语义Web和语义网格及数据资源整合的探讨

目前,许多电子政务系统面临着一个共同的问题,这就是所谓的＂信息孤岛＂问题,解决该问题的关键是如何将现有数据资源进行有效的整合。而语义网格理论的提出,为这一难题的解决提供了一个新的思路。应用语义网格理论,针对出租汽车管理信息化的应用需求,构造语义网格模型Tx-Grid,并且讨论一种利用本体整合分布式异构数据库资源的机制,有效地解决这一问题。     

A scalable constraint-based Q-hash indexing for moving objects

In this paper, we develop a Q-hash index structure to efficiently store the position of moving objects. An environment of moving objects contains continuously changing locations which are hard to index using traditional index structures such as R-trees, QuadTrees and their variants. In order to answer the queries accurately, one of the problems faced in storing these positions is the number of updates that have to be made to the database whenever locations change. The high maintenance overhead on updates leads to performance degradation of these index structures; additionally, it makes the database very bulky which results in very poor performance in terms of query execution time. One of the main objectives of the structure we propose is to minimize the number of updates to the database to an optimal number so that the accuracy and response time of the query result are not compromised and at the same time the number of wireless communications can be reduced. The indexing is done using a hashing technique where the hashing function makes use of a region based QuadTree structure. To improve the efficiency of the query processing our index structure helps us define constraints over speed, direction and location of the moving object at the device level which controls the number of updates. In addition, in order to answer different query types efficiently at all levels we propose a three-tier (moving object, regional server, central repository) architecture. Our extensive performance evaluation and comparison of the proposed technique concludes that our scheme outperforms existing Q + R-tree and QuadTree in terms of range query execution time by a high order of magnitude.     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

A multi-agent legal recommender system

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.     

基于本体的城市遥感影像语义模型

在快速的城市化进程中,遥感技术可以为城市土地利用变化提供直观、准确、同步的信息。本体是对概念化的明确描述和说明,更接近于认知模型,易于理解和共享;同时又能以计算机可操作的形式化语言明确表达目标领域的概念体系及其蕴含的语义。因此,在分析和借鉴现有相关研究的基础上,将本体、语义思想引入到遥感影像表达,建立了基于本体的城市遥感影像解译应用语义模型和解译框架,应用此框架将有助于提高城市遥感影像解译的精度与效率,对推动遥感技术在城市相关领域中的应用有着积极的意义。     

一种基于DOM的Web文档主题划分方法

主题划分是多主题文档自动摘要中的一个重要问题,提出了一种以网页结构为指导,利用页面对应DOM树中节点的自然分割功能以及相邻边界节点语义相似度的比较进行网页主题划分的方法.实验结果表明该方法具有较高的划分准确率,在此基础上抽取的网页摘要可显著增加文摘内容对原文的覆盖率、有效解决Web文档摘要分布不平衡问题.     

The Use of Context-Aware Policies and Ontologies to Facilitate Business-Aware Network Management

The purpose of autonomic networking is to manage the business and technical complexity of networked components and systems. However, existing network management data has no link to business concepts. This makes it very difficult to ensure that services offered by the network are meeting business objectives. This paper describes a novel context-aware policy model that uses a combination of modeled and ontological data to determine the current context, which policies are applicable to that context, and what services and resources should be offered to which users and applications.

iSAX: disk-aware mining and indexing of massive time series datasets

Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we introduce a novel multi-resolution symbolic representation which can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. To demonstrate the utility of this representation, we constructed a simple tree-based index structure which facilitates fast exact search and orders of magnitude faster, approximate search. For example, with a database of one-hundred million time series, the approximate search can retrieve high quality nearest neighbors in slightly over a second, whereas a sequential scan would take tens of minutes. Our experimental evaluation demonstrates that our representation allows index performance to scale well with increasing dataset sizes. Additionally, we provide analysis concerning parameter sensitivity, approximate search effectiveness, and lower bound comparisons between time series representations in a bit constrained environment. We further show how to exploit the combination of both exact and approximate search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing tens of millions of time series.     

一种新的语义对象行为语言

针对人类用自然语言与机器交互所面临的巨大鸿沟,提出了一种基于类自然语言的语义对象行为语言。SOBL是一种宣称式的陈述语言,用于开发数据库驱动的应用,支持复杂的用户交互,主要由一个Semantic Objects^TM的对象关系开发框架所支持。该语言扩展结构化自然语言,携带语义信息,能够面向非程序员使用,降低了软件的开发难度。最后通过一个具体的实例验证了该语言的有效性。