Course Details

This is a blog for students enrolled in CSCI 8980 special topic course on Big Data Algorithms in Fall 2013 at University of Minnesota to share their feedback on the course, have a forum for discussion and create a repository of datasets and relevant references. All course related information will be posted here.

Here is the course description

For decades researchers across different disciplines of computer science have envisioned the need of techniques to handle data-intensive computing. With the boom of internet and the explosion of data in every socio-economical aspect, once what was a futuristic research, has now transformed itself into a dire requirement. Big Data comes with immense opportunity, but turning this seriously high volume, high velocity, structured or unstructured, heterogeneous, often noisy and high-dimensional data into something one can use is a huge challenge. This course aims at timely dissemination of foundational algorithmic developments for big data analysis and exposing students to cutting edge research in this area. The course will involve deep theoretical analysis with the goal of developing practical algorithms with variety of applications. We will explore trade-off s among space, time and accuracy for algorithm design. The course will cover different sampling methodologies, streaming algorithms where only a small fraction of data can be stored, semi-streaming model that allows a few sequential access to disk and some parallel algorithms, specifically, the map-reduce paradigm. We will study the algorithmic and complexity aspects of these frameworks. A primary focus of this course will be on designing scalable, sub-quadratic, often near-linear or even sub-linear algorithms. We will explore property testing methodology that allows in sub-linear time to test whether a data set has certain property. We will go through the recent progress in developing fast algorithms for basic graph problems such as max-flow min-cut, matching etc., sparse transformations such as sparse fast Fourier transform, and hashing methodologies involving min-hash and locality sensitive hashing. The other topics will include dimensionality reduction techniques to handle high-dimensional data comprising of random projection method, Johnson Lindenstrauss Lemma, metric embedding and graph sparsi fiers. We will also explore crowdsourcing–the power of human assisted computing. Most of the algorithms that we will study in this course will crucially use randomization and will give an answer that is a good approximation of the optimal solution.

Prerequisites Students should have basic knowledge of algorithms: running time analysis, graphs algorithms, and must be familiar with discrete probability. Undergraduates are welcome to attend if they satisfy the requirements.

Syllabus There will be no required text book for this course, instead we will use assorted materials from the web. A tentative list of topics is as follows:

  1.  Streaming: Sampling and Sketching
  2.  Dimensionality Reduction
  3. External Memory and Semi-streaming Algorithms

  4.  Map-Reduce Framework and Extensions
  5.  Near Linear Time Algorithm Design

  6.  Property Testing
  7.  Metric Embedding

  8.  Sparse Transformation
  9. Crowdsourcing

The class meets every Thursday 6:30 pm–9:00 pm CT at MechE 221.

Office hours: Friday 1pm to 5pm at  6-198 KHKH by Appointment.

See pdf flyer of the course with contact information Algorithmic Techniques for Big Data 

Relevant Other Courses Taught in Other Universities

  1. Sub-linear Algorithms by Piotr Indyk, Ronitt Rubinfeld at MIT
  2. Algorithms for Massive Data Sets by Piotr Indyk at MIT
  3. Mining Big Data by Anand Rajaraman and Jeffrey D. Ullman at Stanford University
  4. Dealing with Massive Data by Sergei Vassilvitskii from Google Research at Columbia University
  5. Data Stream Algorithms by Andrew Mcgregor at UMASS
  6. Algorithms for Big Data Management by Ashwin Machanavajjhala at Duke
  7. Data Stream Algorithms by Amit Chakrabarti at Dartmouth


Latex template for writing scribe scribe

Lecture notes will be uploaded unedited whenever available. They might get edited at a later date.

  1. 09/05 Overview Slides   Various models and techniques, counting distinct items in different models, no scribe.
  2. 09/05 Introduction to data streams, finding frequent items deterministically, lower bound for deterministic computation of  distinct elements, Markov inequality, Chebyshev bound, The Chernoff bound.  Scribe by Vivek Mishra.  Lecture 1
  3. 09/12 Universal family of hash functions, Analysis of two algorithms for counting distinct items  Slides for Lecture 2 
  4. 09/12 Count-Min sketch and applications   Slides from Andrew Mcgregor’s Data Streaming Course, Project discussion Project Discussion

Background on Randomized Algorithms:

  • Randomized Algorithms by Motwani and Raghavan
  • Probability and Computing by Mitzenmacher and Upfal
  • The Probabilistic Method by Alon and Spencer

Relevant Reading


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