Enabling Interactive Analytics of Secure Data using Cloud Kotta

Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page

A Secure Data Enclave and Analytics Platform for Social Scientists

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

Cloud Kotta: Enabling Secure and Scalable Data Analytics in the Cloud

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 Mining Research with In-copyright and Use-limited Text Datasets: Preliminary Findings from a Systematic Literature Review and Stakeholder Interviews

Text data mining and analysis has emerged as a viable research method for scholars, following the growth of mass digitization, digital publishing, and scholarly interest in data re-use. Yet the texts that comprise datasets for analysis are frequently protected by copyright or other intellectual property rights that limit their access and use. This paper discusses the role of libraries at the intersection of data mining and intellectual property, asserting that academic libraries are vital partners in enabling scholars to effectively incorporate text data mining into their research. We report on activities leading up to an IMLS-funded National Forum of stakeholders and discuss preliminary findings from a systematic literature review, as well as initial results of interviews with forum stakeholders. Emerging themes suggest the need for a multi-pronged distributed approach that includes a public campaign for building awareness and advocacy, development of best practice guides for library support services and training, and international efforts toward data standardization and copyright harmonization.Institute of Museum and Library Services (LG-73-17-0070-17)Ope

Virtual Cluster Management for Analysis of Geographically Distributed and Immovable Data

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Scenarios exist in the era of Big Data where computational analysis needs to utilize widely distributed and remote compute clusters, especially when the data sources are sensitive or extremely large, and thus unable to move. A large dataset in Malaysia could be ecologically sensitive, for instance, and unable to be moved outside the country boundaries. Controlling an analysis experiment in this virtual cluster setting can be difficult on multiple levels: with setup and control, with managing behavior of the virtual cluster, and with interoperability issues across the compute clusters. Further, datasets can be distributed among clusters, or even across data centers, so that it becomes critical to utilize data locality information to optimize the performance of data-intensive jobs. Finally, datasets are increasingly sensitive and tied to certain administrative boundaries, though once the data has been processed, the aggregated or statistical result can be shared across the boundaries. This dissertation addresses management and control of a widely distributed virtual cluster having sensitive or otherwise immovable data sets through a controller. The Virtual Cluster Controller (VCC) gives control back to the researcher. It creates virtual clusters across multiple cloud platforms. In recognition of sensitive data, it can establish a single network overlay over widely distributed clusters. We define a novel class of data, notably immovable data that we call "pinned data", where the data is treated as a first-class citizen instead of being moved to where needed. We draw from our earlier work with a hierarchical data processing model, Hierarchical MapReduce (HMR), to process geographically distributed data, some of which are pinned data. The applications implemented in HMR use extended MapReduce model where computations are expressed as three functions: Map, Reduce, and GlobalReduce. Further, by facilitating information sharing among resources, applications, and data, the overall performance is improved. Experimental results show that the overhead of VCC is minimum. The HMR outperforms traditional MapReduce model while processing a particular class of applications. The evaluations also show that information sharing between resources and application through the VCC shortens the hierarchical data processing time, as well satisfying the constraints on the pinned data

Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library

We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted

Resource Sharing for Multi-Tenant Nosql Data Store in Cloud

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system. In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy. In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not designed for. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads