Distributed communities of researchers rely increasingly on valuable, proprietary, or sensitive datasets. Given the growth of such data, especially in fields new to data-driven research like the social sciences and humanities, coupled with what are often strict and complex data-use agreements, many research communities now require methods that allow secure, scalable and cost-effective storage and analysis. Here we present Cloud Kotta: a cloud-based data management and analytics framework. Cloud Kotta delivers an end-to-end solution for coordinating secure access to large datasets, and an execution model that provides both automated infrastructure scaling and support for executing analytics near to the data. Cloud Kotta implements a fine-grained security model ensuring that only authorized users may access, analyze, and download protected data. It also implements automated methods for acquiring and configuring low-cost storage and compute resources as they are needed. We present the architecture and implementation of Cloud Kotta and demonstrate the advantages it provides in terms of increased performance and flexibility. We show that Cloud Kotta's elastic provisioning model can reduce costs by up to 16x when compared with statically provisioned models.

Data-driven research is increasingly ubiquitous and data itself is a defining asset for researchers, particularly in the computational social sciences and humanities. Entire careers and research communities are built around valuable, proprietary or sensitive datasets. However, many existing computation resources fail to support secure and cost-effective storage of data while also enabling secure and flexible analysis of the data. To address these needs we present CLOUD KOTTA, a cloud-based architecture for the secure management and analysis of social science data. CLOUD KOTTA leverages reliable, secure, and scalable cloud resources to deliver capabilities to users, and removes the need for users to manage complicated infrastructure. CLOUD KOTTA implements automated, cost-aware models for efficiently provisioning tiered storage and automatically scaled compute resources. CLOUD KOTTA has been used in production for several months and currently manages approximately 10TB of data and has been used to process more than 5TB of data with over 75,000 CPU hours. It has been used for a broad variety of text analysis workflows, matrix factorization, and various machine learning algorithms, and more broadly, it supports fast, secure and cost-effective research.

Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models.This paper introduces two probabilistic topic models, Correlated LDA (C-LDA) and Correlated HDP (C-HDP).These address problems that can arise when analyzing large, asymmetric, and potentially weakly-related collections.Topic correlations in weakly-related collections typically lie in the tail of the topic distribution, where they would be overlooked by models unable to fit large numbers of topics.To efficiently model this long tail for large-scale analysis, our models implement a parallel sampling algorithm based on the Metropolis-Hastings and alias methods (Yuan et al., 2015).The models are first evaluated on synthetic data, generated to simulate various collection-level asymmetries.We then present a case study of modeling over 300k documents in collections of sciences and humanities research from JSTOR.

Automatically identifying related specialist terms is a difficult and important task required to understand the lexical structure of language.This paper develops a corpus-based method of extracting coherent clusters of satellite terminologyterms on the edge of the lexicon -using co-occurrence networks of unstructured text.Term clusters are identified by extracting communities in the cooccurrence graph, after which the largest is discarded and the remaining words are ranked by centrality within a community.The method is tractable on large corpora, requires no document structure and minimal normalization.The results suggest that the model is able to extract coherent groups of satellite terms in corpora with varying size, content and structure.The findings also confirm that language consists of a densely connected core (observed in dictionaries) and systematic, semantically coherent groups of terms at the edges of the lexicon.