Modern video analytics requires efficient machine learning model serving and evaluation. We present Deluceva, a system that optimizes video applications by applying incremental and approximate computation techniques. Experiments on three real models and six videos show that our prototype system can achieve significant performance gains up to 79% with F1 errors below 0.1.

Commercial DBMSs, such as Microsoft SQL Server, cater to diverse workloads including transaction processing, decision support, and operational analytics. They also support variety in physical design structures such as B+ tree and columnstore. The benefits of B+ tree for OLTP workloads and columnstore for decision support workloads are well-understood. However, the importance of hybrid physical designs, consisting of both columnstore and B+ tree indexes on the same database, is not well-studied --- a focus of this paper. We first quantify the trade-offs using carefully-crafted micro-benchmarks. This micro-benchmarking indicates that hybrid physical designs can result in orders of magnitude better performance depending on the workload. For complex real-world applications, choosing an appropriate combination of columnstore and B+ tree indexes for a database workload is challenging. We extend the Database Engine Tuning Advisor for Microsoft SQL Server to recommend a suitable combination of B+ tree and columnstore indexes for a given workload. Through extensive experiments using industry-standard benchmarks and several real-world customer workloads, we quantify how a physical design tool capable of recommending hybrid physical designs can result in orders of magnitude better execution costs compared to approaches that rely either on columnstore-only or B+ tree-only designs.

We present a new approach for data analytics with iterations. Users express their analysis in Datalog with bag-monotonic aggregate operators, which enables the expression of computations from a broad variety of application domains. Queries are translated into query plans that can execute in shared-nothing engines, are incremental, and support a variety of iterative models (synchronous, asynchronous, different processing priorities) and failure-handling techniques. The plans require only small extensions to an existing shared-nothing engine, making the approach easily implementable. We implement the approach in the Myria big-data management system and use our implementation to empirically study the performance characteristics of different combinations of iterative models, failure handling methods, and applications. Our evaluation uses workloads from a variety of application domains. We find that no single method outperforms others but rather that application properties must drive the selection of the iterative query execution model.

In this demonstration, we will showcase Myria, our novel cloud service for big data management and analytics designed to improve productivity. Myria's goal is for users to simply upload their data and for the system to help them be self-sufficient data science experts on their data -- self-serve analytics. Using a web browser, Myria users can upload data, author efficient queries to process and explore the data, and debug correctness and performance issues. Myria queries are executed on a scalable, parallel cluster that uses both state-of-the-art and novel methods for distributed query processing. Our interactive demonstration will guide visitors through an exploration of several key Myria features by interfacing with the live system to analyze big datasets over the web.