This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subsequent impacts. We designed the DCPViz pipeline to fetch and extract NEX-DCP30 data with minimal data transfer from their public sources. We implemented DCPViz to demonstrate its scalability and scientific value and to evaluate its utility under three use cases based on different models and through domain expert feedback.

This paper proposes a visual analytics framework that addresses the complex user interactions required through a command-line interface to run analyses in distributed data analysis systems. The visual analytics framework facilitates the user to manage access to the distributed servers, incorporate data from the source, run data-driven analysis, monitor the progress, and explore the result using interactive visualizations. We provide a user interface embedded with generalized functionalities and access protocols and integrate it with a distributed analysis system. To demonstrate our proof of concept, we present two use cases from the earth science and Sustainable Human Building Ecosystem research domain.

In this paper, we propose an effective, multi-view, generative, transfer learning framework for multivariate time-series data. While generative models are demonstrated effective for several machine learning tasks, their application to time-series classification problems is underexplored. The need for additional exploration is motivated when data are large, annotations are unbalanced or scarce, or data are distributed and fragmented. Recent advances in computer vision attempt to use synthesized samples with system generated annotations to overcome the lack or imbalance of annotated data. However, in multi-view problem settings, view mismatches between the synthetic data and real data pose additional challenges against harnessing new annotated data collections. The proposed method offers important contributions to facilitate knowledge sharing, while simultaneously ensuring an effective solution for domain-specific, finelevel categorizations. We propose a principled way to perform view adaptation in a cross-view learning environment, wherein pairwise view similarity is identified by a smaller subset of source samples that closely resemble the target data patterns. This approach integrates generative models within a deep classification framework to minimize the gap between source and target data. More precisely, we design category specific conditional, generative models to update the source generator in order for transforming source features so that they appear as target features and simultaneously tune the associated discriminative model to distinguish these features. During each learning iteration, the source generator is conditioned by a source training set represented as some target-like features. This transformation in appearance was performed via a target generator specifically learned for target-specific customization per category. Afterward, a smaller source training set, indicating close target pattern resemblance in terms of the corresponding generative and discriminative loss, is used to fine-tune the source classification model parameters. Experiments show that compared to existing approaches, our proposed multiview, generative, transfer learning framework improves timeseries classification performance by around 4% in the UCI multiview activity recognition dataset, while also showing a robust, generalized representation capacity in classifying several large-scale multi-view light curve collections.

In this paper, we propose an effective, multi-view, multivariate deep classification model for time-series data. Multi-view methods show promise in their ability to learn correlation and exclusivity properties across different independent information resources. However, most current multi-view integration schemes employ only a linear model and, therefore, do not extensively utilize the relationships observed across different view-specific representations. Moreover, the majority of these methods rely exclusively on sophisticated, handcrafted features to capture local data patterns and, thus, depend heavily on large collections of labeled data. The multi-view, multivariate deep classification model for time-series data proposed in this paper makes important contributions to address these limitations. The proposed model derives a LSTM-based, deep feature descriptor to model both the view-specific data characteristics and cross-view interaction in an integrated deep architecture while driving the learning phase in a data-driven manner. The proposed model employs a compact context descriptor to exploit view-specific affinity information to design a more insightful context representation. Finally, the model uses a multi-view factor-pooling scheme for a context-driven attention learning strategy to weigh the most relevant feature dimensions while eliminating noise from the resulting fused descriptor. As shown by experiments, compared to the existing multi-view methods, the proposed multi-view deep sequential learning approach improves classification performance by roughly 4% in the UCI multi-view activity recognition dataset, while also showing significantly robust generalized representation capacity against its single-view counterparts, in classifying several large-scale multi-view light curve collections.

Big data analytics traditionally involves download of massive amounts of datasets to common server/cluster for processing. Analytic process gets slower with increasing size of required data and network conditions. Data scientists also need explicit access to data locations to download required data. Explicit access to required data may not always be granted due to security reasons. To simplify and accelerate the analytics process on distributed big data with security considerations, we proposed the Virtual Information Fabric Infrastructure (VIFI) for data driven discoveries. Instead of moving large amounts of data to a common place of processing, VIFI allows automatic transfer of required analytics programs to the distributed data locations for in-place processing of relevant data. VIFI allows data scientists to conduct and coordinate complex analytics processes on distributed data repositories using containerization technology and open-source workflow design tools. VIFI alleviates users from having detailed knowledge of distributed data locations, as well as required dependencies, installation and configuration of analytical libraries. In this paper, we demonstrate our current and future work to improve the VIFI architecture using previous and additional uses cases, data management layer that simplifies search of relevant data sets through addition of metadata, integration with security policies at different institutions with the proposed VIFI security layer, and the use of a user-friendly web interface to carry different VIFI activities.