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

综合数字事件档案和图书馆（IDEAL）系统解决了将最好的数字图书馆和档案技术相结合的需求，以支持正在记住和/或研究重要事件的利益相关者。它扩展了弗吉尼亚理工大学在危机，悲剧和恢复网络（见http://www.ctrnet.net）上的工作，以处理政府和社区活动，以及一系列重大的自然或人为灾害。它解决了对应急准备/响应，数字政府和社会科学感兴趣的人的需求。它证明了5S（社会，场景，空间，结构，流）方法的有效性，智能信息系统的爬行和归档事件的广泛兴趣。它利用并扩展了互联网档案馆的功能，以开发可以永久存档以及搜索和访问的自发事件集合，以及支持可扩展索引的LucidWorks大数据软件，分析和访问非常大的集合。通过一种新的基于模型的方法来进行智能集中爬行，它提高了质量（例如，准确性，覆盖面和消除噪音），以确保全面性，平衡性和低偏差，这是社会科学家对历史重要事件进行学术研究所需的。它集成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，图书馆员，研究人员，学者和公众。IDEAL将推文和网页的处理连接起来，将非正式和正式媒体结合起来，自动检测重要事件，并支持建立关于选定的一般或特定主题的集合。它支持多种类型和多个级别的集成，包括关于它正在抓取的事件的关键模型（事件模型），关于事件的信息源（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。IDEAL网站（http：www.eventsarchive.org）支持搜索、浏览、分析和可视化事件集合（tweets和网页），以及访问项目软件、方法、发现、出版物和其他结果。鼓励使用集成系统沿着越来越多的收藏品，以及特定的工具，如用于重点抓取的工具，这将有助于馆长避免不相关的内容，同时包括更广泛的来源，大大改善目前的抓取和存档方法。重要的数据和信息的利益事件保存而不是丢失，帮助保存我们的历史和文化，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，界面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.