面向大数据环境的决策信息需求通用描述语言

制定重大决策需要全方面的数据支持,而大数据时代的到来却让决策信息搜集更加困难。数据多样性和价值密度低的特点,让决策者耗费大量时间在信息需求的表达上。设计了一种通用的信息需求描述语言,及其为数据库、发布/订阅、搜索引擎等常见信息资源解析转换的方法,使得用户可以通过一种统一的方式将相关异构信息全部搜集到。实验表明,其基于本体的语义描述能力能够有效屏蔽数据异构性,提高信息搜集的全面性和准确性。     

本体存储技术研究

Ontology是对一个特定领域中重要概念的共享的形式化的描述,由于具有明确性和共享性,它可以作为领域内不同主体之间进行交流的语义基础;更进一步的,Ontology可以帮助机器理解文档表达的语义信息.语义网络是Ontology的一个重要应用场景,Ontology用来描述网络资源的语义,从而使机器具有自动管理网络信息的能力.那么巨大的数据规模是语义网络环境下Ontology数据存储管理面临的一个突出问题,所以介绍了本体存储的方法、存储模式及几种典型的本体存储管理系统.讨论了当前本体存储模式的问题并展望了未来的发展方向.     

本体存储技术研究

Ontobgy是对一个特定领域中重要概念的共享的形式化的描述,由于具有明确性和共享性,它可以作为领域内不同主体之同进行交流的语义基础;更进一步的,Ontology可以帮助机器理解文档表达的语义信息。语义网络是Ontology的一个重要应用场景,Ontolcgy用来描述网络资源的语义,从而使机器具有自动管理网络信息的能力。那么巨大的数据规模是语义网络环境下Ontology数据存储管理面临的一个突出问题,所以介绍了本体存储的方法、存储模式及几种典型的本体存储管理系统。讨论了当前本体存储模式的问题并展望了未来的发展方向。     

Exception representation and management in open multi-agent systems

One of the major issues in dealing with exceptions in open multi-agent systems (MAS) is lack of uniform representation of exceptions and their shared semantics. In the absence of a uniform framework different business organizations may use different representations for the same exception or may interpret the same exception in different ways. In order to address this issue we apply an ontological approach as a uniform way of representing and interpreting exceptions in cross-organizational settings. This helps agents from different organizations interpret exceptional situations in an unambiguous way and exchange exception related information using standard structures. We believe that an exception ontology along with a domain ontology increases the open MAS reliability and also enhances its fault tolerance capability. The proposed ontology is built using the ontological support provided by the JADE agent framework and exception diagnoses agents are implemented using the JACK™ agent framework.     

Social network technologies for semantic linking of information objects in scientific digital library

In the last decade, scientific digital libraries were traditionally used for publishing research results and for enabling wide open access to them. Functional capabilities of digital libraries can be extended by offering users the opportunity of linking information objects of the library and providing for created linkages explicitly defined semantics based on a given ontology. Such an activity of users, which is peculiar to social networks, motivated by different reasons, and carried out on their own initiative, results in the dynamic semantic structure of the digital library content. In the environment of such a kind of a social network, certain new forms of scientific activities become possible and data sources can be created that provide more information for scientometric researches as compared to presently available ones. In this paper, we propose an approach for creating such networks and discuss results of its implementation in the Socionet environment; the Socionet is a large-scale online information space that covers information resources of a number of scientific, educational, etc. organizations.     

Supporting pervasive computing applications with active context fusion and semantic context delivery

Future pervasive computing applications are envisioned to adapt the applications’ behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.     

基于语义的轻量级数据集成方法

为解决现有数据集成方法的集成成本过高、缺乏语义信息等问题,对带有语义信息的轻量级数据集成方法开展研究.对本体、元数据等相关理论进行概述,给出了一种基于语义的轻量级数据集成方法,并详细分析了其中的两个主要过程:本体的识别和元数据的抽取、本体映射和基于本体映射的元数据集成,并进行了实例分析.分析结果表明,所给方法切实可行,用元数据集成替代数据集成能够避免大量数据的移动和存储,有效降低数据集成的成本,使数据集成过程轻量化,同时,语义信息的融入能够更好地为上层应用提供支持.     

网络大数据计算技术与应用综述

网络大数据蕴含着丰富的社会信息,可以看作是对真实社会的网络映射。分析网络大数据并发现其中所暗含的线索与规律,可以帮助人们更好地感知现在、预测未来。本文简要介绍了国内外有关网络大数据的战略布局,总结了网络大数据计算技术研究面临的挑战,并从网络大数据计算的架构体系以及网络大数据的感知与表示、内容建模与语义理解等方面分析了研究现状。最后,通过实例对网络大数据将带来的新型应用进行了展望。     

基于本体的查询系统研究

该文对查询系统做了深入的研究,提出了一种基于本体的智能查询系统的形式化模型,并且对系统的具体运行过程进行了详细的分析。该模型充分考虑了用户查询的语义信息,并且引入了本体环境和用户查询环境来对系统进行建模。将用来对数据库的语义信息进行描述的本体层引入到异构的、分布式的数据库系统中,在方便用户进行查询的同时增加查询结果的相关性和用户满意度。同时,系统能够及时地反映数据库信息的动态变化。     

移动用户兴趣点标记语义映射方法

针对当前移动用户兴趣点标注没有统一的语义本体这一问题,通过对移动用户兴趣点分类,建立了 POI概念本体层次树,将用户标注的POI信息与本体树节点通过一种改进的映射方法建立映射关系,为移动用户标注信息提供统一的规范化语义。实验收集多名志愿者5周的真实标注信息来评价该方法,实验结果表明该方法具有较高的准确率。     

SurvSurf: human retrieval on large surveillance video data

The volume of surveillance videos is increasing rapidly, where humans are the major objects of interest. Rapid human retrieval in surveillance videos is therefore desirable and applicable to a broad spectrum of applications. Existing big data processing tools that mainly target textual data cannot be applied directly for timely processing of large video data due to three main challenges: videos are more data-intensive than textual data; visual operations have higher computational complexity than textual operations; and traditional segmentation may damage video data’s continuous semantics. In this paper, we design SurvSurf, a human retrieval system on large surveillance video data that exploits characteristics of these data and big data processing tools. We propose using motion information contained in videos for video data segmentation. The basic data unit after segmentation is called M-clip. M-clips help remove redundant video contents and reduce data volumes. We use the MapReduce framework to process M-clips in parallel for human detection and appearance/motion feature extraction. We further accelerate vision algorithms by processing only sub-areas with significant motion vectors rather than entire frames. In addition, we design a distributed data store called V-BigTable to structuralize M-clips’ semantic information. V-BigTable enables efficient retrieval on a huge amount of M-clips. We implement the system on Hadoop and HBase. Experimental results show that our system outperforms basic solutions by one order of magnitude in computational time with satisfactory human retrieval accuracy.     

Characterization of 3D shape parts for semantic annotation

3D content stored in big databases or shared on the Internet is a precious resource for several applications, but unfortunately it risks being underexploited due to the difficulty of retrieving it efficiently. In this paper we describe a system called the “ShapeAnnotator” through which it is possible to perform non-trivial segmentations of 3D surface meshes and annotate the detected parts through concepts expressed by an ontology. Each part is connected to an instance that can be stored in a knowledge base to ease the retrieval process based on semantics. Through an intuitive interface, users create such instances by simply selecting proper classes in the ontology; attributes and relations with other instances can be computed automatically based on a customizable analysis of the underlying topology and geometry of the parts. We show how our part-based annotation framework can be used in two scenarios, namely for the creation of avatars in emerging Internet-based virtual worlds, and for product design in e-manufacturing.     

User-centric social context information management: an ontology-based approach and platform

Social context information has been used with encouraging results in developing socially aware applications in different domains. However, users’ social context information is distributed over the Web and managed by many different proprietary applications, which is a challenge for application developers as they must collect information from different sources and wade through a lot of irrelevant information to obtain the social context information of interest. On the other hand, it is extremely hard for information owners to control how their information should be exposed to different users and applications. Combining the social context information from the diverse sources, incorporating richer semantics and preserving information owners’ privacy could greatly assist the developers and as well as the information owners. In this paper, we introduce a social context information management system (SCIMS). It includes the ability to acquire raw social data from multiple sources; an ontology-based model for classifying, inferring and storing social context information, in particular, social relationships and status; an ontology-based policy model and language for owners to control access to their information; a query interface for accessing and utilizing social context information. We evaluate the performance of SCIMS using real data from Facebook, LinkedIn, Twitter, and Google Calendar and demonstrate its applicability through a socially aware phone call application.     

基于Android系统的大数据推送平台研究

移动互联网与社交网络的兴起将大数据带入新的征程,在大数据时代,移动互联网用户如何及时有效地获取信息也成为一个问题。针对大数据对移动互联网用户的影响,提出一种基于Android系统的消息推送平台,推送平台通过建立数据与用户之间的连接,将经过分析后的优质数据及时的传递给用户,分析推送平台的实现原理,并对其应用场景进行简单的分析。     

语义虚拟环境本体可视化模型的研究

当前信息可视化所面临的一个瓶颈就是,需要一种模型来把场景图形内容和特定领域的语义信息进行有效集成,使用户可以有效地解释个性化可视化信息。基于X3D标准以及本体用本体描述语言OWL描述X3D标准,构建了一个X3D标准本体和一个映射本体来实现X3D标准本体和其他具体领域本体之间类和属性的映射,以达到丰富虚拟场景语义的目的。基于X3D标准本体和映射本体设计了语义虚拟环境本体可视化模型,从而为具体领域本体实现可视化提供了一种方法。实验结果证明了该模型的可行性。     

形式概念分析在本体构建中的应用

随着信息共享和数据交换的范围不断扩大,语义数据集成成为数据库技术研究中的热点。本体作为共享概念的形式化说明,用于表达数据源的语义、识别和建立概念间的语义关联、达成语义一致,提供了语义异构问题的解决途径。传统的关系数据库由于具备技术成熟、应用广泛、数据管理能力强、数据安全程度高等特点,依然是数据存储的主要手段。然而,关系数据只表达了"属性/值"二元关系,不具备语义描述的能力,如何在关系数据中直接抽取数据的语义,构建本体,提炼丰富的语义信息,弥补关系数据表的缺陷,是一个重要的研究方向。文中探讨了运用形式概念分析的技术,结合数据库模式及其数据,构建本体的方法。在研究本体构建的基本方法基础上,论述了形式概念分析方法,并结合给定的数据库模式及其数据信息,演示了运用形式概念分析的方法构建本体的过程。     

SocialRobot: a big data-driven humanoid intelligent system in social media services

The blooming of social media services makes them the attractive resources for publishing and seeking information (posting, reposting and searching tweets) as well as socializing and interacting with other users (following and messaging other users) in social media services. With the increasing number of users and the interacting frequency between users, tremendous user-generated contents are bursting out every day. Hence, users may face a overload of information. To address the above problem, in this paper, we present SocialRobot, a humanoid intelligent system that has been deployed in Sina Weibo. For its socialization characteristic, we naturally implement it as a virtual user in Sina Weibo. By following and interacting with SocialRobot, high-quality and user-interested content can be recommended to the users from the big social media data. And the need for searching specific information can be satisfied by chatting with the SocialRobot. A crowdsourcing evaluation shows the effective performance of the SocialRobot on resolving the information overload.     

DICON: interactive visual analysis of multidimensional clusters

Clustering as a fundamental data analysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.     

Ontology driven interactive healthcare with wearable sensors

Ubiquitous healthcare is the service that offers health-related information and contents to users without any limitations of time and space. Especially, to offer customized services to users, the technology of acquiring context information of users in real time is the most important consideration. In this paper, we researched wearable sensors. We proposed the ontology driven interactive healthcare with wearable sensors (OdIH_WS) to achieve customized healthcare service. For this purpose, wearable-sensor-based smart-wear and methods of data acquisition and processing are being developed. The proposed system has potential value in healthcare. A smart wear using wearable sensors is fabricated as a way of non-tight and comfortable style fitting for the curves of the human body based on clothes to wear in daily life. The design sample of the smart wear uses basic stretch materials and is designed to sustain its wearable property. To offer related information, it establishes an environment-information-based healthcare ontology model needed for inference, and it is composed of inside-outside context information models depending on the users’ context. The modeling of the proposed system involved combinations of information streams, focusing on service context information. With the proposed service inference rules, customized information and contents could be drawn by the inference engine. In the established OdIH_WS, real-time health information monitoring was achieved. The results of system performance and users’ satisfaction evaluations confirmed that the proposed system is superior to other existing systems.     

基于HBase的领域本体存储方法及其应用研究

在分析民航突发事件应急管理领域本体及其存储特点的基础上,提出了一种基于HBase的领域本体存储方法,采用将领域本体元数据与RDF实例数据分开存储的方式,给出了描述领域本体类及属性信息的元数据和RDF实例数据的存储模型,及其基于MapReduce的领域本体RDF数据并行加载过程。结合应用实现了领域本体基于HBase API的基本图模式查询,并在Hadoop环境下进行了实验与效果分析,为民航应急管理领域本体的海量数据存储提供了理论与方法支撑。