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

集成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，以支持那些记住和/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危机、悲剧和恢复网络（见http://www.ctrnet.net）的工作，除了处理一系列重大的自然或人为灾害外，还处理政府和社区事件。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。它证明了5S（社会、场景、空间、结构、流）方法通过抓取和存档广泛感兴趣的事件来实现智能信息系统的有效性。它利用并扩展了Internet Archive的功能来开发可以永久存档、搜索和访问的自发事件集合，以及LucidWorks大数据软件，支持可扩展的索引、分析和访问非常大的集合。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，覆盖率和消除噪声），以确保全面，平衡和低偏差，这是社会科学家对历史重要事件的学术研究所需要的。它集成了一系列可视化功能，以支持关键利益相关者社区，包括档案管理员、图书管理员、研究人员、学者和一般公众。IDEAL将tweet和网页的处理连接起来，结合非正式和正式媒体，自动检测重要事件，并支持在所选的一般或特定主题上建立集合。它支持在多个级别上集成多种类型，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（发布场所模型）以及与事件相关的实体（社会/组织模型）。集成的服务包括主题识别、分类（建立在正在设计的特殊本体之上）、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（tweet和网页），以及访问项目软件、方法、发现、出版物和其他结果。鼓励使用集成系统和越来越多的收藏，以及特定的工具，如集中爬行，这应该帮助策展人避免不相关的内容，同时包括更广泛的来源，在目前的爬行和存档方法上有显着改进。有关重要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在学术研究、算法、软件、接口和大数据处理方面积累了经验，并做出了贡献。

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.