COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 4.1: Part 1: ntroduction Limitations of MapReduce ❖ MapReduce greatly simplified big data analysis on large, unreliable clusters. It is great at one-pass computation. ❖ But as soon as it got popular, users wanted more: ➢ More complex, multi-pass analytics (e.g. ML, graph) ➢ More interactive […]
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ : MapReduce III Design Pattern 3: Order Inversion Computing Relative Frequencies ❖ “Relative” Co-occurrence matrix construction ➢ Similar problem as before, same matrix ➢ Instead of absolute counts, we take into consideration the fact that some words appear more frequently than others Word wi may co-occur
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This exercise aims to get you to apply the order inversion and value-to-key conversion design patterns for MapReduce programming you have learned in Chapter 3.1. If you have not finished solving the problems in Lab 3, please keep working on them, and then move to Lab 4. Create a folder ¡°Lab4¡± and put all your
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 1: Course Information and Introduction to Big Data Management Part 1: Course Information Course Info ❖ Lectures: 10:00 – 12:00 (Tuesday) and 14:00 – 16:00 (Thursday) ➢ Purely online (access through Moodle) ❖ Labs: Weeks 2-10 ❖ Consultation (Weeks 1-10): Questions regarding lectures, course materials, assignements,
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This exercise aims to get you to: • Install and configure HBase, and Manage data using HBase Shell • Install and configure Hive, and Manage data using Hive HBase Installation and Configuration 1. Download HBase 2.4.8 $ wget https://dlcdn.apache.org/hbase/2.4.8/hbase-2.4.8-bin.tar.gz Then unpack the package: $ tar xvf hbase-2.4.8-bin.tar.gz 2. Define environment variables for HBase We need
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 9: Recommender Systems Recommendations Recommendations Products, web sites, blogs, news items, … Recommender Systems Recommender Systems ❖ Application areas ➢ Movie recommendation (Netflix) ➢ Related product recommendation (Amazon) ➢ Web page ranking (Google) ➢ Social recommendation (Facebook) Netflix Movie Recommendation Why using Recommender Systems? ❖ Value
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This exercise aims to get you to apply the design patterns you have learned in Chapter 2.2 in MapReduce programming. If you have not finished solving the problems in Lab 2, please keep working on them first and then move to Lab 3. Create a folder ¡°Lab3¡±. Put all your codes written in this week¡¯s
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 7.2: Finding Similar Items Docu- ment Candidate pairs: those pairs of signatures that we need to test for similarity of strings of length k that appear in the doc- ument Signatures: short integer vectors that represent the sets, and reflect their similarity Step 3: Locality-Sensitive Hashing:
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 4: MapReduce IV Graph Data Processing in MapReduce What’s a Graph? ❖ G = (V,E), where ➢ V represents the set of vertices (nodes) ➢ E represents the set of edges (links) ➢ Both vertices and edges may contain additional information ❖ Different types of graphs:
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This exercise aims to get you to: • Install and configure Hadoop MapReduce • Practice HDFS operations • Test Hadoop MapReduce with the pseudo-distributed mode Background In the examples below, we have used the $ sign to represent the prompt from the command interpreter (shell). The actual prompt may look quite different on your computer
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