Jeremy Kepner talked about his newly released book, "Mathematics of Big Data," which serves as the motivational material for the D4M course.

Lecture: Mathematics of Big Data and Machine Learning

Introduction to signal processing applied to graphs. Course outline. Discussion of relevant technologies programming and storage technologies. Constructing a graph from raw data.

Lecture 0: Introduction

D4M.mit.edu software demo example/1Intro/2EdgeArt. Adjacency matrix construction. Incidence matrix construction.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/1Intro/2EdgeArt. Adjacency matrix construction. Incidence matrix construction.

Lecture 1: Examples Demonstration

Historical evolution of the web and cloud computing. Using the exploded (D4M) schema. Analyzing computer network data. Analyzing computer network data.

Lecture 3: Entity Analysis in Unstructured Data

Computing statistics and analytics on data in the exploded (D4M) schema.

Lecture 4: Analysis of Structured Data

D4M.mit.edu software demo examples/2Apps/4BioBlast. Distribution of genetic sample data. Ingesting genetic data into an associative array using the exploded (D4M) schema. Correlating genetic data via associative array multiplication.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/2Apps/4BioBlast. Distribution of genetic sample data. Ingesting genetic data into an associative array using the exploded (D4M) schema. Correlating genetic data via associative array multiplication.

Lecture 6: Examples Demonstration

D4M.mit.edu software demo example/2Apps/1EntityAnalysis. Incidence array of text entities. Computing the entity degree distribution.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/2Apps/1EntityAnalysis. Incidence array of text entities. Computing the entity degree distribution.

Lecture 3: Examples Demonstration

D4M.mit.edu software demo examples/3Scaling/1KroneckerGraph. Generation of power law graphs via Kronecker products. D4M.mit.edu software demoexamples/3Scaling/3MatrixPerformance. Measuring the performance of array multiplication.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/3Scaling/1KroneckerGraph. Generation of power law graphs via Kronecker products. <a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demoexamples/3Scaling/3MatrixPerformance. Measuring the performance of array multiplication.

Lecture 7: Examples Demonstration

D4M.mit.edu software demo examples/3Scaling/2ParallelDatabase. MIT SuperCloud database management system. Starting the Apache Accummulo database. High performance database ingest. Using the D4M schema.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/3Scaling/2ParallelDatabase. MIT SuperCloud database management system. Starting the Apache Accummulo database. High performance database ingest. Using the D4M schema.

Demonstration 7

Genectic sequence analysis using associative arrays. Creating of a genetic processing pipeline. Ingest of genetic sample data into a database. Sub-sampling of data. Correlation of genetic samples using associative array multiplication.

Lecture 6: Bio Sequence Cross Correlation

Theory of Kronecker graphs. Database ingest performance and database query performance. Array multiplication performance.

Lecture 7: Kronecker Graphs, Data Generation, and Performance

D4M.mit.edu software demo examples/2Apps/3PerfectPowerLaw. Generate synthetic power law distributions.  Analyze power law distributions in real data.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo examples/2Apps/3PerfectPowerLaw. Generate synthetic power law distributions.  Analyze power law distributions in real data.

Lecture 5: Examples Demonstration

Statistical distribution of background/noise in databases. Power law distribution describes many backgrounds. Perfect power law distribution can be used to bin and model the background data.

Lecture 5: Perfect Power Law Graphs -- Generation, Sampling, Construction, and Fitting

Associative array mathematics. Relevant operations on an associative array. Semirings and matrices. See MIT Press book "Mathematics of Big Data."

Lecture 2: Group Theory

D4M.mit.edu software demo example/1Intro/3GroupTheory. Associativity, commutativity, distributivity properties.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/1Intro/3GroupTheory. Associativity, commutativity, distributivity properties.

Lecture 2: Examples Demonstration

Creating an exploded database schema.  Standard database processing chain. Graph adjacency matrix.  Vertex degree distribution. Directed graphs, multi-graphs, and hyper-graphs. Graph incidence matrix.

Lecture 1: Using Associative Arrays

D4M.mit.edu software demo example/2Apps/2TrackAnalysis. Tracking entities through space and time.

<a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/2Apps/2TrackAnalysis. Tracking entities through space and time.

Lecture 4: Examples Demonstration

Introduction to associative arrays. D4M.mit.edu software demo example/1Intro/1AssocIntro. Creating, writing, reading, selecting, and performing math on associative arrays.

Introduction to associative arrays. <a href="http://D4M.mit.edu" rel="nofollow" target="_blank">D4M.mit.edu</a> software demo example/1Intro/1AssocIntro. Creating, writing, reading, selecting, and performing math on associative arrays.

Lecture 0: Examples Demonstration

D4M: Signal Processing on Databases

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