1. Big Data Analytics in Parallel Systems
Big Data Analytics: Data Mining + parallelism + text
Machine learning models
Graph algorithms
Search engine technology
Why perform analytics inside a parallel DBMS?
Queries, speed, user/space management, consistency
Security, fault tolerance, concurrency
How?
CS: Scalable algorithms, external data structures, relational algebra, SQL query optimization, UDFs
Programming: C++, C, Java, Unix
Math: linear algebra, graphs, numerical methods
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Our motivation: Databases are getting larger, most data mining algorithms work on flat files, data coming in/out of the DBMS is time-consuming and error-prone.
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Contributor: C. Ordonez 1

2. Recent projects
Percentage cubes for DSS
Graph analytics: beating Spark
PCA with multicore CPUs
R on streaming network data
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Our research coverd the entire spectrum of data mining, going from exploratory OLAP analysis up to predictive models.
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Contributor: C. Ordonez 2

3. Why we are different Parallel analytics on big data Applications:
Dimensionality reduction (PCA, factor analysis)
Classification, regression, time series, histograms
Graphs (page rank, reachability, clique detection)
Patterns (association rules, OLAP cubes)
Applications:
Corporate databases and data lakes
Medical: microarray data, heart, cancer diseases
Network data, files, documents
Expertise on both
Parallel Data Systems
Machine learning, advanced statistics, graphs
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We already have a set of fundamental algorithms working on several public and commercial DBMSs; our application areas are mainly biomedical, but can be applied anywhere where there is a large database that needs to analyzed.
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Contributor: C. Ordonez 3