$ brew install monetdb
$ pip3 install numpy
$ git clone https://github.com/eXascaleInfolab/2018-RecovDB.git recovdb
$ cd recovdb/
$ sh createdb.sh
-
Install Anaconda2 from: https://docs.anaconda.com/anaconda/install/ in your 'HOME' folder
-
Add the following line to (.profile or .bash_profile):
export PYTHONPATH="${PYTHONPATH}:'HOME'/anaconda2/lib/python2.7/site-packages/"
- Execute and restart:
$ source .profile (or source bash_profile)
$ sudo shutdown -r now
$ git clone https://github.com/eXascaleInfolab/2018-RecovDB.git recovdb
$ cd recovdb/
$ sh monetdb_install.sh
$ sh createdb.sh
We show how to recover overlapping missing blocks in two climate time series located in recovery/input/original.txt
$ sh connectdb.sh
sql> \<./recov_udf.sql
sql> \q
We show how to decompose a matrix of time series located in decomposition/input/climate.csv
$ sh connectdb.sh
sql> \<./decomp_udf.sql
sql> \q
To add a dataset to the recovery:
- Name your file
original.txt
and add it torecovery/input/
- Requirements: columns= 4, column separator: empty space, row separator: newline
To add a dataset to the decomposition :
- Name your file
climate.csv
and add it todecomposition/input/
- Requirements: column separator: empty space, row separator: newline
RecovDB is also avilable as a GUI here.
Please cite the following paper when using RecovDB:
@inproceedings{arous2019recovdb,
title={RecovDB: Accurate and Efficient Missing Blocks Recovery for Large Time Series},
author={Arous, Ines and Khayati, Mourad and Cudr{\'e}-Mauroux, Philippe and Zhang, Ying and Kersten, Martin and Stalinlov, Svetlin},
booktitle={2019 IEEE 35th International Conference on Data Engineering (ICDE)},
pages={1976--1979},
year={2019},
organization={IEEE}
}