Right HTML, Wrong JSON: Challenges in Replaying Archived Webpages Built with Client-Side Rendering

Many web sites are transitioning how they construct their pages. The conventional model is where the content is embedded server-side in the HTML and returned to the client in an HTTP response. Increasingly, sites are moving to a model where the initial HTTP response contains only an HTML skeleton plus JavaScript that makes API calls to a variety of servers for the content (typically in JSON format), and then builds out the DOM client-side, more easily allowing for periodically refreshing the content in a page and allowing dynamic modification of the content. This client-side rendering, now predominant in social media platforms such as Twitter and Instagram, is also being adopted by news outlets, such as CNN.com. When conventional web archiving techniques, such as crawling with Heritrix, are applied to pages that render their content client-side, the JSON responses can become out of sync with the HTML page in which it is to be embedded, resulting in temporal violations on replay. Because the violative JSON is not directly observable in the page (i.e., in the same manner a violative embedded image is), the temporal violations can be difficult to detect. We describe how the top level CNN.com page has used client-side rendering since April 2015 and the impact this has had on web archives. Between April 24, 2015 and July 21, 2016, we found almost 15,000 mementos with a temporal violation of more than 2 days between the base CNN.com HTML and the JSON responses used to deliver the content under the main story. One way to mitigate this problem is to use browser-based crawling instead of conventional crawlers like Heritrix, but browser-based crawling is currently much slower than non-browser-based tools such as Heritrix.Comment: 20 pages, preprint version of paper accepted at the 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL

Web archives: the future

T his report is structured first, to engage in some speculative thought about the possible futures of the web as an exercise in prom pting us to think about what we need to do now in order to make sure that we can reliably and fruitfully use archives of the w eb in the future. Next, we turn to considering the methods and tools being used to research the live web, as a pointer to the types of things that can be developed to help unde rstand the archived web. Then , we turn to a series of topics and questions that researchers want or may want to address using the archived web. In this final section, we i dentify some of the challenges individuals, organizations, and international bodies can target to increase our ability to explore these topi cs and answer these quest ions. We end the report with some conclusions based on what we have learned from this exercise

Scripts in a Frame: A Framework for Archiving Deferred Representations

Web archives provide a view of the Web as seen by Web crawlers. Because of rapid advancements and adoption of client-side technologies like JavaScript and Ajax, coupled with the inability of crawlers to execute these technologies effectively, Web resources become harder to archive as they become more interactive. At Web scale, we cannot capture client-side representations using the current state-of-the art toolsets because of the migration from Web pages to Web applications. Web applications increasingly rely on JavaScript and other client-side programming languages to load embedded resources and change client-side state. We demonstrate that Web crawlers and other automatic archival tools are unable to archive the resulting JavaScript-dependent representations (what we term deferred representations), resulting in missing or incorrect content in the archives and the general inability to replay the archived resource as it existed at the time of capture. Building on prior studies on Web archiving, client-side monitoring of events and embedded resources, and studies of the Web, we establish an understanding of the trends contributing to the increasing unarchivability of deferred representations. We show that JavaScript leads to lower-quality mementos (archived Web resources) due to the archival difficulties it introduces. We measure the historical impact of JavaScript on mementos, demonstrating that the increased adoption of JavaScript and Ajax correlates with the increase in missing embedded resources. To measure memento and archive quality, we propose and evaluate a metric to assess memento quality closer to Web users’ perception. We propose a two-tiered crawling approach that enables crawlers to capture embedded resources dependent upon JavaScript. Measuring the performance benefits between crawl approaches, we propose a classification method that mitigates the performance impacts of the two-tiered crawling approach, and we measure the frontier size improvements observed with the two-tiered approach. Using the two-tiered crawling approach, we measure the number of client-side states associated with each URI-R and propose a mechanism for storing the mementos of deferred representations. In short, this dissertation details a body of work that explores the following: why JavaScript and deferred representations are difficult to archive (establishing the term deferred representation to describe JavaScript dependent representations); the extent to which JavaScript impacts archivability along with its impact on current archival tools; a metric for measuring the quality of mementos, which we use to describe the impact of JavaScript on archival quality; the performance trade-offs between traditional archival tools and technologies that better archive JavaScript; and a two-tiered crawling approach for discovering and archiving currently unarchivable descendants (representations generated by client-side user events) of deferred representations to mitigate the impact of JavaScript on our archives. In summary, what we archive is increasingly different from what we as interactive users experience. Using the approaches detailed in this dissertation, archives can create mementos closer to what users experience rather than archiving the crawlers’ experiences on the Web

Web Archive Services Framework for Tighter Integration Between the Past and Present Web

Web archives have contained the cultural history of the web for many years, but they still have a limited capability for access. Most of the web archiving research has focused on crawling and preservation activities, with little focus on the delivery methods. The current access methods are tightly coupled with web archive infrastructure, hard to replicate or integrate with other web archives, and do not cover all the users\u27 needs. In this dissertation, we focus on the access methods for archived web data to enable users, third-party developers, researchers, and others to gain knowledge from the web archives. We build ArcSys, a new service framework that extracts, preserves, and exposes APIs for the web archive corpus. The dissertation introduces a novel categorization technique to divide the archived corpus into four levels. For each level, we will propose suitable services and APIs that enable both users and third-party developers to build new interfaces. The first level is the content level that extracts the content from the archived web data. We develop ArcContent to expose the web archive content processed through various filters. The second level is the metadata level; we extract the metadata from the archived web data and make it available to users. We implement two services, ArcLink for temporal web graph and ArcThumb for optimizing the thumbnail creation in the web archives. The third level is the URI level that focuses on using the URI HTTP redirection status to enhance the user query. Finally, the highest level in the web archiving service framework pyramid is the archive level. In this level, we define the web archive by the characteristics of its corpus and building Web Archive Profiles. The profiles are used by the Memento Aggregator for query optimization

Models and methods for web archive crawling

Web archives offer a rich and plentiful source of information to researchers, analysts, and legal experts. For this purpose, they gather Web sites as the sites change over time. In order to keep up to high standards of data quality, Web archives have to collect all versions of the Web sites. Due to limited resuources and technical constraints this is not possible. Therefore, Web archives consist of versions archived at various time points without guarantee for mutual consistency. This thesis presents a model for assessing the data quality in Web archives as well as a family of crawling strategies yielding high-quality captures. We distinguish between single-visit crawling strategies for exploratory and visit-revisit crawling strategies for evidentiary purposes. Single-visit strategies download every page exactly once aiming for an “undistorted” capture of the ever-changing Web. We express the quality of such the resulting capture with the “blur” quality measure. In contrast, visit-revisit strategies download every page twice. The initial downloads of all pages form the visit phase of the crawling strategy. The second downloads are grouped together in the revisit phase. These two phases enable us to check which pages changed during the crawling process. Thus, we can identify the pages that are consistent with each other. The quality of the visit-revisit captures is expressed by the “coherence” measure. Quality-conscious strategies are based on predictions of the change behaviour of individual pages. We model the Web site dynamics by Poisson processes with pagespecific change rates. Furthermore, we show that these rates can be statistically predicted. Finally, we propose visualization techniques for exploring the quality of the resulting Web archives. A fully functional prototype demonstrates the practical viability of our approach.Ein Webarchiv ist eine umfassende Informationsquelle für eine Vielzahl von Anwendern, wie etwa Forscher, Analysten und Juristen. Zu diesem Zweck enthält es Repliken von Webseiten, die sich typischerweise im Laufe der Zeit geändert haben. Um ein möglichst umfassendes und qualitativ hochwertiges Archiv zu erhalten, sollten daher - im Idealfall - alle Versionen der Webseiten archiviert worden sein. Dies ist allerdings sowohl aufgrund mangelnder Ressourcen als auch technischer Rahmenbedingungen nicht einmal annähernd möglich. Das Archiv besteht daher aus zahlreichen zu unterschiedlichen Zeitpunkten erstellten “Mosaiksteinen”, die mehr oder minder gut zueinander passen. Diese Dissertation führt ein Modell zur Beurteilung der Datenqualität eines Webarchives ein und untersucht Archivierungsstrategien zur Optimierung der Datenqualität. Zu diesem Zweck wurden im Rahmen der Arbeit “Einzel-” und “Doppelarchivierungsstrategien” entwickelt. Bei der Einzelarchivierungsstrategie werden die Inhalte für jede zu erstellende Replik genau einmal gespeichert, wobei versucht wird, das Abbild des sich kontinuierlich verändernden Webs möglichst “unverzerrt” zu archivieren. Die Qualität einer solchen Einzelarchivierungsstrategie kann dabei durch den Grad der “Verzerrung” (engl. “blur”) gemessen werden. Bei einer Doppelarchivierungsstrategie hingegen werden die Inhalte pro Replik genau zweimal besucht. Dazu teilt man den Archivierungsvorgang in eine “Besuchs-” und “Kontrollphase” ein. Durch die Aufteilung in die zuvor genannten Phasen ist es dann möglich festzustellen, welche Inhalte sich im Laufe des Archivierungsprozess geändert haben. Dies ermöglicht exakt festzustellen, ob und welche Inhalte zueinander passen. Die Güte einer Doppelarchivierungsstrategie wird dazu mittels der durch sie erzielten “Kohärenz” (engl. “coherence”) gemessen. Die Archivierungsstrategien basieren auf Vorhersagen über das Änderungsverhalten der zur archivierenden Inhalte, die als Poissonprozesse mit inhaltsspezifischen Änderungsraten modelliert wurden. Weiterhin wird gezeigt, dass diese Änderungsraten statistisch bestimmt werden können. Abschließend werden Visualisierungstechniken für die Qualitätsanalyse des resultierenden Webarchivs vorgestellt. Ein voll funktionsfähiger Prototyp demonstriert die Praxistauglichkeit unseres Ansatzes

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms