02282 Algorithms for Massive Data Sets

General Info | Mandatory Exercises | Weekplan | FAQ

General Info

Teachers

Inge Li Gørtz, inge@dtu.dk.
Philip Bille, phbi@dtu.dk, office hours Monday + Thursday 12.30-13.00.
Hjalte Wedel Vildhøj, hwvi@dtu.dk.
Patrick Hagge Cording, phaco@dtu.dk.

When and where Monday 8.15-12, Bldg. 324, room 040. The course runs in the DTU spring semester (Feb. 2 to May 15). There is no teaching in the Easter break (March 30 and April 6).

Piazza The course uses Piazza as onlineforum discussion of material. Sign up here

Mandatory Exercises

Use the template.tex file to prepare your hand in exercises. Do not repeat the problem statement in your hand in. Compile using LaTeX. Upload the resulting pdf file (and only this file) via Campusnet. The maximum size of the finished pdf must be at most 2 pages. An exercise from week x must be handed in no later than Sunday in week x before 20.00.

Collaboration policy for mandatory exercises

You may collaborate with up to 3 fellow students on the hand in exercises. The collaborators must be listed in your solution (see template).
Collaboration is limited to discussion of ideas only, and you should write up the solutions entirely on your own.
Do not use or seek out solutions from previous years of the course, solutions from similar courses, or solutions found on the internet.

Violations of the collaboration policy will be reported.

Weekplan

The weekplan is preliminary It will be updated during the course. Under each week there is a number of suggestions for reading material regarding that weeks lecture. It is not the intention that you read ALL of the papers. It is a list of papers and notes where you can read about the subject discussed at the lecture.

Topics	Slides	Weekplan	Mandatory	Material
Approximation Algorithms I: Scheduling, k-center.	1x1 · 4x1	Approximation Algorithms I	Week 1	The Design of Approximation Algorithms, Williamson and Shmoys, Cambridge Press, section 1.1, 2.1, 2.2, and 2.3. A unified approach to approximation algorithms for bottleneck problems, D. S. Hochbaum and D. B. Shmoys, Journal of the ACM, Volume 33 Issue 3, 1986.
Integer Data Structures I: Dictionaries, Universal and Perfect Hashing.	1x1 · 4x1	Hashing	Mandatory	Notes from Aarhus, Peter Bro Miltersen Scribe notes from MIT Universal Classes of Hash Functions, J. Carter and M. Wegman, J. Comp. Sys. Sci., 1977 Storing a Sparse Table with O(1) Worst Case Access Time, M. Fredman, J. Komlos and E. Szemeredi, J. ACM., 1984
Integer Data Structures II: Predecessor Problem, van Emde Boas, x-Fast and y-Fast Tries	1x1 · 4x1	Predecessor	Mandatory	Scribe notes from MIT Introduction to Algorithms, 3rd edition, Chap. 20, T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, 2009 Log-Logarithmic Worst-Case Range Queries are Possible in Space Θ(n), Dan E. Willard, Inf. Process. Lett., 1983 Preserving Order in a Forest in less than Logarithmic Time,P. van Emde Boas, FOCS, 1975 Time-space trade-offs for predecessor search, M. Patrascu and M. Thorup, STOC 2006
Trees I: Nearest Common Ancestor, Range Minimum Query		NCA+RMQ	Mandatory	The LCA problem revisited, M. A. Bender, M. Farach-Colton, Latin American Symposium 2000. Scribe notes from MIT Fast Algorithms for Finding Nearest Common Ancestors, D. Harel and R. E. Tarjan, SIAM J. Comput., 13(2), 338–355.
Trees II: Level Ancestor, Path Decompositions, Tree Decompositions	1x1 · 4x1	Level Ancestor	Mandatory	The Level Ancestor Problem Simplified, M. A. Bender, M. Farach-Colton, Theoret. Comp. Sci., 2003. Scribe notes from MIT Finding level-ancestors in dynamic trees, P. F. Dietz, WADS 1991. Finding level-ancestors in trees, O. Berkman, U. Vishkin, J. Comput. System Sci., 1994
Trees III: Range Reporting, Range Trees, Fractional Cascading, kD Trees	1x1 · 4x1	Range Reporting	Mandatory	Scribe notes from MIT Computational Geometry: Algorithms and Applications, M. de Berg, O. Cheong, M. van Kreveld and M. Overmars Fractional cascading: I. A data structuring technique, B. Chazelle and L. Guibas, Algoritmica, 1986 Analysis of range searches in quad trees, J. L. Bentley and D. F. Stanat, Inf. Process. Lett., 1975
Strings I: Dictionaries, Tries, Suffix trees, Suffix Sorting	1x1 · 4x1	Suffix Trees	Mandatory	Linear work suffix array construction, J. Kärkkäinen, P. Sanders, S. Burkhardt, J. ACM, 2006 Scribe notes from MIT Algorithms on Strings, Trees, and Sequences, Chap. 5-9, D. Gusfield On the sorting-complexity of suffix tree construction, M. Farach-Colton, P. Ferragina, S. Muthukrishnan, J. ACM, 2000
Strings II: Approximate String Matching	Part 1, Part 2	Approximate String Matching	Mandatory	G. M. Landau and U. Vishkin, “Efficient string matching with k mismatches“, Theoretical Computer Science, Volume 43, 1986. M. J. Fischer and M. S. Paterson, “String Matching and Other Products“1974 K. Abrahamson, “Generalized string matching“, SIAM Journal on Computing, Volume 16 Issue 6, 1987. Amihood Amir , Moshe Lewenstein , Ely Porat, “Faster Algorithms for String Matching with k Mismatches“, JACM 2000.
Strings III: Compression, Lempel-Ziv, Grammar-compression, and Burrows-Wheeler Transform	1x1	Compression	Mandatory	Guy E. Blelloch, “Introduction to Data Compression“. Notes on Compression Schemes.
Strings IV: Compressed Pattern Matching	1x1	Compressed Pattern Matching	Mandatory	Philip Bille, Gad M. Landau, Rajeev Raman, Kinihiko Sadakane, Srinivasa Rao Satti, Oren Weimann “Random Access to Grammar-Compressed Strings and Trees“, SODA 2011. Leszek Gasieniec, Roman Kolpakov, Igor Potapov, Paul Sant “Real-time Traversal in Grammar-based Compressed Files“, DCC 2005. Martin Farach, S. Muthukrishnan “Perfect hashing for strings: Formalization and algorithms“, CPM 2005.
External Memory I: I/O Model, Cache-Oblivious Model, External Dynamic Programming	1x1 · 4x1	External Memory	Mandatory	Cache-oblivious dynamic programming, R. A. Chowdhury and V. Ramachandran, SODA 2006 The Input/Output Complexity of Sorting and Related Problems, A. Aggarwal and J. Vitter, CACM 1988 Cache-Oblivious Algorithms and Data Structures, Erik Demaine, Lecture Notes from the EEF Summer School on Massive Data Sets, 2002
External Memory II: B-trees	1x1	External Memory II	Mandatory	Cache-oblivious B-trees, M. A. Bender, E. D. Demaine, and M. Farach-Colton, SICOMP 2005. A locality-preserving cache-oblivious dynamic dictionary, M. A. Bender, Z. Duan, J. Iacono, and J. Wu, Journal of Algorithms 2004. The results from the two papers are also partly covered in the lecture notes from last week.
Course Roundup, Questions, Future Perspectives

Frequently Asked Questions

How should I write my mandatory exercises? The ideal writing format for mandatory exercises is classical scientific writing, such as the writing found in the peer-reviewed articles listed as reading material for this course (not textbooks and other pedagogical material). One of the objectives of this course is to practice and learn this kind of writing. A few tips:

Write things directly: Cut to the chase and avoid anything that is not essential. Test your own writing by answering the following question: “Is this the shortest, clearest, and most direct exposition of my ideas/analysis/etc.?”
Add structure: Don’t mix up description and analysis unless you know exactly what you are doing. For a data structure explain following things separately: The contents of the data structure, how to build it, how to query/update it, correctness, analysis of space, analysis of query/update time, and analysis of preprocessing time. For an algorithm explain separately what it does, correctness, analysis of time complexity, and analysis of space complexity.
Be concise: Convoluted explanations, excessively long sentences, fancy wording, etc. have no in place scientific writing. Do not repeat the problem statement.
Try to avoid pseudocode: Generally, aim for human readable description of algorithms that can easily and unambiguously be translated into code.
Examples for support: Use figures and examples to illustrate key points of your algorithms and data structures.

How much do the mandatory exercises count in the final grade? The final grade is an overall evaluation of your mandatory exercise and the oral exam combined. Thus, there is no precise division of these part in the final grade. However, expect that (in most cases, and under normal circumstances) the mandatory exercises account for a large fraction of the final grade.

Can I write my assignments in Danish? Ja. Du er meget velkommen til at aflevere på dansk.

What do I do if I want to do a MSc/BSc thesis or project in Algorithms? Great! Algorithms is an excellent topic to work on :-) and Algorithms for Massive Data Sets is designed to prepare you to write a strong thesis. Some basic tips and points.

Let us know well in advance: Identifying an interesting problem in algorithms that matches your interest can take time. With enough time to go over the related litterature and study up on relevant topics your project will likely be more succesful. It may also be a good idea to do an initial “warm up” project before a large thesis to test ideas or survey an area.
Join the community: It is very good idea to enter the local algorithms community at DTU and the Copenhagen area to get a feel for what kind of stuff you could work on for your thesis and what thesis work algorithms is about. Talk to other students doing thesis work in algorithms. Go to algorithms talks and thesis defenses in algorithms.
Collaborate: We strongly encourage you to do your thesis in pairs. We think that having a collaborator to discuss with greatly helps in many aspects of thesis work in algorithms. Our experience confirms this.
No strings attached. Choosing a topic for your thesis is important. You are welcome to discuss master thesis topics with us without pressure to actually write your thesis in algorithms. We encourage you to carefully select your topic.