本数据集包含蜂窝通信网络产生的告警事件序列数据。每条告警记录包含告警事件类型编号、告警发生时间戳和产生告警的网络元素节点编号三个要素。相较于同类仿真生成的事件序列数据，本数据集来源于真实通信网络运维场景，具有告警风暴、级联传播、拓扑依赖等真实复杂特性，主要应用于事件类型因果关系推断、网络拓扑依赖建模、根因告警定位、告警级联传播分析，可为大规模通信网络智能运维和故障根因定位研究提供真实数据基础。
", "ds_source": "本数据集基于论文中的博弈模型通过计算机仿真生成。
", "ds_process_way": "采用Pandas进行告警事件记录读取、时间戳解析与排序、告警事件类型编码和重复记录核查；采用NumPy进行事件序列数组构建、网络拓扑邻接矩阵处理、事件间时间间隔计算、数值归一化以及模型输入张量转换。
", "ds_quality": "满足相应论文中博弈模型的建模条件
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "无特别要求", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 1257470, "ds_files_count": 0, "ds_format": "CSV", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "12ca6b85-f2eb-424c-a9ad-b85634277bbd.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "结合论文中博弈模型使用。", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "莫远秋", "ds_serv_phone": "13216110989", "ds_serv_mail": "yuanqiumo@seu.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:04", "last_updated": "2026-07-09 10:58:04", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "", "ds_format": "CSV", "ds_source": "This dataset is generated through computer simulation based on the game model presented in the paper.", "ds_quality": "Meet the modeling conditions of the game model in the corresponding paper", "ds_ref_way": "", "ds_abstract": "This dataset contains sequence data of alarm events generated by cellular communication networks. Each alarm record contains three elements: alarm event type number, alarm occurrence timestamp, and network element node number that generated the alarm. Compared with event sequence data generated by similar simulations, this dataset comes from real communication network operation and maintenance scenarios, with real and complex characteristics such as alarm storms, cascading propagation, and topological dependencies. It is mainly used for inferring causal relationships of event types, modeling network topological dependencies, root cause alarm localization, and analyzing alarm cascading propagation. It can provide a real data foundation for intelligent operation and maintenance of large-scale communication networks and research on fault root cause localization.", "ds_time_res": "", "ds_acq_place": "No special requirements", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Pandas was used for loading alarm event records, parsing and sorting timestamps, encoding alarm event types and checking duplicate records; NumPy was used for constructing event sequence arrays, processing the topology adjacency matrix, computing inter-event time intervals, numerical normalization and converting data into model input tensors.", "ds_ref_instruction": "Use in conjunction with the game model presented in the paper." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "事件序列", "因果结构学习", "根因定位" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "莫远秋", "email": "yuanqiumo@seu.edu.cn", "work_for": "东南大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "莫远秋", "email": "yuanqiumo@seu.edu.cn", "work_for": "东南大学", "country": "中国" } ], "ds_managers": [ { "true_name": "莫远秋", "email": "yuanqiumo@seu.edu.cn", "work_for": "东南大学", "country": "中国" } ], "category": "其他" }