Complex video queries can be answered by decomposing them into modular subtasks. However, existing video data management systems assume the existence of predefined modules for each subtask. We introduce VOCAL-UDF, a novel self-enhancing system that supports compositional queries over videos without the need for predefined modules. VOCAL-UDF automatically identifies and constructs missing modules and encapsulates them as user-defined functions (UDFs), thus expanding its querying capabilities. To achieve this, we formulate a unified UDF model that leverages large language models (LLMs) to aid in new UDF generation. VOCAL UDF handles a wide range of concepts by supporting both program-based UDFs (i.e., Python functions generated by LLMs) and distilled-model UDFs (lightweight vision models distilled from strong pretrained models). To resolve the inherent ambiguity in user intent, VOCAL-UDF generates multiple candidate UDFs and uses active learning to efficiently select the best one. With the self-enhancing capability, VOCAL-UDF significantly improves query performance across three video datasets.

Abstract This paper evaluates the suitability of Apache Arrow, Parquet, and ORC as formats for subsumption in an analytical DBMS. We systematically identify and explore the high-level features that are important to support efficient querying in modern OLAP DBMSs and evaluate the ability of each format to support these features. We find that each format has trade-offs that make it more or less suitable for use as a format in a DBMS and identify opportunities to more holistically co-design a unified in-memory and on-disk data representation. Notably, for certain popular machine learning tasks, none of these formats perform optimally, highlighting significant opportunities for advancing format design. Our hope is that this study can be used as a guide for system developers designing and using these formats, as well as provide the community with directions to pursue for improving these common open formats.

research-article Share on Query Processing on Gaming Consoles Authors: Wei Cui Microsoft Research Asia, CN Microsoft Research Asia, CN 0009-0005-9362-3585View Profile , Qianxi Zhang Microsoft Research Asia, CN Microsoft Research Asia, CN 0000-0002-0646-5365View Profile , Spyros Blanas The Ohio State University, US The Ohio State University, US 0009-0004-2703-7177View Profile , Jesús Camacho-Rodríguez Microsoft, US Microsoft, US 0009-0008-9151-6024View Profile , Brandon Haynes Microsoft Gray Systems Lab, US Microsoft Gray Systems Lab, US 0000-0002-1501-9586View Profile , Yinan Li Microsoft Research, US Microsoft Research, US 0009-0004-5483-2862View Profile , Ravi Ramamurthy Microsoft, USA Microsoft, USA 0000-0002-3484-0038View Profile , Peng Cheng Microsoft Research, CN Microsoft Research, CN 0000-0003-4014-4757View Profile , Rathijit Sen Microsoft, US Microsoft, US 0000-0003-4736-2837View Profile , Matteo Interlandi Microsoft, US Microsoft, US 0000-0002-5756-8321View Profile Authors Info & Claims DaMoN '23: Proceedings of the 19th International Workshop on Data Management on New HardwareJune 2023Pages 86–88https://doi.org/10.1145/3592980.3595313Published:18 June 2023Publication History 0citation191DownloadsMetricsTotal Citations0Total Downloads191Last 12 Months191Last 6 weeks191 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number of analytic jobs processing huge volumes of data on a daily basis, and workloads are often inundated with overlapping computations across multiple jobs. Reusing common computation is crucial for efficient cluster resource utilization and reducing job execution time. Detecting common computation is the first and key step for reducing this computational redundancy. However, detecting equivalence on large-scale analytics engines requires efficient and scalable solutions that are fully automated. In addition, to maximize computation reuse, equivalence needs to be detected at the semantic level instead of just the syntactic level (i.e., the ability to detect semantic equivalence of seemingly different-looking queries). Unfortunately, existing solutions fall short of satisfying these requirements. In this paper, we take a major step towards filling this gap by proposing GEqO, a portable and lightweight machine-learning-based framework for efficiently identifying semantically equivalent computations at scale. GEqO introduces two machine-learning-based filters that quickly prune out nonequivalent subexpressions and employs a semi-supervised learning feedback loop to iteratively improve its model with an intelligent sampling mechanism. Further, with its novel database-agnostic featurization method, GEqO can transfer the learning from one workload and database to another. Our extensive empirical evaluation shows that, on TPC-DS-like queries, GEqO yields significant performance gains-up to 200x faster than automated verifiers-and finds up to 2x more equivalences than optimizer and signature-based equivalence detection approaches.

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.

We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets. VOCALExplore supports interactive labeling sessions and trains models using user-supplied labels. VOCALExplore maximizes model quality by automatically deciding how to select samples based on observed skew in the collected labels. It also selects the optimal video representations to use when training models by casting feature selection as a rising bandit problem. Finally, VOCALExplore implements optimizations to achieve low latency without sacrificing model performance. We demonstrate that VOCALExplore achieves close to the best possible model quality given candidate acquisition functions and feature extractors, and it does so with low visible latency (~1 second per iteration) and no expensive preprocessing.

This paper evaluates the suitability of Apache Arrow, Parquet, and ORC as formats for subsumption in an analytical DBMS. We systematically identify and explore the high-level features that are important to support efficient querying in modern OLAP DBMSs and evaluate the ability of each format to support these features. We find that each format has trade-offs that make it more or less suitable for use as a format in a DBMS and identify opportunities to more holistically co-design a unified in-memory and on-disk data representation. Our hope is that this study can be used as a guide for system developers designing and using these formats, as well as provide the community with directions to pursue for improving these common open formats.

We demonstrate EQUI-VOCAL, a system that synthesizes compositional queries over videos from user feedback. EQUI-VOCAL enables users to query a video database for complex events by providing a few positive and negative examples of what they are looking for and labeling a small number of additional system-selected examples. Using those user inputs, EQUI-VOCAL synthesizes declarative queries that can then retrieve additional instances of the desired events. The demonstration makes two contributions: it introduces EQUI-VOCAL's graphical user interface and enables conference attendees to experiment with EQUI-VOCAL on a variety of queries. Both enable users to gain a better understanding of EQUI-VOCAL's query synthesis approach and to explore the impact of hyperparameters and label noise on system performance.

We introduce EQUI-VOCAL: a new system that automatically synthesizes queries over videos from limited user interactions. The user only provides a handful of positive and negative examples of what they are looking for. EQUI-VOCAL utilizes these initial examples and additional ones collected through active learning to efficiently synthesize complex user queries. Our approach enables users to find events without database expertise, with limited labeling effort, and without declarative specifications or sketches. Core to EQUI-VOCAL's design is the use of spatio-temporal scene graphs in its data model and query language and a novel query synthesis approach that works on large and noisy video data. Our system outperforms two baseline systems---in terms of F1 score, synthesis time, and robustness to noise---and can flexibly synthesize complex queries that the baselines do not support.

We demonstrate Pipemizer , an optimizer and recommender aimed at improving the performance of queries or jobs in pipelines. These job pipelines are ubiquitous in modern data analytics due to jobs reading output files written by other jobs. Given that more than 650k jobs run on Microsoft's SCOPE job service per day and about 70% have inter-job dependencies, identifying optimization opportunities across query jobs is of considerable interest to both cluster operators and users. Pipemizer addresses this need by providing recommendations to users, allowing users to understand their system, and facilitating automated application of recommendations. Pipemizer introduces novel optimizations that include holistic pipeline-aware statistics generation, inter-job operator push-up, and job split & merge. This demonstration showcases optimizations and recommendations generated by Pipemizer , enabling users to understand and optimize job pipelines.

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×.

We present a new video storage system (VSS) designed to decouple high-level video operations from the low-level details required to store and efficiently retrieve video data. VSS is designed to be the storage subsystem of a video data management system (VDBMS) and is responsible for: (1) transparently and automatically arranging the data on disk in an efficient, granular format; (2) caching frequently-retrieved regions in the most useful formats; and (3) eliminating redundancies found in videos captured from multiple cameras with overlapping fields of view. Our results suggest that VSS can improve VDBMS read performance by up to 54%, reduce storage costs by up to 45%, and enable developers to focus on application logic rather than video storage and retrieval.

Many recent video applications---including traffic monitoring, drone analytics, autonomous driving, and virtual reality---require piecing together, combining, and operating over many related video streams. Despite the massive data volumes involved and the need to jointly reason (both spatially and temporally) about these videos, current techniques to store and manipulate such data are often limited to file systems and simple video processing frameworks that reason about a single video in isolation. We present Apperception, a new type of database management system optimized for geospatial video applications. Apperception comes with an easy to use data model to reason about multiple geospatial video data streams, and a programming interface for developers to collectively reason about the entities observed in those videos. Our demo will let users write queries over video using Apperception and retrieve (in real-time) both metadata and rendered video data. Users can also compare results and observe speedups achieved by using Apperception.

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 of LightDB, a database management system designed to efficiently manage virtual, augmented, and mixed reality (VAMR) video content. VAMR video differs from its two-dimensional counterpart in that it is spherical with periodic angular dimensions, is nonuniformly and continuously sampled, and applications that consume such videos often have demanding latency and throughput requirements. To address these challenges, LightDB treats VAMR video data as a logically-continuous six-dimensional light field. Furthermore, LightDB supports a rich set of operations over light fields, and automatically transforms declarative queries into executable physical plans. We have implemented a prototype of LightDB and, through experiments with VAMR applications in the literature, we find that LightDB offers up to 4× throughput improvements compared with prior work.

We demonstrate VisualCloud, a database management system designed to efficiently ingest, store, and deliver virtual reality (VR) content at scale. VisualCloud targets both live and prerecorded spherical panoramic (a.k.a. 360°) VR videos. It persists content as a multidimensional array that utilizes both dense (e.g., space and time) and sparse (e.g., bitrate) dimensions. VisualCloud uses orientation prediction to reduce data transfer by degrading out-of-view portions of the video. Content delivered through VisualCloud requires up to 60% less bandwidth than existing methods and scales to many concurrent connections.