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1. Building an end-to-end BAD application

   https://doi.org/10.1145/3465480.3467840  

   Shirazi, Shahrzad Haji; Carey, Michael J.; Tsotras, Vassilis J. (June 2021, Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems)

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   Traditional big data infrastructures are passive in nature, passively answering user requests to process and return data. In many applications however, users not only need to analyze data, but also to subscribe to and actively receive data of interest, based on their subscriptions. Their interest may include the incoming data's content as well as its relationships to other data. Moreover, data delivered to subscribers may need to be enriched with additional relevant and actionable information. To address this Big Active Data (BAD) challenge we have advocated the need for building scalable BAD systems that continuously and reliably capture big data while enabling timely and automatic delivery of relevant and possibly enriched information to a large pool of subscribers. In this demo we showcase how to build an end-to-end active application using a BAD system and a standard email broker for data delivery. This includes enabling users to register their interests with the bad system, ingesting and monitoring data, and producing customized results and delivering them to the appropriate subscribers. Through this example we demonstrate that even complex active data applications can be created easily and scale to many users, considerably limiting the effort of application developers, if a BAD approach is taken. 

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2. Implementation of an end-to-end gradual verification system

   https://doi.org/10.1145/3484271.3484980  

   Gouni, Hemant; Zimmerman, Conrad (October 2021, SPLASH Companion ’21, October 17–22, 2021, Chicago, IL, USA)

   Static verification is used to ensure the correctness of programs. While useful in critical applications, the high overhead associated with writing specifications limits its general applicability. Similarly, the run-time costs introduced by dynamic verification limit its practicality. Gradual verification validates partially specified code statically where possible and dynamically where necessary. As a result, software developers gain granular control over the trade-offs between static and dynamic verification. This paper contains an end-to-end presentation of gradual verification in action, with a focus on applying it to 𝐶0 (a safe subset of C) and implementing the required dynamic verification. 

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3. EVA: An End-to-End Exploratory Video Analytics System

   https://doi.org/10.1145/3595360.3595858  

   Kakkar, Gaurav Tarlok; Cao, Jiashen; Chunduri, Pramod; Xu, Zhuangdi; Vyalla, Suryatej Reddy; Dintyala, Prashanth; Prabakaran, Anirudh; Bang, Jaeho; Sengupta, Aubhro; Ravichandran, Kaushik; et al (June 2023, ACM)

   In recent years, deep learning models have revolutionized computer vision, enabling diverse applications. However, these models are computationally expensive, and leveraging them for video analyt- ics involves low-level imperative programming. To address these efficiency and usability challenges, the database community has de- veloped video database management systems (VDBMSs). However, existing VDBMSs lack extensibility and composability and do not support holistic system optimizations, limiting their practical appli- cation. In response to these issues, we present our vision for EVA, a VDBMS that allows for extensible support of user-defined functions and employs a Cascades-style query optimizer. Additionally, we leverage RAY’s distributed execution to enhance scalability and performance and explore hardware-specific optimizations to facilitate runtime optimizations. We discuss the architecture and design of EVA, our achievements thus far, and our research roadmap. 

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4. Avalanche: an End-to-End Library for Continual Learning

   Lomonaco, V.; Pellegrini, L.; Cossu, A.; Carta, A.; Graffieti, G.; Hayes, T.; De Lange, M.; Masana, M.; Pomponi, J.; Ven, G.; et al (January 2021, CLVISION)

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   Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms. 

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5. Aging-resilient SRAM design: an end-to-end framework

   https://doi.org/10.1109/VTS48691.2020.9107571  

   Zuo, Xuan; Gupta, Sandeep K. (April 2020, IEEE VLSI Test Symposium (VTS))

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