Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

An increasingly important system requirement for distributed stream processing applications
is to provide strong correctness guarantees under unexpected failures and out-of-order data …

In this paper, we present the first comprehensive survey of window types for stream
processing systems which have been presented in research and commercial systems. We …

Modern Stream Processing Engines (SPEs) process large data volumes under tight latency
constraints. Many SPEs execute processing pipelines using message passing on shared …

The Internet of Things (IoT) presents a novel computing architecture for data management: a
distributed, highly dynamic, and heterogeneous environment of massive scale. Applications …

Stream Processing Engines (SPEs) execute long-running queries on unbounded data
streams. They follow an interpretation-based processing model and do not perform runtime …

We present TVA, a multi-party computation (MPC) system for secure analytics on secret-
shared time series data. TVA achieves strong security guarantees in the semi-honest and …

A recent trend in stream processing is offloading the computation of decomposable
aggregation functions (DAF) from cloud nodes to geo-distributed fog/edge devices to …

Many companies and businesses train and deploy machine learning (ML) pipelines to
answer prediction queries. In many applications, new training data continuously becomes …

The conventional approach of evaluating massive data is inappropriate for real-time
analysis; therefore, analyzing big data in a data stream remains a critical issue for numerous …