Digitization and the large-scale preservation of digitized content have engendered new ways of accessing and analyzing collections concurrent with other data mining and extraction efforts. Distant reading refers to the analysis of entire collections instead of close reading individual items like a single physical book or electronic document. The steps performed in distant reading are often common across various types of data collections like books, journals, or web archives, sources that are very valuable and have often been neglected as Big Data. We have extended our tool ArchiveSpark, originally designed to efficiently process Web archives, in order to support arbitrary data collections being served from either local or remote data sources by using metadata proxies. The ability to share and reuse researcher workflows across disciplines with very different datasets makes ArchiveSpark a universal distant reading framework. In this paper, we describe ArchiveSpark's design extensions along an example of how it can be leveraged to analyze symptoms of Polio mentioned in journals from the Medical Heritage Library. Our experiments demonstrate how users can reuse large portions of their job pipeline to accomplish a specific task across diverse data types and sources. Migrating an ArchiveSpark job to process a different dataset introduces an additional average code complexity of only 4.8%. Its expressiveness, scalability, extensibility, reusability, and efficiency has the potential to advance novel and rich methods of scholarly inquiry.

Web archives are large longitudinal collections that store webpages from the past, which might be missing on the current live Web. Consequently, temporal search over such collections is essential for finding prominent missing webpages and tasks like historical analysis. However, this has been challenging due to the lack of popularity information and proper ground truth to evaluate temporal retrieval models. In this paper we investigate the applicability of external longitudinal resources to identify important and popular websites in the past and analyze the social bookmarking service Delicious for this purpose. The timestamped bookmarks on Delicious provide explicit cues about popular time periods in the past along with relevant descriptors. These are valuable to identify important documents in the past for a given temporal query. Focusing purely on recall, we analyzed more than 12,000 queries and find that using Delicious yields average recall values from 46% up to 100%, when limiting ourselves to the best represented queries in the considered dataset. This constitutes an attractive and low-overhead approach for quick access into Web archives by not dealing with the actual contents.