DecentralDC

Assessing Data Contribution under Decentralized Sharing and Exchanging BlockChain.

Introduction of Code

We provide the codes of main experiment for two scale scenarios. Below, we illustrate the code using Scenario #1 as an example (the code structure for Scenario #2 is identical to Scenario #1):

01_create_participant.py, 02_create_behavior_paradigm.py, 03_create_bs.py, and 04_simulate_parallel processing_bs.py are scripts for dataset construction, corresponding to Step 1 to Step 4 in Section 5.1.1.

05.1_construct_bx_paradigm_seq_set.py and 05.2_mine_max_seq_pattern.py correspond to Stage 1 of the proposed method, which involves mining maximal sequential patterns of behavior paradigms in two steps. Note that running 05.2_mine_max_seq_pattern.py requires the installation of the spmf algorithm package(https://pypi.org/project/spmf/) in the Python environment.

06_cal_bx_paradigm_weight.py corresponds to Stage 2 of the proposed method, which is to calculate the importance weights of behavior paradigms.

07_cal_DBC.py corresponds to Stage 3 of our method, which is to calculate the contribution of each database participanting in sharing and exchange combined with the amount of data.

evaluate_metrics.py is used to compute precision, recall, and F1 for the VMSP and CM-SPADE methods.

t-fcsm_evaluate_meters.py implements the t-FCSM method and calculates the precision, recall, and F1-score for this method.

Introduction of Data

We provide the datasets of main experiment for two scale scenarios. Below, we illustrate using Scenario #1 as an example (the situation for Scenario #2 is identical to Scenario #1):

bx_paradigm_info contains the defined behavior paradigms for the scenario.

bx_seq_dataset contains the sharing and exchanging behavior metadata sequence for the scenario. For ease of subsequent processing, we replace the provider_id and db_id with bx_paradigm_ID in the metadata information, and omit the data_hash field.

collaborative business refers to the collaborative businesses defined within the scenario.

We provide both txt and pkl formats of the files. The former facilitates readers to inspect the contents of the dataset, while the latter is intended for actual program processing.