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1. GRETA: Graph-based Real-time Event Trend Aggregation

   https://doi.org/10.14778/3151113.3151120  

   Poppe, Olga; Lei, Chuan; Rundensteiner, Elke; Maier, David (August 2018, Proceedings of the VLDB Endowment)

   null (Ed.)

   Streaming applications from algorithmic trading to traffic management deploy Kleene patterns to detect and aggregate arbitrarily-long event sequences, called event trends. State-of-the-art systems process such queries in two steps. Namely, they first construct all trends and then aggregate them. Due to the exponential costs of trend construction, this two-step approach su↵ers from both a long delays and high memory costs. To overcome these limitations, we propose the Graph-based Real-time Event Trend Aggregation (GRETA) approach that dynamically computes event trend aggregation without first constructing these trends. We define the GRETA graph to compactly encode all trends. Our GRETA runtime incrementally maintains the graph, while dynamically propagating aggregates along its edges. Based on the graph, the final aggregate is incrementally updated and instantaneously returned at the end of each query window. Our GRETA runtime represents a win-win solution, reducing both the time complexity from exponential to quadratic and the space complexity from exponential to linear in the number of events. Our experiments demonstrate that GRETA achieves up to four orders of magnitude speed-up and up to 50–fold memory reduction compared to the state-of-the-art two-step approaches. 

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2. Muse: Multi-query Event Trend Aggregation

   https://doi.org/10.1145/3340531.3412138  

   Rozet, Allison; Poppe, Olga; Lei, Chuan; Rundensteiner, Elke A. (October 2020, Proceedings of the 29th ACM International Conference on Information & Knowledge Management)

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   ABSTRACT Streaming analytics deploy Kleene pattern queries to detect and aggregate event trends on high-rate data streams. Despite increasing workloads, most state-of-the-art systems process each query independently, thus missing cost-saving sharing opportunities. Sharing event trend aggregation poses several technical challenges. First, Kleene patterns are in general difficult to share due to complex nesting and arbitrarily long matches. Second, not all sharing opportunities are beneficial because sharing Kleene patterns incurs non-trivial overhead to ensure the correctness of final aggregation results. We propose Muse (Multi-query Shared Event trend aggregation), the first framework that shares aggregation queries with Kleene patterns while avoiding expensive trend construction. To find the beneficial sharing plan, the Muse optimizer effectively selects robust sharing candidates from the exponentially large search space. Our experiments demonstrate that Muse increases throughput by 4 orders of magnitude compared to state-of-the-art approaches. ACM Reference Format: Allison Rozet, Olga Poppe, Chuan Lei, and Elke A. Rundensteiner. 2020. MUSE: Multi-query Event Trend Aggregation. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20), October 19–23, 2020, Virtual Event, Ireland. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3340531.3412138 

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3. To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams

   https://doi.org/10.1145/3448016.3452785  

   Poppe, Olga; Lei, Chuan; Ma, Lei; Rozet, Allison; Rundensteiner, Elke A. (June 2021, Proceedings of the 2021 International Conference on Management of Data (SIGMOD’21), June 18–27, 2021, Virtual Event, China.)

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   Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. State-of-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided by statically selected and hence rigid sharing plans that are often sub-optimal under stream fluctuations. In this work, we propose a novel framework Hamlet that is the first to overcome these limitations. Hamlet introduces two key innovations. First, Hamlet adaptively decides at run time whether to share or not to share computations depending on the current stream properties to harvest the maximum sharing benefit. Second, Hamlet is equipped with a highly efficient shared trend aggregation strategy that avoids trend construction. Our experimental study on both real and synthetic data sets demonstrates that Hamlet consistently reduces query latency by up to five orders of magnitude compared to state-of-the-art approaches. 

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4. The Art of Prompting: Event Detection based on Type Specific Prompts

   https://doi.org/10.18653/v1/2023.acl-short.111  

   Wang, Sijia; Yu, Mo; Huang, Lifu (August 2023, Association for Computational Linguistics)

   We compare various forms of prompts to represent event types and develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection. The experimental results demonstrate that a well-defined and comprehensive event type prompt can significantly improve event detection performance, especially when the annotated data is scarce (few-shot event detection) or not available (zero-shot event detection). By leveraging the semantics of event types, our unified framework shows up to 22.2% F-score gain over the previous state-of-the-art baselines. 

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5. Event Neural Networks

   Dutson, Matthew; Li, Yin; Gupta, Mohit (August 2022, Springer (European Conference on Computer Vision - ECCV))

   Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this redundancy to achieve considerable computation savings during video inference. A defining characteristic of EvNets is that each neuron has state variables that provide it with long-term memory, which allows low-cost, high-accuracy inference even in the presence of significant camera motion. We show that it is possible to transform a wide range of neural networks into EvNets without re-training. We demonstrate our method on state-of-the-art architectures for both high- and low-level visual processing, including pose recognition, object detection, optical flow, and image enhancement. We observe roughly an order-ofmagnitude reduction in computational costs compared to conventional networks, with minimal reductions in model accuracy. 

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