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f | 1 | { | f | 1 | { |
2 | "author": "Ordoni, Elaheh", | 2 | "author": "Ordoni, Elaheh", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 4 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
5 | "doi": "10.35097/1298", | 5 | "doi": "10.35097/1298", | ||
6 | "doi_date_published": "2023", | 6 | "doi_date_published": "2023", | ||
7 | "doi_publisher": "", | 7 | "doi_publisher": "", | ||
8 | "doi_status": "True", | 8 | "doi_status": "True", | ||
9 | "extra_authors": [ | 9 | "extra_authors": [ | ||
10 | { | 10 | { | ||
11 | "extra_author": "Bach, Jakob", | 11 | "extra_author": "Bach, Jakob", | ||
12 | "orcid": "0000-0003-0301-2798" | 12 | "orcid": "0000-0003-0301-2798" | ||
13 | }, | 13 | }, | ||
14 | { | 14 | { | ||
15 | "extra_author": "Fleck, Ann-Katrin", | 15 | "extra_author": "Fleck, Ann-Katrin", | ||
16 | "orcid": "0000-0001-8842-8906" | 16 | "orcid": "0000-0001-8842-8906" | ||
17 | } | 17 | } | ||
18 | ], | 18 | ], | ||
19 | "groups": [], | 19 | "groups": [], | ||
20 | "id": "bb15eb7b-aaf3-4324-8f1a-9264f7b4b19a", | 20 | "id": "bb15eb7b-aaf3-4324-8f1a-9264f7b4b19a", | ||
21 | "isopen": false, | 21 | "isopen": false, | ||
22 | "license_id": "CC BY 4.0 Attribution", | 22 | "license_id": "CC BY 4.0 Attribution", | ||
23 | "license_title": "CC BY 4.0 Attribution", | 23 | "license_title": "CC BY 4.0 Attribution", | ||
24 | "metadata_created": "2023-08-04T08:50:29.080516", | 24 | "metadata_created": "2023-08-04T08:50:29.080516", | ||
t | 25 | "metadata_modified": "2023-08-04T08:53:27.663320", | t | 25 | "metadata_modified": "2023-08-04T09:04:09.339729", |
26 | "name": "rdr-doi-10-35097-1298", | 26 | "name": "rdr-doi-10-35097-1298", | ||
27 | "notes": "Abstract: These are the experimental data for the | 27 | "notes": "Abstract: These are the experimental data for the | ||
28 | paper\r\n\r\n> Ordoni, Elaheh, Jakob Bach, and Ann-Katrin Fleck. | 28 | paper\r\n\r\n> Ordoni, Elaheh, Jakob Bach, and Ann-Katrin Fleck. | ||
29 | \"Analyzing and Predicting Verification of Data-Aware Process | 29 | \"Analyzing and Predicting Verification of Data-Aware Process | ||
30 | Models--A Case Study With Spectrum Auctions\"\r\n\r\npublished by | 30 | Models--A Case Study With Spectrum Auctions\"\r\n\r\npublished by | ||
31 | [*IEEE Access*](https://ieeeaccess.ieee.org/) in 2022.\r\nYou can find | 31 | [*IEEE Access*](https://ieeeaccess.ieee.org/) in 2022.\r\nYou can find | ||
32 | the paper [here](https://www.doi.org/10.1109/ACCESS.2022.3154445) and | 32 | the paper [here](https://www.doi.org/10.1109/ACCESS.2022.3154445) and | ||
33 | the code | 33 | the code | ||
34 | (https://github.com/Jakob-Bach/Analyzing-Auction-Verification).\r\nSee | 34 | (https://github.com/Jakob-Bach/Analyzing-Auction-Verification).\r\nSee | ||
35 | the `README` for details.\r\n\r\nFrom the raw experimental data, we | 35 | the `README` for details.\r\n\r\nFrom the raw experimental data, we | ||
36 | also extracted and pre-processed a smaller dataset that is suitable | 36 | also extracted and pre-processed a smaller dataset that is suitable | ||
37 | for training prediction models.\r\nThis prediction dataset is | 37 | for training prediction models.\r\nThis prediction dataset is | ||
38 | available under the name `Auction Verification` in the [UCI Machine | 38 | available under the name `Auction Verification` in the [UCI Machine | ||
39 | Learning | 39 | Learning | ||
40 | ta.ics.uci.edu/ml/datasets/auction+verification).\r\nTechnicalRemarks: | 40 | ta.ics.uci.edu/ml/datasets/auction+verification).\r\nTechnicalRemarks: | ||
41 | These are the experimental data for the paper\r\n\r\n> Ordoni, Elaheh, | 41 | These are the experimental data for the paper\r\n\r\n> Ordoni, Elaheh, | ||
42 | Jakob Bach, and Ann-Katrin Fleck. \"Analyzing and Predicting | 42 | Jakob Bach, and Ann-Katrin Fleck. \"Analyzing and Predicting | ||
43 | Verification of Data-Aware Process Models -- a Case Study with | 43 | Verification of Data-Aware Process Models -- a Case Study with | ||
44 | Spectrum Auctions\"\r\n\r\nCheck our [GitHub | 44 | Spectrum Auctions\"\r\n\r\nCheck our [GitHub | ||
45 | ository](https://github.com/Jakob-Bach/Analyzing-Auction-Verification) | 45 | ository](https://github.com/Jakob-Bach/Analyzing-Auction-Verification) | ||
46 | for the code and instructions to reproduce the experiments.\r\n\r\n- | 46 | for the code and instructions to reproduce the experiments.\r\n\r\n- | ||
47 | `result[0-5].csv`: The output of the iterative verification procedure, | 47 | `result[0-5].csv`: The output of the iterative verification procedure, | ||
48 | input to `prepare_dataset.py` (which pre-processes and consolidates | 48 | input to `prepare_dataset.py` (which pre-processes and consolidates | ||
49 | the dataset).\r\n- `auction_verification_large.csv`: The output of | 49 | the dataset).\r\n- `auction_verification_large.csv`: The output of | ||
50 | `prepare_dataset.py` (consolidated dataset), input to | 50 | `prepare_dataset.py` (consolidated dataset), input to | ||
51 | `run_experiments.py` (the experimental pipeline).\r\n- | 51 | `run_experiments.py` (the experimental pipeline).\r\n- | ||
52 | `prediction_results.csv`: The output of `run_experiments.py` (full | 52 | `prediction_results.csv`: The output of `run_experiments.py` (full | ||
53 | numeric experimental results), input to `run_evaluation.py` (which | 53 | numeric experimental results), input to `run_evaluation.py` (which | ||
54 | prints statistics and creates the plots for the paper).", | 54 | prints statistics and creates the plots for the paper).", | ||
55 | "num_resources": 0, | 55 | "num_resources": 0, | ||
56 | "num_tags": 4, | 56 | "num_tags": 4, | ||
57 | "orcid": "", | 57 | "orcid": "", | ||
58 | "organization": { | 58 | "organization": { | ||
59 | "approval_status": "approved", | 59 | "approval_status": "approved", | ||
60 | "created": "2023-01-12T13:30:23.238233", | 60 | "created": "2023-01-12T13:30:23.238233", | ||
61 | "description": "RADAR (Research Data Repository) is a | 61 | "description": "RADAR (Research Data Repository) is a | ||
62 | cross-disciplinary repository for archiving and publishing research | 62 | cross-disciplinary repository for archiving and publishing research | ||
63 | data from completed scientific studies and projects. The focus is on | 63 | data from completed scientific studies and projects. The focus is on | ||
64 | research data from subjects that do not yet have their own | 64 | research data from subjects that do not yet have their own | ||
65 | discipline-specific infrastructures for research data management. ", | 65 | discipline-specific infrastructures for research data management. ", | ||
66 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 66 | "id": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
67 | "image_url": "radar-logo.svg", | 67 | "image_url": "radar-logo.svg", | ||
68 | "is_organization": true, | 68 | "is_organization": true, | ||
69 | "name": "radar", | 69 | "name": "radar", | ||
70 | "state": "active", | 70 | "state": "active", | ||
71 | "title": "RADAR", | 71 | "title": "RADAR", | ||
72 | "type": "organization" | 72 | "type": "organization" | ||
73 | }, | 73 | }, | ||
74 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | 74 | "owner_org": "013c89a9-383c-4200-8baa-0f78bf1d91f9", | ||
75 | "private": false, | 75 | "private": false, | ||
76 | "production_year": "2022", | 76 | "production_year": "2022", | ||
77 | "publication_year": "2023", | 77 | "publication_year": "2023", | ||
78 | "publishers": [ | 78 | "publishers": [ | ||
79 | { | 79 | { | ||
80 | "publisher": "Karlsruhe Institute of Technology" | 80 | "publisher": "Karlsruhe Institute of Technology" | ||
81 | } | 81 | } | ||
82 | ], | 82 | ], | ||
83 | "relationships_as_object": [], | 83 | "relationships_as_object": [], | ||
84 | "relationships_as_subject": [], | 84 | "relationships_as_subject": [], | ||
85 | "repository_name": "RADAR (Research Data Repository)", | 85 | "repository_name": "RADAR (Research Data Repository)", | ||
86 | "resources": [], | 86 | "resources": [], | ||
87 | "services_used_list": "", | 87 | "services_used_list": "", | ||
88 | "source_metadata_created": "2023", | 88 | "source_metadata_created": "2023", | ||
89 | "source_metadata_modified": "", | 89 | "source_metadata_modified": "", | ||
90 | "state": "active", | 90 | "state": "active", | ||
91 | "subject_areas": [ | 91 | "subject_areas": [ | ||
92 | { | 92 | { | ||
93 | "subject_area_additional": "", | 93 | "subject_area_additional": "", | ||
94 | "subject_area_name": "Computer Science" | 94 | "subject_area_name": "Computer Science" | ||
95 | } | 95 | } | ||
96 | ], | 96 | ], | ||
97 | "tags": [ | 97 | "tags": [ | ||
98 | { | 98 | { | ||
99 | "display_name": "formal verification", | 99 | "display_name": "formal verification", | ||
100 | "id": "983d756d-3ad9-442e-8db2-751ce1b80f2c", | 100 | "id": "983d756d-3ad9-442e-8db2-751ce1b80f2c", | ||
101 | "name": "formal verification", | 101 | "name": "formal verification", | ||
102 | "state": "active", | 102 | "state": "active", | ||
103 | "vocabulary_id": null | 103 | "vocabulary_id": null | ||
104 | }, | 104 | }, | ||
105 | { | 105 | { | ||
106 | "display_name": "machine learning", | 106 | "display_name": "machine learning", | ||
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108 | "name": "machine learning", | 108 | "name": "machine learning", | ||
109 | "state": "active", | 109 | "state": "active", | ||
110 | "vocabulary_id": null | 110 | "vocabulary_id": null | ||
111 | }, | 111 | }, | ||
112 | { | 112 | { | ||
113 | "display_name": "model checking", | 113 | "display_name": "model checking", | ||
114 | "id": "f58f2b5f-29c1-4ead-a7ba-ce056dc5057c", | 114 | "id": "f58f2b5f-29c1-4ead-a7ba-ce056dc5057c", | ||
115 | "name": "model checking", | 115 | "name": "model checking", | ||
116 | "state": "active", | 116 | "state": "active", | ||
117 | "vocabulary_id": null | 117 | "vocabulary_id": null | ||
118 | }, | 118 | }, | ||
119 | { | 119 | { | ||
120 | "display_name": "spectrum auctions", | 120 | "display_name": "spectrum auctions", | ||
121 | "id": "06097327-05db-4b8b-84d1-51bf8b257b16", | 121 | "id": "06097327-05db-4b8b-84d1-51bf8b257b16", | ||
122 | "name": "spectrum auctions", | 122 | "name": "spectrum auctions", | ||
123 | "state": "active", | 123 | "state": "active", | ||
124 | "vocabulary_id": null | 124 | "vocabulary_id": null | ||
125 | } | 125 | } | ||
126 | ], | 126 | ], | ||
127 | "title": "Experimental data for the paper \"analyzing and predicting | 127 | "title": "Experimental data for the paper \"analyzing and predicting | ||
128 | verification of data-aware process models -- a case study with | 128 | verification of data-aware process models -- a case study with | ||
129 | spectrum auctions\"", | 129 | spectrum auctions\"", | ||
130 | "type": "vdataset", | 130 | "type": "vdataset", | ||
131 | "url": "https://doi.org/10.35097/1298" | 131 | "url": "https://doi.org/10.35097/1298" | ||
132 | } | 132 | } |