43 citations · 93 across the 8 of their papers we have counts for
7 papers · 1 filter
MOStream: A Modular and Self-Optimizing Data Stream Clustering Algorithm
Zhengru Wang, Xin Wang, Shuhao Zhang
Data stream clustering is a critical operation in various real-world applications, ranging from the Internet of Things (IoT) to social media and financial systems. Existing data st…
MorphStream: Scalable Processing of Transactions over Streams on Multicores
Yancan Mao, Jianjun Zhao, Zhonghao Yang +3
Transactional Stream Processing Engines (TSPEs) form the backbone of modern stream applications handling shared mutable states. Yet, the full potential of these systems, specifical…
Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]
Shuhao Zhang, Xianzhi Zeng, Yuhao Wu +1
Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured…
CStream: Parallel Data Stream Compression on Multicore Edge Devices
Xianzhi Zeng, Shuhao Zhang
In the burgeoning realm of Internet of Things (IoT) applications on edge devices, data stream compression has become increasingly pertinent. The integration of added compression ov…
A Survey on Transactional Stream Processing
Shuhao Zhang, Juan Soto, Volker Markl
Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, nume…
BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures
Shuhao Zhang, Jiong He, Amelie Chi Zhou +1
We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution…
Towards Concurrent Stateful Stream Processing on Multicore Processors (Technical Report)
Shuhao Zhang, Yingjun Wu, Feng Zhang +1
Recent data stream processing systems (DSPSs) can achieve excellent performance when processing large volumes of data under tight latency constraints. However, they sacrifice suppo…