Local causal structure learning aims to discover and distinguish the direct causes and direct effects of a target variable. However, the state-of-the-art algorithms for local causal structure learning fail to perform well when dealing with missing data. The general approach is to fill in the missing data using imputation techniques before learning the local causal structure, but this method suffers from problems such as low accuracy, low efficiency, and instability. To address these issues, we propose a novel method for local causal structure learning with missing data, named misLCS. Firstly, we design an iterative data imputation method to obtain the complete and correct data from the missing data. Then, misLCS adopts a data subset strategy to get a data subset that variables are closely related to the target variable. Thirdly, within this data subset, misLCS constructs the local causal skeleton of the target variable using a mutual information-based feature selection method and orients the direction of edges using conditional independence tests and Meek rules. Finally, misLCS updates the missing data in preparation for the next iteration. This procedure continues until the direct causes and direct effects of the target variable have been identified. Our experiments on seven benchmark Bayesian networks and a real-world bioinformatics dataset, with a number of variables from 11 to 801, demonstrate that our algorithm achieves better accuracy than the existing local causal structure learning algorithms.

This study proposes a novel Approximately Balanced Tree Partitioning Algorithm (TPA) to overcome the significant challenges in genealogical data management, encompassing the storage, maintenance, and interpretation of complex familial networks. Our TPA is adept at modularizing and simplifying intricate relationships in genealogical graphs into logically succinct tree structures, reducing user cognitive load and enhancing the utility of genealogical data in real applications like hereditary disease research, forensic investigation, and consanguinity counseling. In addition, TPA prioritizes structural closeness in partitioning to avoid misleading insights from unrelated data points and maintain a balance of node distribution to prevent workload and communication overheads in distributed graph data processing systems. The effectiveness of our algorithm is demonstrated through extensive experiments on four real-world genealogical datasets, substantiating its superiority over five state-of-the-art rival models in dealing with the complex and rapidly expanding landscape of genealogical data.

A data central platform positions the data of an entity-be it a corporate entity, institutional body, or governmental department-as a pivotal strategic asset.It's a management mechanism that spans from data collection to processing and application, aiming to improve data quality, achieve extensive data sharing, and ultimately maximize the value of the data.A definition for data central-platforms was provided, and a generic architecture was presented along with the core technologies and functions of physical management, logical management, data asset management, data services and information security management.Finally, taking the construction of Huapu system as an example, a realization of the data central platform, which is geared towards genealogical big data and integrated with the HAO intelligence model, was introduced-Huapu Central-Platform.

数据中台将一个机构(企业、事业或政府部门)的数据作为战略资产进行管理,是从数据收集到处理应用的一套管理机制,以期提高数据质量,实现广泛的数据共享,最终实现数据价值最大化.给出数据中台的定义,提出数据中台参考技术框架,并分别对物理管理、逻辑管理、数据资产管理、数据服务和信息安全管理的组成和技术进行了展开讨论.最后以华谱系统建设为例,介绍面向家谱大数据、结合HAO智能模型的数据中台实现——华谱数据中台.

针对碎片化的各姓氏家谱数据,华谱系统通过构建家谱知识图谱的数据中台,能够解决数据孤岛、烟囱式开发等问题.“数据中台”是一个源自国内的新近技术概念,在华谱系统建设中,我们通过家谱知识图谱的构建和应用,对这个概念进行了正式定义.基于这个定义和对应的7项核心功能,本文提出一种用于家谱数据分析的数据中台建设架构Huapu-CP(华谱系统),并通过该架构详细介绍面向家谱领域的数据中台核心技术,分析数据中台构建的关键问题.

As a widely used data structure, graphs are good at characterizing data with internal associations, such as social and biological data. Tree structured data are special and are widely used in many real-world applications, such as organizational structure analysis and genealogical knowledge graph reasoning. For example, in kinship knowledge graph analysis, when a genealogical tree is particularly large (more than 25 levels and 45,000 nodes), it is a great challenge to partition this large tree into a specified number of subtrees with succinct logic and a balanced number of nodes. Therefore, in this paper, we propose the TPA (tree partitioning algorithm) algorithm to achieve a balanced and succinct logic partition of large-scale tree structured data. TPA first extracts all related nodes from a massive graph database and then constructs the convergent subgraph into a complete tree with a specified root node. Specifically, several virtual nodes are supplemented for generation-skipping connected nodes to achieve correct node numbering and partitioning. Finally, a graph partitioning algorithm is executed on the complete tree to obtain a specified number of subtrees with succinct logic and balanced node scales. Experiments conducted on four real-world datasets verify the effectiveness of our TPA algorithm.

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.