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The Age of Big Data

The Age of Big Data. Thanks to Kayvan Tirdad at York University and Kalapriya Kannan at IBM. 1. Contents. Introduction: Explosion in Quantity of Data. 1. 1. Big Data Characteristics. 2. 2. Cost Problem (example). 3. 3. Importance of Big Data. 4. 4. Usage Example in Big Data. 5.

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The Age of Big Data

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  1. The Age of Big Data Thanks to Kayvan Tirdad atYork University and Kalapriya Kannan at IBM

  2. 1 Contents Introduction: Explosion in Quantity of Data 1 1 Big Data Characteristics 2 2 Cost Problem (example) 3 3 Importance of Big Data 4 4 Usage Example in Big Data 5 5

  3. Contents Some Challenges in Big Data 1 6 Other Aspects of Big Data 7 2

  4. Data verses Information What we have / What we want • Data is raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized. • When data is processed, organized, structured or presented in a given context so as to make it useful.

  5. What Is Big Data? • Big data is defined as voluminous unstructured data from many different sources, such as: • Social networks • Banking and financial services • E-commerce services • Web-centric services • Internet search indexes • Scientific searches • Document searches • Medical records • Weblogs

  6. Big data-From Wikipedia, the free encyclopedia Big data[1][2] is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. As of 2012[update], limits on the size of data sets that are feasible to process in a reasonable amount of time were on the order of exabytes of data.[8] Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics,[9]connectomics, complex physics simulations,[10] and biological and environmental research.[11] The limitations also affect Internet search, finance and business informatics. Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies (remote sensing), software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks.[12][13][14] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[15] as of 2012[update], every day 2.5 exabytes (2.5×1018) of data were created.[16] The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.[17] Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers".[18] What is considered "big data" varies depending on the capabilities of the organization managing the set, and on the capabilities of the applications that are traditionally used to process and analyze the data set in its domain. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[19]

  7. What is big data? • “Every day, we create 2.5 quintillion(1018) bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is “big data.”

  8. Simple to start • What is the maximum file size you have dealt so far? • Movies/Files/Streaming video that you have used? • What have you observed? • What is the maximum download speed you get? • Simple computation • How much time to just transfer.

  9. Data size 1 요타 바이트

  10. Introduction: Explosion in Quantity of Data 1946 2012 Eniac LHC X 6,000,000 = 1 (40 TB/S) Air Bus A380 • - 1 billion line of code • each engine generate 10 TB • every 30 min 640TB per Flight Twitter Generate approximately 12 TB of data per day New York Stock Exchange 1TB of data everyday storage capacity has doubled roughly every three years since the 1980s

  11. Our Data-driven World Introduction: Explosion in Quantity of Data • Science • Data bases from astronomy, genomics, environmental data, transportation data, … • Humanities and Social Sciences • Scanned books, historical documents, social interactions data, new technology like GPS … • Business & Commerce • Corporate sales, stock market transactions, census, airline traffic, … • Entertainment • Internet images, Hollywood movies, MP3 files, … • Medicine • MRI & CT scans, patient records, …

  12. Spatio-Temporal Data Average Monthly Temperature of land and ocean

  13. 5 Our Data-driven World Introduction: Explosion in Quantity of Data - Fish and Oceans of Data What we do with these amount of data? Ignore

  14. 7 Big Data Characteristics Big Data Vectors (3Vs) • high-volume amount of data • high-velocity Speed rate in collecting or acquiring or generating or processing of data • high-variety different data type such as audio, video, image data (mostly unstructured data)

  15. Big data spans three dimensions: Volume, Velocity and Variety • Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information. • Turn 12 terabytes of Tweets created each day into improved product sentiment analysis • Convert 350 billion annual meter readings to better predict power consumption • Velocity:Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value. • Scrutinize 5 million trade events created each day to identify potential fraud • Analyze 500 million daily call detail records in real-time to predict customer churn faster • The latest I have heard is 10 nano seconds delay is too much. • Variety: Big data is any type of data - structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together. • Monitor 100’s of live video feeds from surveillance cameras to target points of interest • Exploit the 80% data growth in images, video and documents to improve customer satisfaction

  16. 6 Big Data Characteristics How big is the Big Data? - What is big today maybe not big tomorrow • Any data that can challenge our current technology in some manner can consider as Big Data • Volume • Communication • Speed of Generating • Meaningful Analysis Big Data Vectors (3Vs) "Big Data are high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization” Gartner 2012

  17. 8 Cost Problem (example) Cost of processing 1 Petabyte of data with 1000 node ? 1 PB = 1015 B = 1 million gigabytes = 1 thousand terabytes • 9 hours for each node to process 500GB at rate of 15MB/S • 15*60*60*9 = 486000MB ~ 500 GB • 1000 * 9 * 0.34$ = 3060$ for single run • 1 PB = 1000000 / 500 = 2000 * 9 = 18000 h /24 = 750 Day • The cost for 1000 cloud node each processing 1PB 2000 * 3060$ = 6,120,000$

  18. 9 Importance of Big Data • Government • In 2012, the Obama administration announced the Big Data Research • and Development Initiative • 84 different big data programs spread across six departments • Private Sector • - Walmart handles more than 1 million customer transactions every hour, • which is imported into databases estimated to contain more than • 2.5 petabytes of data • - Facebook handles 40 billion photos from its user base. • - Falcon Credit Card Fraud Detection System protects 2.1 billion active • accounts world-wide • Science • - Large Synoptic Survey Telescope will generate • 140 Terabyte of data every 5 days. • - Large HardonColider 13 Petabyte data produced in 2010 • - Medical computation like decoding human Genome • - Social science revolution • - New way of science (Microscope example)

  19. 10 Importance of Big Data • Technology Player in this field • Oracle • Exadata • Microsoft • HDInsight Server • IBM • Netezza • Job • The U.S. could face a shortage by 2018 of 140,000 to 190,000 people with "deep analytical talent" and of 1.5 million people capable of analyzing data in ways that enable business decisions. (McKinsey & Co) • Big Data industry is worth more than $100 billion growing at almost 10% a year (roughly twice as fastas the software business)

  20. 12 Usage Example of Big Data US 2012 Election - data mining for individualized ad targeting - Orca big-data app - YouTube channel( 23,700 subscribers and 26 million page views) - Ace of Spades HQ - predictive modeling - mybarackobama.com - drive traffic to other campaign sites Facebook page (33 million "likes") YouTube channel (240,000 subscribers and 246 million page views). - a contest to dine with Sarah Jessica Parker - Every single night, the team ran 66,000 computer simulations, Reddit!!! - Amazon web services

  21. 13 Usage Example in Big Data Data Analysis prediction for US 2012 Election media continue reporting the race as very tight Drew Linzer, June 2012 332 for Obama, 206 for Romney Nate Silver’s, Five thirty Eight blog Predict Obama had a 86% chance of winning Predicted all 50 state correctly Sam Wang, the Princeton Election Consortium The probability of Obama's re-election at more than 98%

  22. 11 Usage Example in Big Data • Moneyball: The Art of Winning an Unfair Game Oakland Athletics baseball team and its general manager Billy Beane - Oakland A's' front office took advantage of more analytical gauges of player performance to field a team that could compete successfully against richer competitors in MLB - Oakland approximately $41 million in salary, New York Yankees, $125 million in payroll that same season. Oakland is forced to find players undervalued by the market, - Moneyball had a huge impact in other teams in MLB And there is a moneyball movie!!!!!

  23. Some Challenges in Big Data • Big Data Integration is Multidisciplinary • Less than 10% of Big Data world are genuinely relational • Meaningful data integration in the real, messy, schema-less and complex Big Data world of database and semantic web using multidisciplinary and multi-technology methode • The Billion Triple Challenge • Web of data contain 31 billion RDf triples, that 446million of them are RDF links, 13 Billion government data, 6 Billion geographic data, 4.6 Billion Publication and Media data, 3 Billion life science data • BTC 2011, Sindice 2011 • The Linked Open Data Ripper • Mapping, Ranking, Visualization, Key Matching, Snappiness • Demonstrate the Value of Semantics: let data integration drive DBMS technology • Large volumes of heterogeneous data, like link data and RDF

  24. 15 Other Aspects of Big Data Six Provocations for Big Data 1- Automating Research Changes the Definition of Knowledge 2- Claim to Objectively and Accuracy are Misleading 3- Bigger Data are not always Better data 4- Not all Data are equivalent 5- Just because it is accessible doesn’t make it ethical 6- Limited access to big data creats new digital divides

  25. Other Aspects of Big Data Five Big Question about big Data: 1-What happens in a world of radical transparency, with data widely available? 2- If you could test all your decisions, how would that change the way you compete? 3- How would your business change if you used big data for widespread, real time customization? 4- How can big data augment or even replace Management? 5-Could you create a new business model based on data?

  26. The Four Phases of Data Conversion 1 2 3 4

  27. Finally…. `Big- Data’ is similar to ‘Small-data’ but bigger .. But having data bigger it requires different approaches: Techniques, tools, architecture … with an aim to solve new problems Or old problems in a better way

  28. Whom does it matter • Research Community  • Business Community - New tools, new capabilities, new infrastructure, new business models etc., • On sectors Financial Services..

  29. How are revenues looking like….

  30. The Social Layer in an Instrumented Interconnected World 4.6 billion camera phones world wide 30 billion RFID tags today (1.3B in 2005) 12+ TBsof tweet data every day 100s of millions of GPS enabled devices sold annually ? TBs ofdata every day 2+ billion people on the Web by end 2011 25+ TBs oflog data every day 76 million smart meters in 2009… 200M by 2014

  31. What does Big Data trigger? • From “Big Data and the Web: Algorithms for Data Intensive Scalable Computing”, Ph.D Thesis, Gianmarco

  32. BIG DATA is not just HADOOP Understand and navigate federated big data sources Federated Discovery and Navigation Hadoop File System MapReduce Manage & store huge volume of any data Data Warehousing Structure and control data Stream Computing Manage streaming data Text Analytics Engine Analyze unstructured data Integrate and govern all data sources Integration, Data Quality, Security, Lifecycle Management, MDM

  33. Types of tools typically used in Big Data Scenario • Where is the processing hosted? • Distributed server/cloud • Where data is stored? • Distributed Storage (eg: Amazon s3) • Where is the programming model? • Distributed processing (Map Reduce) • How data is stored and indexed? • High performance schema free database • What operations are performed on the data? • Analytic/Semantic Processing (Eg. RDF/OWL)

  34. When dealing with Big Data is hard • When the operations on data are complex: • Eg. Simple counting is not a complex problem. • Modeling and reasoning with data of different kinds can get extremely complex • Good news with big-data: • Often, because of the vast amount of data, modeling techniques can get simpler (e.g., smart counting can replace complex model-based analytics)… • …as long as we deal with the scale.

  35. Time for thinking • What do you do with the data. • Lets take an example: • “From application developers to video streamers, organizations of all sizes face the challenge of capturing, searching, analyzing, and leveraging as much as terabytes of data per second—too much for the constraints of traditional system capabilities and database management tools.”

  36. Why Big-Data? • Key enablers for the appearance and growth of ‘Big-Data’ are: • Increase in storage capabilities • Increase in processing power • Availability of data

  37. THINK

  38. References • B. Brown, M. Chuiu and J. Manyika, “Are you ready for the era of Big Data?” McKinsey Quarterly, Oct • 2011, McKinsey Global Institute • C. Bizer, P. Bonez, M. L. Bordie and O. Erling, “The Meaningful Use of Big Data: Four Perspective – • Four Challenges” SIGMOD Vol. 40, No. 4, December 2011 • D. Boyd and K. Crawford, “Six Provation for Big Data” A Decade in Internet Time: Symposium on the • Dynamics of the Internet and Society, September 2011, Oxford Internet Institute • 4. D. Agrawal, S. Das and A. E. Abbadi, “Big Data and Cloud Computing: Current State and Future • Opportunities” ETDB 2011, Uppsala, Sweden • D. Agrawal, S. Das and A. E. Abbadi, “Big Data and Cloud Computing: New Wine or Just New Bottles?” • VLDB 2010, Vol. 3, No. 2 • 6. F. J. Alexander, A. Hoisie and A. Szalay, “Big Data” IEEE Computing in Science and Engineering • journal 2011 • 7. O. Trelles, P Prins, M. Snir and R. C. Jansen, “Big Data, but are we ready?” Nature Reviews, Feb 2011 • K. Bakhshi, “Considerations for Big data: Architecture and approach” Aerospace Conference, 2012 • IEEE • S. Lohr, “The Age of Big Data” Thr New York times Publication, February 2012 • 10. M. Nielsen, “Aguide to the day of big data”, Nature, vol. 462, December 2009

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