III: Small: Integrated Digital Event Archiving and Library (IDEAL)

综合数字事件档案和图书馆(理想)系统满足了将数字图书馆和档案技术的最佳结合起来的需要，以支持正在记住和/或研究重要事件的利益攸关方。它扩展了弗吉尼亚理工大学在危机、悲剧和恢复网络方面的工作(见http://www.ctrnet.net))，以处理政府和社区事件，以及一系列重大的自然或人为灾难。它满足了那些对应急准备/响应、数字政府和社会科学感兴趣的人的需求。它通过对广泛感兴趣的事件进行爬行和存档，证明了5S(社会、场景、空间、结构、流)方法对智能信息系统的有效性。它利用并扩展了互联网档案的功能，以开发可永久存档以及搜索和访问的自发事件集合，以及支持对非常大的集合进行可扩展索引、分析和访问的LucidWorks大数据软件。通过一种新的基于模型的智能聚焦爬行方法，它提高了网页集合的质量(例如，准确性、覆盖率和消除噪音)，以确保全面、平衡和低偏见，这是社会科学家对历史上重要事件进行学术研究所需的。它结合了一系列可视化功能，以支持关键的利益相关者社区，包括档案管理员、图书馆管理员、研究人员、学者和普通公众。Idea连接推文和网页的处理，将非正式和正式媒体结合在一起，以自动检测重要事件，并支持就选定的一般或特定主题建立集合。它支持多种类型和多个级别的集成，包括关于它正在爬行的事件的关键模型(事件模型)、关于事件的信息来源(源模型)、用于传播关于事件的信息的机制(发布场地模型)以及与事件相关的实体(社会/组织模型)。集成服务包括主题识别、分类(基于正在设计的特殊本体)、情感分析以及数据、信息和上下文的可视化。理想网站(http://www.eventsarchive.org)支持(推文和网页)事件集合的搜索、浏览、分析和可视化，以及访问项目软件、方法、发现、出版物和其他结果。鼓励使用综合系统和越来越多的收藏，以及特定的工具，如重点爬行，这应有助于馆长避免无关的内容，同时包括更广泛的来源，大大改进目前的爬行和存档方法。有关感兴趣事件的重要数据和信息被保存而不是丢失，有助于保护我们的历史和文化，支持公共利益、教育、政策制定、历史分析和比较研究。学习社会学、人机交互、数字图书馆、信息检索、计算语言学、多媒体和超文本的学生正在获得经验，并在学术研究、算法、软件、接口和大数据处理方面做出贡献。

The Integrated Digital Event Archive and Library (IDEAL) system addresses the need for combining the best of digital library and archive technologies in support of stakeholders who are remembering and/or studying important events. It extends the work at Virginia Tech on the Crisis, Tragedy, and Recovery network (see http://www.ctrnet.net) to handle government and community events, in addition to a range of significant natural or manmade disasters. It addresses needs of those interested in emergency preparedness/response, digital government, and the social sciences. It proves the effectiveness of the 5S (Societies, Scenarios, Spaces, Structures, Streams) approach to intelligent information systems by crawling and archiving events of broad interest. It leverages and extends the capabilities of the Internet Archive to develop spontaneous event collections that can be permanently archived as well as searched and accessed, and of the LucidWorks Big Data software that supports scalable indexing, analyzing, and accessing of very large collections. Through a new model-based approach to intelligent focused crawling, it improves the quality (e.g., accuracy, coverage, and elimination of noise) of collections of webpages so as to ensure comprehensiveness, balance, and low bias, as is needed for scholarly study of historically important events by social scientists. It incorporates a range of visualization capabilities in support of key stakeholder communities, including archivists, librarians, researchers, scholars, and the general public. IDEAL connects the processing of tweets and webpages, combining informal and formal media, to automatically detect important events, as well as to support building collections on chosen general or specific topics. It supports integration of multiple types and at multiple levels, including key models about the event it is crawling (event models), the sources of information about the event (source models), the mechanisms used for disseminating information about the event (publishing venue models), and the entities related to the event (society /organization models). Integrated services include topic identification, categorization (building upon special ontologies being devised), sentiment analysis, and visualization of data, information, and context.The IDEAL website (http://www.eventsarchive.org) supports searching, browsing, analyzing, and visualizing of event collections (of both tweets and webpages), as well as access to project software, methods, findings, publications, and other results. Usage is encouraged of the integrated system along with a growing number of collections, as well as of particular tools such as for focused crawling, which should aid curators to avoid non-relevant content while including a broader range of sources, improving significantly upon current crawling and archiving methods. Important data and information on events of interest are saved rather than lost, helping preserve our history and culture, in support of public interest, education, policy making, historical analyses, and comparative studies. Students studying sociology, human-computer interaction, digital libraries, information retrieval, computational linguistics, multimedia, and hypertext are gaining experience and contributing in scholarly studies, algorithms, software, interfaces, and big data handling.