Unlike traditional object stores, Augmented Reality (AR) query workloads possess several unique characteristics, such as spatial and visual information. Such workloads are often keyed on a variety of attributes simultaneously, such as device orientation and position, the scene in view, and spatial anchors. The natural mode of user-interaction in these devices triggers queries implicitly based on the field in the user's view at any instant, generating data queries in excess of the device frame rate. Ensuring a smooth user experience in such a scenario requires a systemic solution exploiting the unique characteristics of the AR workloads. For exploration in such contexts, we are presented with a view-maintenance or cache-prefetching problem; how do we download the smallest subset from the server to the mixed reality device such that latency and device space constraints are met? We present a novel data platform - DreamStore, that considers AR queries as first-class queries, and view-maintenance and large-scale analytics infrastructure around this design choice. Through performance experiments on large-scale and query-intensive AR workloads on DreamStore, we show the advantages and the capabilities of our proposed platform.

Interactive visualizations have become an effective and pervasive mode of allowing users to explore the data in a visual, fluid, and immersive manner. While modern web, mobile, touch, and gesturedriven next-generation interfaces such as Leap Motion allow for highly interactive experiences, they pose unique and unprecedented workloads to the underlying data platform. Usually, these visualizations do not need precise results for most queries generated during an interaction, and the users require the intermediate results as feedback only to guide them towards their goal query. We present a middleware component - Flux Capacitor, that insulates the backend from bursty and query-intensive workloads. Flux Capacitor uses prefetching and caching strategies devised by exploiting the inherent physics-metaphor of UI widgets such as friction and inertia in range sliders, and typical characteristics of user-interaction. This enables low interaction response times while intelligently trading off accuracy

In the context of interactive query sessions, it is common to issue a succession of queries, transforming a dataset to the desired result. It is often difficult to comprehend a succession of transformations, especially for complex queries. Thus, to facilitate understanding of each data transformation and to provide continuous feedback, we introduce the concept of "data tweening", i.e., interpolating between resultsets, presenting to the user a series of incremental visual representations of a resultset transformation. We present tweening methods that consider not just the changes in the result, but also the changes in the query. Through user studies, we show that data tweening allows users to efficiently comprehend data transforms, and also enables them to gain a better understanding of the underlying query operations.

With the development and advancement of new data interaction modalities, data exploration and analysis has become a highly interactive process situating the user in a session of successive queries. With rapidly changing results, it becomes difficult for the end user to fully comprehend transformations, especially the transforms corresponding to complex queries. We introduce "data tweening" as an informative way of visualizing structural data transforms, presenting the users with a series of incremental visual representations of a resultset transformation. We present transformations as ordered sequences of basic structural transforms and visual cues. The sequences are generated using an automated framework which utilizes differences between the consecutive resultsets and queries in a query session. We evaluate the effectiveness of tweening as a visualization method through a user study.