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After decades of progress, database management systems (DBMSs) are now the backbones of many data applications that we interact with on a daily basis. Yet, with the emergence of new data types and hardware, building and optimizing new data systems remain as difficult as the heyday of relational databases. In this paper, we summarize our work towards automating the building and optimization of data systems. Drawing from our own experience, we further argue that any automation technique must address three aspects: user specification, code generation, and result validation. We conclude by discussing a case study using videos data processing, along with opportunities for future research towards designing data systems that are automatically generated. 

Current video database management systems (VDBMSs) fail to support the growing number of video datasets in diverse domains because these systems assume clean data and rely on pretrained models to detect known objects or actions. Existing systems also lack good support for compositional queries that seek events con- sisting of multiple objects with complex spatial and temporal rela- tionships. In this paper, we propose VOCAL, a vision of a VDBMS that supports efficient data cleaning, exploration and organization, and compositional queries, even when no pretrained model exists to extract semantic content. These techniques utilize optimizations to minimize the manual effort required of users. 

Modern video data management systems store videos as a single encoded file, which significantly limits possible storage level optimizations. We design, implement, and evaluate TASM, a new tile-based storage manager for video data. TASM uses a feature in modern video codecs called "tiles" that enables spatial random access into encoded videos. TASM physically tunes stored videos by optimizing their tile layouts given the video content and a query workload. Additionally, TASM dynamically tunes that layout in response to changes in the query workload or if the query workload and video contents are incrementally discovered. Finally, TASM also produces efficient initial tile layouts for newly ingested videos. We demonstrate that TASM can speed up subframe selection queries by an average of over 50% and up to 94%. TASM can also improve the throughput of the full scan phase of object detection queries by up to 2×. 

Compressed videos constitute 70% of Internet traffic, and video upload growth rates far outpace compute and storage improvement trends. Past work in leveraging perceptual cues like saliency, i.e., regions where viewers focus their perceptual attention, reduces compressed video size while maintaining perceptual quality, but requires significant changes to video codecs and ignores the data management of this perceptual information. In this paper, we propose Vignette, a compression technique and storage manager for perception-based video compression in the cloud. Vignette complements off-the-shelf compression software and hardware codec implementations. Vignette's compression technique uses a neural network to predict saliency information used during transcoding, and its storage manager integrates perceptual information into the video storage system. Our results demonstrate the benefit of embedding information about the human visual system into the architecture of cloud video storage systems. 

Recently, video database management systems (VDBMSs) have re-emerged as an active area of research and development. To accelerate innovation in this area, we present Visual Road, a benchmark that evaluates the performance of these systems. Visual Road comes with a data generator and a suite of queries over cameras positioned within a simulated metropolitan environment. Visual Road's video data is automatically generated with a high degree of realism, and annotated using a modern simulation and visualization engine. This allows for VDBMS performance evaluation while scaling up the size of the input data. Visual Road is designed to evaluate a broad variety of VDBMSs: real-time systems, systems for longitudinal analytical queries, systems processing traditional videos, and systems designed for 360 videos. We use the benchmark to evaluate three recent VDBMSs both in capabilities and performance. 

We present the data model, architecture, and evaluation ofLightDB, a database management system designed to efficientlymanage virtual, augmented, and mixed reality (VAMR) video con-tent. VAMR video differs from its two-dimensional counterpartin that it is spherical with periodic angular dimensions, is nonuni-formly and continuously sampled, and applications that consumesuch videos often have demanding latency and throughput require-ments. To address these challenges, LightDB treats VAMR videodata as a logically-continuous six-dimensional light field. Further-more, LightDB supports a rich set of operations over light fields,and automatically transforms declarative queries into executablephysical plans. We have implemented a prototype of LightDB and,through experiments with VAMR applications in the literature, wefind that LightDB offers up to 4×throughput improvements com-pared with prior work.