Lectures and Readings (Big Data Computing, 2019)

Tuesday, February 26 - Course introduction and overview. The four V's of big data. Volume: how big is "big"? What can we do with big data? How can we program big data systems? Velocity: what can we compute if we cannot even store the input?

• Nice reading: the sexiest job of the 21st century
• Slides with course introduction

Friday, March 1 - Processing data on disk: the external memory model (Aggarwal & Vitter): parameters M and B. Fundamental external memory bounds: scan and sort. Data permutation: I/O-analysis of the naive linear-time algorithm, scan&sort algorithm.

• External memory: introductory slides
• Notes on scan&sort permutation algorithm (only page 1)

Tuesday, March 5 - Sorting data on disk: 2-way mergesort, I/O analysis. A key inefficiency of 2-way mergesort. k-way mergesort, with full analysis. Hints for an efficient implementation. The 1TB sorting problem: can we do more than one pass? A back of the envelope calculation.

• Notes on binary mergesort I/O analysis
• Notes on k-way mergesort
• K. Mehlhorn, P. Sanders. Algorithms and data structures: The basic toolbox, 2009, Chapter 5 (Sorting and selection), Section 7 (without sample sort). On-line version.

Friday, March 8 - Public transport strike. No class.

Tuesday, March 12 - MapReduce: stable storage, distributed file system, programming model (data as key-value pairs, map/reduce functions, wordcount example).

• Dean & Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, OSDI 2004 paper
• MapReduce programming model: slides

Friday, March 15 - MapReduce runtime system: distributed execution overview, master, workers, key partitioning, local storage vs. distributed file system, task coordination, failures.

• MapReduce runtime system: slides

Tuesday, March 19 - Hands-on class. Introduction to Hadoop, configuration, context, mapper and reducer classes, basic writable types (Text, IntWritable, LongWritable). Implementing WordCount. Running WordCount: main HDFS commands.

• Hadoop: slides
• Wordcount implementation, data sets, how-to-run notes
• If you want to avoid installing Hadoop on your platform (or want to do that later), you can use the LXLE virtual machine (file size: 4GB). This requires VirtualBox, which is available on the Oracle website.

Friday, March 22 - Good coding practices: examples of simple MapReduce algorithms (matrix transpose, sum of n values). Towards a theoretical cost model.

• Computational model, examples: slides
• A model of computation for MapReduce, by H. Karloff, S. Suri & S. Vassilvitskii, appeared in Proceedings of the ACM SIAM Symposium on Discrete Algorithms, SODA 2010 (Sections 1, 2, 3, only definitions covered in class).

Tuesday, March 26 - Computing a minimum spanning tree (MST) in MapReduce: high-level description of the algorithm. MST algorithm: correctness and space analysis. Local space of reduce 1 functions, i.e., expected value of |E_i,j|. Space analysis of round 2: size of subgraph H. When is local space sublinear? Deriving the optimal value for k, sublinearity on c-dense graphs.

• MST: algorithm description and correctness
• MST: notes on space analysis - part 1
• MST: notes on space analysis - part 2
• A model of computation for MapReduce, by H. Karloff, S. Suri & S. Vassilvitskii, Proceedings of the ACM SIAM Symposium on Discrete Algorithms, 2010 (Section 5, only definitions and proofs covered in class).

Friday, March 29 - Mining networks: examples and properties of real-world networks, Erdős-Rényi model, locality and clustering coefficient, small world properties (Milgram's experiment, six degrees of separation), degree distribution and power laws, scale free networks. Generative network models: Watts & Strogatz model, preferential attachment model by Barabasi & Albert.

• Slides on real-world networks properties

Tuesday, April 2 - Triangle counting: naive approach, NodeIterator, !NodeIterator++, MapReduce implementations.

• Slides on triangle counting
• Paper on triangle counting

Friday, April 5 - Spatial and temporal locality. Case study: matrix multiplication. Number of I/Os of the standard algorithm. Reuse distance. Data layout and blocking. Blocked iterative matrix multiplication.

• C. Demetrescu and I. Finocchi: notes on blocked matrix multiplication: iterative algorithm.

April 9 and 12 - classes suspended

Tuesday, April 16 - Exercises

• June 8, 2017
• June 28, 2017
• first midterm 2017
• Midterm solutions.

Tuesday, April 30 - Document similarity: set similarity, shingling, Jaccard similarity, minHashing. Analysis of minHashing with one random permutation. Detailed minHashing example. Implementation of minHashing: (1) how to do a single pass over the document matrix; (2) how to get rid of permutations.

• Slides used in class.
• Mining of Massive Datasets book, sections 3.1, 3.2, 3.3

Friday, May 3 - Midterm

Tuesday, May 7 - Locality-sensitive hashing: partitioning the signature matrix into bands, definition of candidate pair, intuition behind LSH, two examples, analysis.

• Slides: see previous lesson
• Mining of Massive Datasets book, section 3.4

Friday, May 10 - Algorithms for data streams. Motivations, applications, and theoretical model. Streaming puzzles: 1) missing number; 2) curious George goes fishing.

• Class slides
• M. Muthukrishnan: survey on data streams: algorithms and applications
• C. Demetrescu and I. Finocchi: survey on algorithms for data streams, Handbook of Applied Algorithms: Solving Scientific, Engineering and Practical Problems, 2008.

Tuesday, May 14 - Another streaming puzzle: pointer and chaser (how to find a duplicate item). Sampling uniformly from infinite streams: reservoir sampling (algorithm and analysis).

• See previous class for pointer and chaser slides
• Reservoir sampling slides

Friday, May 17 - Triangle counting in (adversarial) data streams: a sampling-based approach. Space lower bound based on a communication complexity reduction.

• Triangle counting slides: upper and lower bounds on triangle counting in data streams

Tuesday, May 21 - Frequent items (aka heavy hitters). Problem definition: frequency threshold, approximating the solution (parameter epsilon) and probabilistic guarantees (parameter delta). Sticky sampling: algorithm, correctness and expected space.

• Heavy hitters: Class slides

Friday, May 24 - Paper hacking session.

Friday, May 31 - Paper hacking session.

Monday, June 3 - Paper hacking session.

-- Irene Finocchi - May 2019