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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?

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.

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.

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).

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.

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 |Ei,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.

Friday, March 29 - Mining networks: examples and properties of real-world networks, Erdős-RÚnyi 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.

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

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.

April 9 and 12 - classes suspended

Tuesday, April 16 - Exercises

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.

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.

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

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).

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

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.

Friday, May 24 - Paper hacking session.

Friday, May 31 - Paper hacking session.

Monday, June 3 - Paper hacking session.

-- Irene Finocchi - May 2019

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Topic revision: r106 - 2019-06-01 - IreneFinocchi

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