The rapid expansion of video streaming content in our daily lives has rendered the real-time processing and analysis of these video streams a critical capability. However, existing deep video analytics systems primarily support only simple queries, such as selection and aggregation. Considering the inherent temporal nature of video streams, queries capable of matching patterns of events could enable a wider range of applications. In this paper, we present Bobsled, a novel video stream processing system designed to efficiently support complex event queries. Experimental results demonstrate that Bobsled can achieve a throughput improvement over state-of-the-art ranging from 2.4× to 11.6×, without any noticeable loss in accuracy.

Detection and localization of actions in videos is an important problem in practice. State-of-the-art video analytics systems are unable to efficiently and effectively answer such action queries because actions often involve a complex interaction between objects and are spread across a sequence of frames; detecting and localizing them requires computationally expensive deep neural networks. It is also important to consider the entire sequence of frames to answer the query effectively.

Recent learned cardinality estimation (CE) models are vulnerable when query predicates or the underlying datasets drift from what the models were trained upon. We propose a system Warper that accelerates model adaptation to drifts; Warper generates additional queries when limited examples are available from the new workload and carefully picks which queries to use to update the CE model. We show that Warper can be used to adapt different CE models including ones that support queries over single tables and join expressions. Experiments with different drifts suggest that Warper has a small computational cost and adapts much faster compared to state-of-the-art solutions. We also show that faster model adaptation improves query performance by shortening the period for which imperfect query plans are picked by a query optimizer due to incorrect cardinality estimates.

With the advent of ubiquitous deployment of smart devices and the Internet of Things, data sources for machine learning inference have increasingly moved to the edge of the network. Existing machine learning inference platforms typically assume a homogeneous infrastructure and do not take into account the more complex and tiered computing infrastructure that includes edge devices, local hubs, edge datacenters, and cloud datacenters. On the other hand, recent AutoML efforts have provided viable solutions for model compression, pruning and quantization for heterogeneous environments; for a machine learning model, now we may easily find or even generate a series of model variants with different tradeoffs between accuracy and efficiency. We design and implement JellyBean, a system for serving and optimizing machine learning inference workflows on heterogeneous infrastructures. Given service-level objectives (e.g., throughput, accuracy), JellyBean picks the most cost-efficient models that meet the accuracy target and decides how to deploy them across different tiers of infrastructures. Evaluations show that JellyBean reduces the total serving cost of visual question answering by up to 58% and vehicle tracking from the NVIDIA AI City Challenge by up to 36%, compared with state-of-the-art model selection and worker assignment solutions. JellyBean also outperforms prior ML serving systems (e.g., Spark on the cloud) up to 5x in serving costs.

We will demonstrate a prototype query-processing engine, which utilizes correlations among predicates to accelerate machine learning (ML) inference queries on unstructured data. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable by classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) build a cheap proxy model for each predicate offline, and inject proxy models in the front of expensive ML UDFs under the independence assumption in queries. Input records that do not satisfy query predicates are filtered early by proxy models to bypass ML UDFs. But enforcing the independence assumption may result in sub-optimal plans. We use correlative proxy models to better exploit predicate correlations and accelerate ML queries. We will demonstrate our query optimizer called CORE, which builds proxy models online, allocates parameters to each model, and reorders them. We will also show end-to-end query processing with or without proxy models.

We consider accelerating machine learning (ML) inference queries on unstructured datasets. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable with classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) assume independence among the query predicates, build a proxy model for each predicate offline, and rewrite a new query by injecting these cheap proxy models in the front of the expensive ML UDFs. In such a manner, unlikely inputs that do not satisfy query predicates are filtered early to bypass the ML UDFs. We show that enforcing the independence assumption in this context may result in sub-optimal plans. In this paper, we propose CORE, a query optimizer that better exploits the predicate correlations and accelerates ML inference queries. Our solution builds the proxy models online for a new query and leverages a branch-and-bound search process to reduce the building costs. Results on three real-world text, image and video datasets show that CORE improves the query throughput by up to 63% compared to PP and up to 80% compared to running the queries as it is.

We consider the problem of pre-training models which convert structured datasets into succinct summaries that can be used to answer cardinality estimation queries. Doing so avoids per-dataset training and, in our experiments, reduces the time to construct summaries by up to 100×. When datasets change, our summaries are incrementally updateable. Our key insights are to use multiple summaries per dataset, use learned summaries for columnsets for which other simpler techniques do not achieve high accuracy, and that analogous to similar pre-trained models for images and text, structured datasets have some common frequency and correlation patterns which our models learn to capture by pre-training on a large and diverse corpus of datasets.

Locating actions in long untrimmed videos has been a challenging problem in video content analysis. The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action. Imitating the human perception procedure with observations and refinements, we propose a novel three-phase action localization framework. Our framework is embedded with an Actionness Network to generate initial proposals through frame-wise similarity grouping, and then a Refinement Network to conduct boundary adjustment on these proposals. Finally, the refined proposals are sent to a Localization Network for further fine-grained location regression. The whole process can be deemed as multi-stage refinement using a novel non-local pyramid feature under various temporal granularities. We evaluate our framework on THUMOS14 benchmark and obtain a significant improvement over the state-of-the-arts approaches. Specifically, the performance gain is remarkable under precise localization with high IoU thresholds. Our proposed framework achieves [email protected]=0.5 of 34.2%.

Classic query optimization techniques, including predicate pushdown, are of limited use for machine learning inference queries, because the user-defined functions (UDFs) which extract relational columns from unstructured inputs are often very expensive; query predicates will remain stuck behind these UDFs if they happen to require relational columns that are generated by the UDFs. In this work, we demonstrate constructing and applying probabilistic predicates to filter data blobs that do not satisfy the query predicate; such filtering is parametrized to different target accuracies. Furthermore, to support complex predicates and to avoid per-query training, we augment a cost-based query optimizer to choose plans with appropriate combinations of simpler probabilistic predicates. Experiments with several machine learning workloads on a big-data cluster show that query processing improves by as much as 10x.

We will demonstrate a prototype query processing engine that uses probabilistic predicates (PPs) to speed up machine learning inference jobs. In current analytic engines, machine learning functions are modeled as user-defined functions (UDFs) which are both time and resource intensive. These UDFs prevent predicate pushdown; predicates that use the outputs of these UDFs cannot be pushed to before the UDFs. Hence, considerable time and resources are wasted in applying the UDFs on inputs that will be rejected by the subsequent predicate. We uses PPs that are lightweight classifiers applied directly on the raw input and filter data blobs that disagree with the query predicate. By reducing the input to be processed by the UDFs, PPs substantially improve query processing. We will show that PPs are broadly applicable by constructing PPs for many inference tasks including image recognition, document classification and video analyses. We will also demonstrate query optimization methods that extend PPs to complex query predicates and support different accuracy requirements.

There are a large number of images available on the web; meanwhile, only a subset of web images can be labeled by professionals because manual annotation is time-consuming and labor-intensive. Although we can now use the collaborative image tagging system, e.g., Flickr, to get a lot of tagged images provided by Internet users, these labels may be incorrect or incomplete. Furthermore, semantics richness requires more than one label to describe one image in real applications, and multiple labels usually interact with each other in semantic space. It is of significance to learn semantic context with large-scale weakly-labeled image set in the task of multi-label annotation. In this paper, we develop a novel method to learn semantic context and predict the labels of web images in a semi-supervised framework. To address the scalability issue, a small number of exemplar images are first obtained to cover the whole data cloud; then the label vector of each image is estimated as a local combination of the exemplar label vectors. Visual context, semantic context, and neighborhood consistency in both visual and semantic spaces are sufficiently leveraged in the proposed framework. Finally, the semantic context and the label confidence vectors for exemplar images are both learned in an iterative way. Experimental results on the real-world image dataset demonstrate the effectiveness of our method.