Skip to content
No description, website, or topics provided.
C++ Python CMake
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
include
src
.gitignore
CMakeLists.txt
README.md
benchmark.py
run_me.py

README.md

Multidimensional Adaptive Indexing (MDAI)

In this stand-alone implementation, we present various MDAI approaches and compare them to state of the art indexing strategies for multidimensional data through several different benchmarks.

Algorithms

How to run

Run the following command on the root of the project, it will guide you through selecting the experiment and algorithms to run. Then it will provide instructions on what to do next.

python3 run_me.py

To Do

  • Data Generator Benchmark
  • Workload Generator Benchmark
  • Implement Full Scan
  • Implement Testing procedures
  • Implement Cracking KD-Tree Broad
  • Make both KD-Trees update their statistics (height, number of nodes, min_height)
  • Implement Cracking KD-Tree Narrow (Need to fix bugs and improve its speed)
  • Implement KD-Tree with median
  • Implement KD-Tree with average
  • Implement Quasii
  • Implement Full Index B-Tree
  • Implement Regular Cracking using B-Tree
  • Implement a Row-Store Table to check if they have difference
  • Check "Bitwise dimensional co-clustering for analytical workloads"

Papers

  • Cracking KD-Tree: The First Multidimensional Adaptive Indexing (Position Paper). P. Holanda, M. Nerone, E. C. de Almeida and S. Manegold @ DATA 2018
You can’t perform that action at this time.