1. Parallel Analytic Systems
Carlos Ordonez 1

2. Big Data Analytics research
Input? BIG DATA (large data sets, large files, many documents, many tables, fast growing)
How? Fast external algorithms; memory-efficient data structures at two storage levels.
Parallel: multi-threaded or multi-node
Ideal goals: linear time O(n), linear speedup
Hardware? Multicore CPU or parallel cluster
Infrastructure? Distributed RAM, parallel file system; any large files
Challenging: Theory+programming in action

3. Systems research today
Transaction processing? Main memory, lock-free
Efficient analysis? joins, compiled queries, streams, exploit ample RAM, multi-core, leverage R/ScaLAPACK
Compiler versus interpreter?
Massive storage? Posix vs HDFS
Fast external algorithms? Simple tasks.
Parallel computation? Multi-core with threads, Shared-nothing (embedded message-passing)
Exploiting new hardware? Interesting,difficult,customized
Analytics: queries, cubes, statistics, Machine learning

4. DB Systems involves Core CS research: Theory+Programming
Theory we use:
Time complexity (big O()) and I/O cost (disk, solid state memory)
Many data structures (arrays, trees, hash tables, linked lists)
Relational model and information retrieval models
Linear algebra
Multivariate statistics, machine learning models
Compilers and programming languages: parsing/compiling/optimizing code; recursion
Programming:
Languages: C++ and Java combined with R, SQL, Scala
Systems: parallel DBMSs, Spark
OS: Unix, but we have a lot of past work on MS Windows
Libraries: MPI, R
Systems aspects: Threads, binary I/O, parallel file systems, code generation, code optimization, interpreter runtime..a lot of fun.

5. Typical Problems
Summarization for linear models: vector outer products
Exploration: cubes, lattices, ad-hoc queries
Graph transitive closure (linear recursion), clique enumeration
Bayesian models: MCMC, classification, regression, variable/feature selection
(read slide title)
Our research coverd the entire spectrum of data mining, going from exploratory OLAP analysis up to predictive models.
(then read the titles of each application) 5

6. Why join the parallel DBMS group?
Balance between theory (mathematics) and programming (C++)
Mature and stable CS research area
Great interaction between industry and academic research
Always looking for motivated students
Visit my web page, DBLP. Google “Ordonez SQL”, stop by