This document discusses using machine learning to automate tracking of data usage (DU) quality metrics. It outlines automating the collection of DU event data from multiple sources and storing it in a normalized database. Machine learning algorithms would then be implemented to predict DU events and identify contributing factors. Key steps completed include identifying relevant data sources and metrics, and setting up schemas to automatically update and pull data. Current work involves implementing predictive learning algorithms and refining the data extraction, transformation and loading processes. Challenges include dealing with nested hierarchical data, variable recording intervals, and changing features between releases.

1. Machine Learning and Quality Analytics
Ryan Riopelle, UC San Diego/CU Boulder
Engineering Intern, Database Management and Analytics Team, Solidfire
Automating DU Quality Tracking
Identify data sources and quality metrics that have been previously
monitored.
Automate data processes for recording and evaluating DU events.
Machine Learning and Predictive Analytics
Implement machine learning algorithms to Predict DU events.
Determine the key contributing factors involved with DU events
Group customer feature usage into patterns that be tracked.
Build a streaming pipeline for anomaly detection over customers usage
patterns.
Future Work
Methods
Milestones Achieved
Project Goals
Benefits and Challenges
Query
Data
K- Means
Clustering
Columns
Clustering Input: Group
using K-Means, Means
Shift, Affinity Propagation,
Spectral Clustering
Use Cluster Variance Analysis
to Determine N-Groups
TF-IDF Vectorizer Python Dictionary
(IDF) Inverse Document Frequency
weighting
Clustered
Categorical
Data
Split Data
Test
Data
Training
Data
Out to
Streaming
Analysis
ML Classification: Ada Boosting, Boosted Trees,
Support Vector Machines (SVM), Neural Networks
(Multi-layer Perceptron), Stochastic Gradient Descent
(SGD)
Optimization Tuning:
Bagging, Ensembles,
Boosting, Changing
Kernel Functions,
Changing Learning Rate/
Step Size Parameter,
Loss/Error Function
Benefits
Improve analytics by reducing the time that it takes to manually query
each data source.
Reduce overhead by reducing time that it takes to manually query each
data source.
Normalize data process for consistent and reliable analytics.
Identifying a consistent set of (KPI’s) companywide.
Challenges
Dealing with highly nested hierarchical data.
Variable time intervals for metrics record recorded by collectors.
Constantly changing features associated with different element releases.
Dealing with effects of multicollinearity across data.
AIQ
System design, operations and data management.
Operations
Data management best practices
Monitoring and alerting
Disaster Recovery
Implementation of replication and backup practices for
critical business systems. AIQ, AT2 and DMA.
Support
Move from Reactive to Predictive model for DU/DL
Engineering
Analytics tools for better resource management and
identification of potential problems.
.
Automating DU Quality Tracking
Identify data sources and quality metrics previously monitored.
 Completed–
 Identify schema structure for database and machine learning.
 Setup schemas with automated updates using Cron and SQL.
 Automated data store with information automatically pulled in from
AIQ, Salesforce, Jira, and Fogbugs.
 Currently Working On–
 Implement Predictive Learning Algorithms
 Measure performance for input fields related to DU events
 Fine tune extraction, translation and loading (ETL) pipeline
.
Data Sources
Normalized
Data Store
Data Extraction,
Translation, and Loading
Automating DU Tracking
Building python connections using SQL Alchemy and PYSH 2 connectors.
Import NoSQL Data into MySQL Database schema, normalize, and perform ETL
processes.
Provide visualizations and regularly timed export options for tracking key performance
indicators (KPI’s).
Algorithms and TrainingData Management Life Cycle Dealing With Textual Data