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Michael G Reed munication over a public network It provides anony mous connections that are strongly resistant to both eavesdropping and trac analysis Onion routings anonymous connections are bidirectional and near real time and can be used anywhere a socket connection can be used Any identifying information must be in the data stream carried over
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 2.2: MapReduce II Overview of Previous Lecture ❖ Motivation of MapReduce ❖ Data Structures in MapReduce: (key, value) pairs ❖ Hadoop MapReduce Programming Output pairs do not need to be of the same types as input pairs. A given input pair may map to zero
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ : NoSQL and HBase Part 1: Introduction to NoSQL What does RDBMS provide? ❖ Relational model with schemas ❖ Powerful, flexible query language (SQL) ❖ Transactional semantics: ACID ❖ Rich ecosystem, lots of tool support (MySQL, PostgreSQL, etc.) What is NoSQL? ❖ The name stands for Not
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 4.2: I Download and Configure Spark ❖ Current version: 3.1.2. https://spark.apache.org/downloads.html ➢ You also need to install Java first ❖ After downloading the package, unpack it and then configure the path variable in file ~/.bashrc export SPARK_HOME=/home/comp9313/workdir/spark export PATH=$SPARK_HOME/bin:$PATH ❖ Spark comes with four widely used
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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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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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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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