High Performance Analytical
Appliance). This in-memory paradigm is now touted
as the future database paradigm for Big Data.
The primary limitation of in-memory is cost and
size. There is a significant limit to the amount of
data that can be held in memory. If you need to
perform Big Data–style analysis and you want to
see the bigger picture, in-memory is not enough.
The cost is prohibitive and it is not sustainable.
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11. The Need for a New Big Data Analytics Approach
While these emerging technologies are attempting to address the challenge of Big Data, at the end of the day,
they are heavy-handed and time-consuming because they lack automated intelligence for gaining insight. It’s
time for an entirely new approach.
This new approach demands a paradigm shift that focuses on the following:
• A fundamental change in the role played by analysts from data-miners to insight-evaluators.
• Fast and efficient algorithms that automatically convert data to insight for evaluation.
• Continual improvement of these algorithms to keep up with the speed of data and critical need for
timely insights.
Old Paradigm:
New Paradigm:
Data Analyst Digs for Insights by
Manually Querying a Database
Algorithms Automatically Surface
Insights to Evaluate
Analysis Takes From
Months to Years
Automatic Insights in
Seconds to Minutes
Specialized Skills in Math
and Computer Science Required
Anyone
(No Specialized Skills Required)
Operational and Business Intelligence
Immediate Insight and Perspective
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12. Emergence of Algorithms as a
New Class of Big Data Software Tools
The size and speed of Big Data demands true automation,
in which work is offloaded from human to machine. This
automation happens with algorithms, which are designed
for calculation, data processing, and automated reasoning.
Algorithms are designed for tasks that are beyond human
comprehension and require the speed of machines. This is
the realm of Big Data.
One of the most dramatic and game-changing examples of
an algorithm was designed by Alan Turing to automatically
decode German Navy messages at Bletchley Park during
WWII. In this instance, the urgency was critical and
demanded an automated approach to convert the data
to intelligence. There were 158 million million million
(158,000,000,000,000,000,000) possible ways that a
message could be coded by the German Enigma machine.
The decoder algorithm, Shark, worked and the results
changed the course of the war. The Allies won because they
had a competitive advantage. Bringing it to the present,
the use of Big Data in the 2012 United States presidential
election changed the face of political campaigns forever.
Emcien’s approach to Big Data is to automate the process
of data-to-insight in a timely and cost effective manner
through sophisticated algorithms. The algorithms leverage
advanced mathematics to solve complex problems of
an unimaginable size, thereby pushing the frontier of
innovation and competition. The following section details
Emcien’s algorithmic approach to Big Data Analytics.
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13. Emcien’s Algorithmic Approach to Big Data
Rather than search and crunch data, organizations need the ability to analyze, visualize and ultimately leverage
the patterns and connections within their data. Emcien’s innovation is a suite of automatic pattern detection
algorithms. These algorithms utilize a graph data model that captures the interconnectedness of the data
elements and creates a very elegant representation of high volume data with unknown structure. These fast,
sophisticated algorithms automatically detect patterns and self-organize what they find thereby providing
immediate insight and perspective.
Here is an outline of how the algorithm works:
1. Assesses the data in order to identify and measure connections between data points.
2. Converts the original high density/low value (structured, semi-structured or unstructured) data into a low
density/high value graph.
Structured
If the data is structured:
• Each row in the data table is considered an event.
• Each cell in the row is converted to a node in the graph.
• Cells that co-occur in a row (event) are connected by an arc.
Unstructured
If the data is unstructured:
• Every word or data element is converted to a node.
• Two words or data elements that occur simultaneously in an event are
connected by an arc.
• An event may be defined as a single document, message, email exchange, etc.
3. Builds the graph on non-Euclidean distances. This is important, as most of the data is unstructured and
non-numeric. The distances and strengths are computed in non-Euclidean space. (For example – you may
be closer to your family than your friends – but this is not Euclidean space!).
4. Computes millions of data points across the graphs to enable patterns to emerge.
5. Distills the noise, to allow the signal to emerge. This is made possible because the noise has patterns and
the algorithms are designed to detect these patterns.
6. Enables the key topographical elements from the graph to emerge. The algorithm will then rank and focus
these elements.
7. Categorizes these elements based on the application and output the insight.
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14. Understanding the make-up of Graphs based on Connections
The graph data model is very flexible and displays a distinct topography based on the density of connections. A
cross sectional view of the graph data model will typically expose the following layers Layer
Connectedness
Description
1
Very Noisy Connections
Typically the most highly prevalent data, this layer is composed of high
volume interactions that may be mundane and blatantly obvious.
2
Highly Connected Nodes
Lying just below the noise, this second layer is composed of the first
signal that is interesting. This layer exhibits distinct patterns based on
crowd behavior.
3
Weaker Connections
The third layer is a weaker signal and displays the non-obvious
connections. These relate to events that are less frequent and may be
connected in non-obvious ways.
4
The Faint Signal
Composed of very weak connections and interactions, this last layer is
of interest for security and surveillance. In many cases, this layer only
emerges when the data is very rich in entities, causing connections to
emerge in very non-obvious ways.
Advantages of Emcien’s Graph Data Model
The graph data model exhibits a topography that signifies relationships and connectedness in a way that is not
possible through any other method. Emcien’s algorithms have been designed to surface these patterns. Listed
below are a few key attributes that help describe the characteristics of the algorithms.
Attribute
Advantage
Software
A critical distinction of Emcien’s graph data model is that it is software.
Emcien’s software provides the computational engine with a data representation that
lends itself to high-speed computing. As a result, the software runs on typical commodity
computing environments.
Algorithmic
Layer
Compact
Representation
of Data
Data is big because the number of events can grow exponentially as the various entities are
continually interacting.
Noise
Elimination
14
Although some products on the market model the graph into the database layer or hardware
layer, they do not have an algorithmic layer, and, therefore, require the user to query the
systems based on the old data-inquiring paradigm. Algorithms automate the data analysis
process that is an absolute requirement for efficient Big Data analytics.
The graph data model can be thought of in terms of layers, based on the connectedness of
the data elements. The highly connected and noisy nodes are at the top layer, and the weak
connections lay buried deep in the graphs. The noisy connections can be overwhelming
and tend to render graph models as burdensome. Emcien utilizes a suite of patented
algorithms to automatically distance the noise and detect critical patterns that relate to
highly significant and relevant information.
The graph representation is ideal for Big Data because it creates a very compact
representation of the data. This is because the number of entities grows more slowly
and reaches a natural steady state. In the graph data model these interactions translate
to connection weights, allowing the graph model to encapsulate very big data in
smaller structures.
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15. Not Just Theory: Solving Real-World Problems
The representation and visualization of complex networks as graphs helps surface critical, time-sensitive
intelligence. One of the most important tasks in graph analysis is to identify closely connected network
components comprising nodes that share similar properties. Detecting communities is of significant value
in retail, healthcare, banking and intelligence work - verticals where loosely federated communities deliver
insight and intelligence into the profile of a customer base or any other group being analyzed.
How Can This Model Be Applied? Emcien’s Pattern Detection Engine:
• Intelligence: Surfaces critical correlations
between people that merit serious attention,
determines key individuals in targeted social
networks, and geo-locates persons of interest
and their networks around the world – from
gangs to terrorists.
• Network Security: Auto-detects intrusion patterns
and surfaces suspicious activity by providing
immediate insight into highly linked variables. It
then automatically identifies anomalies without
the user having to query the data. For example,
Emcien analyzes millions to billions of transactions
to identify patterns in source and destinationIP addresses, ports, days, times and activity – to
show you what you should be paying attention
to. Emcien eliminates over 95% of the noise and
identifies patterns that are “surprising” or that
deviate from the norm.
• Fraud Detection: Surfaces patterns in money
laundering and fraud by identifying groups of
customers, locations, or transaction types that
occur together in banking transactions,
• Customer Analytics: Surfaces insights on customer
buying patterns, locations, demographics, loyalty,
savings, lifestyle and insurance.
• Healthcare Analytics: Analyzes massive volumes
of clinical data on medications, allergies, medical
claims, pharmacy therapies, lab results, medical
records, clinician notes and more in order to
surface patterns.
• Performance and Operations Analytics: Analyzes
raw information about performance and operations
of every element of an organization which can be
interpreted to increase profitability or improve
customer service.
In short, Emcien tackles one of the biggest challenges with Big Data, namely “What are the right questions
to ask?” Emcien’s pattern-detection engine quickly discovers the value within massive data sets by making
connections between disparate, seemingly unrelated bits of information and by finding the highest-ranked of
these connections to focus on – which reveals time-sensitive, mission-critical insights.
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15
16. Conclusion
The Big Data Analytics revolution is underway. This
revolution is a historic and game-changing expansion of
the role that information plays in business, government
and consumer realms. To harness the power of this data
revolution, a paradigm shift is required. Organizations
must be able to do more than query their Big Data
stores; search is no longer enough.
Up until now in the history of data analysis, the objective
of queries was to find the signal in the noise. And it
worked because we had clear-cut business questions
and the size of the data was smaller, the data set was
more complete, and we usually knew what we were
looking for. We were playing in the realm of known
knowns and known unknowns. In the new world of Big
Data, it is now more important to know what to ignore.
Because unless you know what to ignore, you’ll never
get a chance to pay attention to what’s really important.
Using algorithms to first ignore the noise and then find
the insights is the way of the new world.
Extracting insight from Big Data requires analytics
methods that are fundamentally different from
traditional querying, mining, and statistical analysis
on small samples. Big Data is often noisy, dynamic,
heterogeneous, unstructured, inter-related and
untrustworthy.
The combinatorial explosion requires new methods
for finding insight in Big Data. The need for data
sophistication is due to economics and time-criticality.
As stated, manually laboring through thousands of
queries to find the ones that deliver insight is not an
efficient way to derive value from data.
Emcien’s technology provides a “Command Center”
for Big Data, automatically interpreting the data,
discovering patterns, identifying complex and significant
relationships, and surfacing the most relevant questions
that lead to the insights analysts need to know.
About Emcien Corp.
Emcien’s automatic pattern-detection engine converts data to actionable insight that organizations can use
immediately. Emcien breaks through time, cost and scale barriers that limit the ability to operationalize the
value of data for mission-critical applications. Our patented algorithms recognize what’s important, defocus
what’s not, evaluate all possible combinations and deliver the optimal results automatically. Emcien’s engine,
fueled by several highly competitive NSF grants and years of research at Georgia Tech and MIT, is delivering
unprecedented value to organizations across sectors that depend on immediate insight for success—banking,
healthcare, insurance, retail, Intelligence and others. Visit emcien.com to learn more.
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17. Sources
1. Christopher C. Shilakes and Julie Tylman, “Enterprise Information Portals”, Merrill Lynch, 16 November, 1998.
2. Michael Friendly, “Milestones in the history of thematic cartography, statistical graphics, and data visualization,” 2008.
3. J. Vascellaro, “Hadoop Has Promise but Also Problems,” The Wall Street Journal, 23 February 2012.
4. R. Srinivasan, “Enterprise Hadoop: Five Issues With Hadoop That Need Addressing,” blog, 28 May 2012.