Exploring Algorithms For Big Data Compsci 229r Lecture 3
Let's dive into the details surrounding Algorithms For Big Data Compsci 229r Lecture 3.
- Distinct elements, k-wise independence, geometric subsampling of streams.
- Hashing: load balancing, k-wise independence, chaining, linear probing.
- Competitive paging, cache-oblivious
- Analysis of ℓp estimation
- This is CS50, Harvard University's introduction to the intellectual enterprises of
In-Depth Information on Algorithms For Big Data Compsci 229r Lecture 3
Necessity of randomized/approximate guarantees, linear sketching, AMS sketch, p-stable sketch for p less than 2. P-stable sketch analysis, Nisan's PRG, ℓp estimation for p Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ... External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.
MapReduce: TeraSort, minimum spanning tree, triangle counting.
That wraps up our extensive overview of Algorithms For Big Data Compsci 229r Lecture 3.
