1.0i40https://i40.dit.uoi.grDimitris Mitrogiorgoshttps://i40.dit.uoi.gr/index.php/author/dim_mitrogiorgos/rich600338<blockquote class="wp-embedded-content" data-secret="XgDKkaSK0N"><a href="https://i40.dit.uoi.gr/index.php/en/big-data-and-analytics/">Big Data and Analytics</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://i40.dit.uoi.gr/index.php/en/big-data-and-analytics/embed/#?secret=XgDKkaSK0N" width="600" height="338" title="“Big Data and Analytics” — i40" data-secret="XgDKkaSK0N" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://i40.dit.uoi.gr/wp-includes/js/wp-embed.min.js </script> Semester B Mandatory Remote Big Data and Analytics Introduction to basic signal processing methods (DFT, wavelets). Data preprocessing. Feature extraction, feature selection, dimensionality reduction (Singular Value Decomposition). Data compression methods (scalar and vector quantization, lossless and lossy compression). Spatial data indexing (Spatial Access Methods – k-d trees, quadtrees, z-ordering, space-filling curves, R-trees). ECTS Credits 0 Weeks 0 Total Hours 0 Bibliography Sources 0 Learning Outcomes 1) Understanding of basic techniques for processing and analyzing large-scale data 2) Understanding and analyzing algorithms for the analysis and processing of multidimensional data 3) Implementation of distributed analysis and large-scale data processing techniques 4) Understanding the use of algorithmic optimization methods 5) Design of techniques for the analysis and processing of large-scale, multidimensional, and multimodal data 6) Experience in optimization techniques and multidimensional signal processing techniques 7) Implementation of distributed techniques for large-scale data processing and analysis General Skills Data search & synthesis Independent project Team project Inductive & Creative Thinking Big data Optimization methods Syllabus Week Topic 1 Introduction to Smart Industry 2 Technologies related to Smart Industry 3 Cyber-Physical Systems 4 Internet of Things – Big Data 5 Machine Learning – Artificial Intelligence 6 Project Topic Selection 7 Blockchain Technology 8 3D – 4D Printing 9 Midterm Project Presentations 10 Applications: Predictive Maintenance 11 Applications: Mass Customization 12 Applications: Manufacturing Cloud 13 Final Project Presentations Evaluation & Workload Semester Workload Evaluation Methods Activity Hours Lectures 39 Bibliographic Assignment 31 Project Implementation 25 Independent Study 30 Course Total 125 Individual Project Final Individual Project Presentation Team Project Final Team Project Presentation ActivityHoursLectures39 Bibliographic Assignment 31 Project Implementation 25 Independent Study 30Course Total125 Individual Project Final Individual Project Presentation Team Project Final Team Project Presentation Bibliography Recommended Textbooks Scientific Journals Convex Optimization, Cambridge University PressStephen Boyd, Lieven Vandenberghe Linear Algebra and Learning from Data Wellesley-Cambridge Press, 2018Strang, Gilbert Introductory Lectures on Convex Programming Volume I: Basic courseYu. Nesterov Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends in Machine Learning, Vol. 3, No. 1 (2010)Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato and Jonathan Eckstein Proximal Algorithms, Foundations and Trends in Optimization, Vol. 1, No. 3 (2013)Neal Parikh, Stephen Boyd Convex Optimization, Cambridge University PressStephen Boyd, Lieven VandenbergheLinear Algebra and Learning from Data Wellesley-Cambridge Press, 2018Strang, GilbertIntroductory Lectures on Convex Programming Volume I: Basic courseYu. NesterovDistributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends in Machine Learning, Vol. 3, No. 1 (2010)Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato and Jonathan EcksteinProximal Algorithms, Foundations and Trends in Optimization, Vol. 1, No. 3 (2013)Neal Parikh, Stephen Boyd Course Information Semester B΄ ECTS5 Minutes per Week 180 Type Specialized Knowledge Requirements — Course Format Synchronous 30% Asynchronous 70% Remote e-class Erasmus ✓ Technologies & Tools DTF/wavelets OPC UA Spatial Access Methods Hadoop Spark Back to Courses Page Πλατφόρμα e-class Υλικό μαθήματος, βίντεο, forum & ανακοινώσεις Πρόσβαση στο e-class