Big Bioinformatics without Big Hassles (November 2014) - Discover how to accelerate translational informatics research with SciDB — an open source computational DBMS that makes it easy to organize, integrate, select, and share ‘omics, variant, biomarker, and outcomes data. (~58 min)
Accelerate Translational Bioinformatics (July 2014) - See how SciDB lets you share, access, and analyze unlimited heterogeneous data. (~55 min)
Massively Scalable Quantitative Finance (July 2014) – SciDB benchmarks for order book creation and consolidation, crypto-currency network analysis (~45 min)
Bigger Analytics without Big Hassles (November 2013)- Demonstrating SciDB in a variety of use cases. (~1 hr)
SciDB for Bioinformatics and Healthcare Analytics (December 2013): Solving large-scale problems in bioinformatics and healthcare with SciDB. (~1 hr)
SciDB Tutorial from XLDB 2013 (October 2013): Paradigm4 architects Alex Poliakov and Paul Brown walk you through an interactive tutorial at XLDB 2013. (~3 hrs)
Time Series Data in SciDB: Paradigm4 presents SciDB-Py at PyData NYC 2013. (~28 min)
Big Analytics with SciDB-Py (August 2013): Paradigm4 and Continuum Analytics present a webinar about SciDB-Py, a Python package that expands the power of Python with SciDB, the massively scalable array database. (~1 hr)
Analyze More, Program Less: SciDB for Computational Finance (June 2013): Watch demos of SciDB performing common financial calculations at scale. (~1 hr)
Big Analytics with SciDB-R (April 2013): See how SciDB-R lets you remain an R programmer but expands its power with SciDB. (~1 hr)
Follow the Bitcoins
Paradigm4 Chief Data Scientist and R contributor Bryan Lewis discusses using SciDB to analyze Bitcoins transactions and find the most important network nodes. His FinDevR 2014 talk also touches on other awesome and more commonplace quantitative finance applications of SciDB—fast OHLC bar building, “as-of” imputations, GLM’s, large correlations, and PCAs.
Crypto-Currency and Massive Network Analysis
A New Database Paradigm For Complex Analytics
Database visionary Mike Stonebraker describes why SciDB is optimal for complex analytics on machine-generated data.
SciDB in the Lab
Yushu Yao of Lawrence Berkeley National Lab – NERSC shares his experiences using SciDB on terabytes of science data.