基于数据中台的高校数据服务体系及快速应用构建研究

针对目前高校在数据集成、数据治理和数据服务等方面的问题,提出了基于数据中台的解决方案,在数据的采集和加工处理中引入数据湖、主题层和专题层,并对数据中台的整体架构设计、中台内数据的分层设计、数据模型的构建、数据服务体系的设计进行了详细阐述;对基于数据中台的快速应用构建,提出了采用大中台、微服务的系统建设方式,通过对系统进行解构,重新根据不同业务逻辑进行模块化组装,实现快速灵活构建业务系统。经过实际案例验证,该研究对于高校如何建设数据中台以及如何在中台上开展各类数字应用建设提供了很好的范例。     

碎片化家谱数据的融合技术

家谱数据是典型的碎片化数据,具有海量、多源、异构、自治的特点.通过数据融合技术将互联网中零散分布的家谱数据融合成一个全面、准确的家谱数据库,有利于针对家谱数据进行知识挖掘和推理,从而为用户提供姓氏起源、姓氏变迁和姓氏间关联等隐含信息.在大数据知识工程BigKE模型的基础上,提出了一个结合HAO智能模型的碎片化数据融合框架FDF-HAO （fragmented data fusion with human intelligence,artificial intelligence and organizational intelligence）,阐述了架构中每层的作用、关键技术和需要解决的问题,并以家谱数据为例,验证了该数据融合框架的有效性.最后,对碎片化数据融合的前景进行展望.     

数据中台架构设计与应用研究

随着企业数字化转型升级的不断深入,数据成为企业的一项重要资产,挖掘数据价值将成为企业的重要发展战略。数据中台能够整合企业内外部数据资源,沉淀企业数据资产,通过数据共享、应用等提升企业数据价值。介绍了数据中台的含义、架构和建设策略,以及数据中台在企业中的应用实践。     

关于高校数据中台的建设方法研究

针对目前国内部分高校存在数据孤岛、碎片化和数据资产利用率低等数据管理问题,提出以数据中台为架构的高校数据管理体系建设方案,通过统一规范校内的数据标准,在服务层面实现各业务数据的互通,使得校内数据资产得以集中规范管理和充分利用,逐步为智慧校园建设打下基础.     

领域知识图谱研究综述

知识图谱由Google公司提出, 作为增强其搜索功能的知识库, 在近几年得到了迅速发展. 随着知识图谱价值不断地被发掘, 各类领域知识图谱也迅速建设起来. 本文通过领域知识图谱和通用知识图谱的对比来清晰化领域知识图谱的定义, 介绍了领域知识图谱的架构, 并以医学知识图谱为例讲解了领域知识图谱的构建技术. 最后, 本文介绍了当前热门的领域知识图谱的发展状况和应用, 对当前领域知识图谱状况进行了较为全面的总结.     

知识赋能的新一代信息系统研究现状、发展与挑战

信息系统的发展目前正处于感知智能迈向认知智能的关键阶段,传统信息系统难以满足发展要求,数字化转型势在必行.数字线索(digitalthread)是面向全生命周期的数据处理框架,通过连接生命周期的各阶段数据,实现物理世界与数字空间的映射与分析.知识图谱(knowledgegraph)是结构化的语义知识库,以符号形式描述物理世界中的概念及其相互关系,通过知识驱动形成体系化的构建与推理流程.两者对知识赋能的信息系统研究具有重要意义.综述了知识赋能的新一代信息系统的研究现状、发展与挑战.首先,从数字线索系统出发,介绍数字线索的概念和发展,分析数字线索的六维数据构成和6个数据处理阶段;然后介绍知识图谱系统,给出普遍认同的知识图谱的定义和发展,概括知识图谱的架构与方法;最后,分析和探索数字线索与知识图谱结合的方向,列举KG4DT (knowledge graph for digital thread)和DT4KG (digital thread for knowledge graph)的受益方向,对未来知识赋能的新一代信息系统提出开放问题.     

基于云服务的数据中台建设

随着社会经济的不断发展,我国政府部门加大了对数据中台的重视程度,根据云服务实际情况建设数据中台,提高日常数据传输效率。但是,从目前数据中台的实际建设情况来看,它很容易受到各种外在因素的影响,降低了数据中台的应用效率,甚至影响到相关企业未来的发展前景。基于此,阐述了数据中台的相关概述,探究了云计算对数据中台建设的作用,使相关工作人员掌握数据中台的基本架构,如终端层、后台层、前台层以及中台层,从而顺利开展整个数据中台建设工作。     

面向用户画像的轻量化数据中台研究

以数据中台作为建设中小企业信息基础设施的新模式，并针对性构建轻量化中台，有益于统一管理数据资产，改善数据孤岛问题，提高企业部署大数据应用的能力；以用户画像这一典型大数据应用为切入点，归纳画像构建流程并与数据中台体系整合；以整合后的中台体系为基础，提炼必要的功能模块并从结构、技术栈、部署三个角度进行轻量化设计，从而提出面向用户画像且易于搭建和部署的轻量化数据中台方案；基于Hadoop生态组件搭建轻量化用户数据中台，并使用淘宝用户消费数据构建RFM模型下的用户价值标签，验证轻量化数据中台的可行性。     

气象数据中台设计与应用研究

文章分析了气象大数据建设过程中的经验和问题,借鉴阿里巴巴的“中台”理念,设计了一种气象数据中台架构。通过数据模型化和服务组件化,形成重庆气象高效敏捷的数据服务能力,帮助用户快速“找到”数据、“应用”数据、“用好”数据。相对传统的数据平台,可提高数据服务的开发效率、降低应用建设的复杂度、提升业务功能的复用能力,对更好地支撑重庆气象业务技术体系具有重要意义。     

军工企业数据中台架构研究

军工企业及其科研院所现有的信息化系统存在数据孤立不能沟通共享的问题,为了提升企业信息化水平、挖掘数据价值,同时应对科研生产业务需求复杂多变从而造成信息化能力提升困难的现状,可通过数据中台的建设来沉淀数据复用能力,形成数据从汇聚、开发、管理到数据服务的中间层能力平台。该文介绍了数据中台建设路线及关键技术,实现了数据中台架构设计,总结了数据中台的应用实践。     

Self-Service Business Intelligence Resulting in Disruptive Technology

Self-Service Business Intelligence (BI) requires a much greater consideration of the knowledge workers or reporting and analytics users' point of view than in traditional reporting. In order to meet the reporting and analytics users' needs, much greater interaction with the BI users was required because the awareness that those doing the development frequently did not share the reporting and analytics users' perspective or even understand it. The purpose of this Self-Service Business Intelligence effort was to provide customers with a window into available business data, so they can easily manipulate their data to answer business questions. This effort explores some of the disruptive technology available to empower a significantly more information-capable customer. In providing Self-Service Business Intelligence, a significant amount of cost savings can be achieved through better communication between business and information technology (IT) individuals, reducing the required development staff in IT, and increasing agility of the enterprise by using the agreed upon Semantic definition of terms and making the business data more accessible.     

潜艇战场损伤评估仿真中的关键技术研究

研究了潜艇战场损伤仿真评估中的两个关键问题,在综合分析潜艇战场损伤因素的基础上,提出了基于历史数据的损伤日志知识库,并结合专家意见和损伤分析技术,定义了新的仿真数据的综合获取技术,定义和构建了仿真体系中各Agent的体系结构以及协作执行单元。仿真结果证明了数据综合获取技术和结构体定义的有效性。     

MERRA Analytic Services: Meeting the Big Data challenges of climate science through cloud-enabled Climate Analytics-as-a-Service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS built on this principle. MERRA/AS enables MapReduce analytics over NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRA/AS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers’ increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing’s capacity to engage communities in the construction of new capabilities is perhaps the most important link between Cloud Computing and Big Data.     

Establishment-level occupational safety analytics: Challenges and opportunities

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the “big” data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.     

面向数据挖掘的背景知识表示技术研究

数据挖掘是一项高级的智能活动,数据挖掘的过程离不开背景知识。该文从数据挖掘角度出发,在详细分析了背景知识在数据挖掘中意义和作用的基础上,狭义地给出了背景知识的定义,并提出了基于一阶谓词逻辑的背景知识技术。最后,以关联规则挖掘和决策树构造为例,说明了背景知识可有效地提高数据挖掘的效率,改善数据挖掘的质量。     

A survey on big data-driven digital phenotyping of mental health

The landscape of mental health has undergone tremendous changes within the last two decades, but the research on mental health is still at the initial stage with substantial knowledge gaps and the lack of precise diagnosis. Nowadays, big data and artificial intelligence offer new opportunities for the screening and prediction of mental problems. In this review paper, we outline the vision of digital phenotyping of mental health (DPMH) by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and present a broad overview of DPMH from sensing and computing perspectives. We first conduct a systematical literature review and propose the research framework, which highlights the key aspects related with mental health, and discuss the challenges elicited by the enriched data for digital phenotyping. Next, five key research strands including affect recognition, cognitive analytics, behavioral anomaly detection, social analytics, and biomarker analytics are unfolded in the psychiatric context. Finally, we discuss various open issues and the corresponding solutions to underpin the digital phenotyping of mental health.     

A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

Visual analytics employs interactive visualizations to integrate users’ knowledge and inference capability into numerical/algorithmic data analysis processes.It is an active research field that has applications in many sectors, such as security, finance, and business.The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field.This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications.More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions.We explore and discuss the analytics space to add the current understanding and better understand research trends in the field.     

The Evolution of Analytics and Internal Audit

In today’s technology and data-rich environment, internal auditors reading any current professional publication are likely to find an article that discusses analytics in some form or fashion. And while there seems to be a proliferation of data regarding using analytics as part of the auditing process, the core concepts of using the analytical approach in auditing have been around for years. This article will provide a brief overview of the evolution of analytics, along with key considerations for using analytics as part of the audit process.     

Social network analytics for churn prediction in telco: Model building,evaluation and network architecture

Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.