This paper presents RisingWave, a new cloud-native streaming database under development. RisingWave's mission is to democratize stream processing: to make stream processing simple, affordable, and accessible. To achieve that, RisingWave treats streams ...

Most state-of-the-art SPEs use punctuations to divide a stream into bounded substreams of messages, such as epochs and windows. The punctuation approach is powerful but has limitations: it does not support cyclic dataflows, is poorly scalable in some ...

Making timely-decisions amid the massive influx of financial data is one of the essential features of stock market analytics. Many stock market analytics should provide functionalities that compute multiple technical indicators simultaneously and detect ...

In this paper, we present a solution to the DEBS 2022 Grand Challenge (GC). According to the GC requirements, the proposed software continuously observes notifications about financial instruments being traded, aiming to timely detect breakout patterns. ...

The DEBS 2022 Grand Challenge (GC) focuses on real-time complex event processing of real-world high-volume tick data. The goal of the challenge is to efficiently compute specific trend indicators and detect patterns resembling those used by real-life ...

The DEBS Grand Challenge (GC) is an annual programming competition open to practitioners from both academia and industry. The GC 2022 edition focuses on real-time complex event processing of high-volume tick data provided by Infront Financial Technology ...

We present a knowledge graph management system designed to run on Edge computing devices that handles high-frequency data streams. During the design phase, we took into account the inherent limitations of the devices, i.e., limited computing power and ...

Sharding is a promising approach to high-performance blockchains and has been extensively explored in academia recently. However, sharding also introduces cross-shard transactions, which require expensive inter-shard coordination to ensure state ...

This paper proposes a learned cost estimation model for Distributed Stream Processing Systems (DSPS) with an aim to provide accurate cost predictions of executing queries. A major premise of this work is that the proposed learned model can generalize to ...