v3.0 [+EMeriTAte with Python bindings]

This version is associated with the following paper:

G. Bergami, Emma Packer, Kirsty Scott, Silvia Del Din. "Predicting Dyskinetic Events through Verified Multivariate Time Series Classification". IDEAS 2024

Differently from version v2.9, where the pipeline was scattered throughout different invocations of Python and C++ code, this version wraps the minimum constituents of KnoBAB for EMeriTAte via pybind11, while preserving the original C++ codebase in C++.

After cloning this repository, you can easily install the Python bindings for the EMeriTAte classifier using pip3 as follows:

pip3 install EMeriTAte/

The present codebase provides an example for using the wrapper, as well as the legacy version of the pipeline.

Legacy pipeline

The legacy pipeline considered originally for the paper included the following steps:

1. Raw data pre-processing in Python, plus DT mining -- Python

2. Specification mining and SAT checking (also phase 4) -- C++

3. Specification crawling for preparing the SAT phase across logs -- Python

4. SAT checking (see Phase N°2)

5. Competitor testing over the same dataset

v2.9

This version is associated with the following paper:

G. Bergami, Emma Packer, Kirsty Scott, Silvia Del Din. "Predicting Dyskinetic Events through Verified Multivariate Time Series Classification". IDEAS 2024

Please refer to version v3.0 for the improved codebase that provides an easy-access Python wrapper over the C++ codebase.

v2.3

This version is associated to the following paper:

G. Bergami "Streamlining Temporal Formal Verification over Columnar Databases". Information. 2024. (Dataset [1,2])

This repository is associated with a Dockerfile. Therefore, a possible way to start in from a Linux environment is the following:

docker build -t "conference:DockerConference" .
docker run -it "conference:DockerConference" bash

The docker file will also automatically run the tests associated to the programs. As suggested in the paper, please also consider running the
given code using the dataset and scripts provided online via OSF.

The updated version of FoodBroker for generating logs from temporal graphs is available here: https://github.com/jackbergus/foodbroker/releases/tag/v1.1

v2.2

This is the version associated to the current paper:

G. Bergami, S. Appleby, G. Morgan. “Specification Mining over Temporal Data". Computers. 2023; 12(9):185. (Source,Dataset 1,2)

The current release provides the following upgrades:

• Implementation of the Bolt2 algorithm as an enhancement of our previous Bolt implementation.

v2.0.6

This version of the code is the one associated to the current paper:

G. Bergami. “Fast Synthetic Data-Aware Log Generation for Temporal Declarative Models”. In GRADES & NDA’23: Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), June 18, 2023, Seattle, WA, USA. (Source Code, Dataset, Slides)

All the subsequent delta-improvement might be found at the following branch: GRADESNDA23

v2.0.5

Preliminary version to the IDEAS'23 paper

v2.1

This is the version associated to the current paper:

• S. Appleby, G. Bergami, G. Morgan. “Enhancing Declarative Temporal Model Mining in Relational Databases: A Preliminary Study". In International Database Engineered Applications Symposium (IDEAS’23), May 5-7, 2023, Heraklion, Crete, Greece. ACM, New York, NY, USA. (Source Code, Dataset, Paper Source, Slides)

The current release provides the following upgrades:

• Implementation of the Bolt algorithm for dataless declarative mining.
• Re-implementation of the following paper on top of KnoBAB:

  Maggi, F.M., Bose, R.P.J.C., van der Aalst, W.M.P. "Efficient Discovery of Understandable Declarative Process Models from Event Logs". In: CAiSE 2012

• Rendition of the TopN Declare mining algorithm on top of the novel pipeline

The re-implementation of the Deviance Learning algorithm is available on this twin Python repository: ModelMining

v1.2

Just for the sake of clarity, changing the paths for the tests to make them run successfully.

Final Journal Paper Version

G. Bergami, S. Appleby, G. Morgan. “Quickening Data-Aware Conformance Checking through Temporal Algebras". Information. 2023; 14(3):173. (Preprint, Source Code, Dataset)

v1.1

Preprint Paper Version

G. Bergami, S. Appleby, G. Morgan. “Quickening Data-Aware Conformance Checking through Temporal Algebras". (Preprint, Source Code, Dataset)

Major contributions for this release:

• Full Query Plan Parallelisation
• Improved xtLTLf operators' algorithms

The associated dataset is available via OSF.

v1.0

This is the version associated to the current paper:

• S. Appleby, G. Bergami, G. Morgan. “Running Temporal Logical Queries on the Relational Model". In International Database Engineered Applications Symposium (IDEAS’22), August 22–24, 2022, Budapest, Hungary. ACM, New York, NY, USA, 10 pages. (Source Code, Dataset, Slides)