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Data mining with big data

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Sandip Tipayle Patil

This document discusses data mining with big data. It defines data mining as the process of discovering patterns in large data sets and big data as collections of data that are too large to process using traditional software tools. The document notes that 2.5 quintillion bytes of data are created daily and that 90% of data was produced in the past two years. It provides examples of big data like presidential debates and photos. It also discusses challenges of mining big data due to its huge volume and complex, evolving relationships between data points.

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Data Mining with Big data By: Pouya Otarod Spring 2014
What is ? Data Mining computational process of discovering patterns in large data sets Big Data it is the term for a collection of data sets so large and complex that it becomes difficult to process data has exponential growth, both structured and unstructured
How much Data does exist? 2.5 quintillion bytes of data are created EVERY DAY IBM: 90 percent of the data in the world today were produced with past two years Forms of Data????
Big Data Examples October 4th, 2012, the first presidential debate Flicker and its photos
Problem! Data has grown tremendously This large amount of data is beyond the of software tools to manage Exploring the large volume of data and extracting useful information and knowledge is a challenge, and sometimes, it is almost infeasible
HACE Theorem Heterogeneous, Autonomous, Complex, Evolving Big data starts with large volume, heterogeneous, autonomous sources with distributed and decentralized control, and seeks to explore complex and evolving relationships among data These are characteristics of Big Data This is theorem to model Big Data characteristics
Data mining with big data
Huge Data with heterogeneous and diverse dimensionality represent huge volume of data Autonomous sources with distributed and decentralized control main characteristics of Big Data Complex and evolving relationships
Data Mining Challenges with Big Data Big Data Mining Platform Dig Data Semantics and Application Knowledge I. Information Sharing and Data Privacy II. Domain and Application Knowledge Big Data Mining Algorithm I. Local Learning and Model Fusion for Multiple Information Sources II. mining from Sparse, Uncertain, and Incomplete Data III. Mining Complex and Dynamic Data
Thanks for you attentions !

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Data mining with big data

  • 1. Data Mining with Big data By: Pouya Otarod Spring 2014
  • 2. What is ? Data Mining computational process of discovering patterns in large data sets Big Data it is the term for a collection of data sets so large and complex that it becomes difficult to process data has exponential growth, both structured and unstructured
  • 3. How much Data does exist? 2.5 quintillion bytes of data are created EVERY DAY IBM: 90 percent of the data in the world today were produced with past two years Forms of Data????
  • 4. Big Data Examples October 4th, 2012, the first presidential debate Flicker and its photos
  • 5. Problem! Data has grown tremendously This large amount of data is beyond the of software tools to manage Exploring the large volume of data and extracting useful information and knowledge is a challenge, and sometimes, it is almost infeasible
  • 6. HACE Theorem Heterogeneous, Autonomous, Complex, Evolving Big data starts with large volume, heterogeneous, autonomous sources with distributed and decentralized control, and seeks to explore complex and evolving relationships among data These are characteristics of Big Data This is theorem to model Big Data characteristics
  • 8. Huge Data with heterogeneous and diverse dimensionality represent huge volume of data Autonomous sources with distributed and decentralized control main characteristics of Big Data Complex and evolving relationships
  • 9. Data Mining Challenges with Big Data Big Data Mining Platform Dig Data Semantics and Application Knowledge I. Information Sharing and Data Privacy II. Domain and Application Knowledge Big Data Mining Algorithm I. Local Learning and Model Fusion for Multiple Information Sources II. mining from Sparse, Uncertain, and Incomplete Data III. Mining Complex and Dynamic Data