THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
Candidate roadmap 2016:Some potential roadmap ideas for 2018+ below. For more details please see the JIRAs.
Predictive models
- Multi-class SVM
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1037 - Mixed effects modeling
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-987 - Gradient boosted machines
- Novelty detection using 1-class SVM MADLIB-990
- Mixed effects modeling MADLIB-987
- Factorization machines
- k-nearest neighbors (kNN) MADLIB-927
- Geographically Weighted Regression (GWR)
- MCMC Probit and Logit regression
- Gaussian Mixture Model using Expectation Maximization (EM) algorithm
- Multi-layer Perceptron
Graph
- Shortest path MADLIB-992
- Standard traversal
- depth first search
- breadth first search
- topological sort
- One mode projection (converting a bi-partitite graph of user-item graph to user-user or item-item graph)
- Connected components
- Page rank
- Hierarchical graph cut
- Between-ness centrality
- Minimum spanning tree
Utilities
- algorithm
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-410 - Hierarchical clustering
- k-NN improvements
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1061 Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1181 - Deep learning
Graph
- Personalized PageRank
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1084 - Betweenness centrality
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1121 - Graph cut
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1074 - Triangle counting
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1125 - Minimum spanning tree
- Eigenvector centrality
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1123 - APSP performance improvements
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1155
Utilities
- Balanced datasets
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1168
Summary - add more statisticsJira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1167 - Anonymization
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-911 - URI tools
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-910 - Path functions (phase 2) MADLIB-977
- Prediction metrics MADLIB-907
- Sessionization MADLIB-909
- Pivoting MADLIB-908
- Anonymization MADLIB-911
- URI tools MADLIB-910
- Stratified sampling MADLIB-986
Usability
- Expand coverage for PivotalR
- Expand coverage for PMML export
- Interface improvement and consistency Implement an interface using named parametersfor 2.0 release (does not need to be backward compatible)
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1157 - Python API
Performance and scalability
- Work around PostgreSQL 1 GB field size limit MADLIB-991
Platform
- Mini-batching
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1048 Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1037 Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1200 - Improve decision tree and random forest performance for run-time and memory usage
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1057 Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-976
Platforms and Frameworks
- PostgreSQL 10 support
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1185 - Support modern versions of gcc
Jira server ASF JIRA serverId 5aa69414-a9e9-3523-82ec-879b028fb15b key MADLIB-1025 - Tensorflow support, or another deep learning framework
- GPU supportSupport for PostgreSQL 9.5 and 9.6 MADLIB-944