Related papers: On the Semantic Overlap of Operators in Stream Pro…
Transactional Stream Processing Engines (TSPEs) form the backbone of modern stream applications handling shared mutable states. Yet, the full potential of these systems, specifically in exploiting parallelism and implementing dynamic…
Dataflow devices represent an avenue towards saving the control and data movement overhead of Load-Store Architectures. Various dataflow accelerators have been proposed, but how to efficiently schedule applications on such devices remains…
Swim extends the actor model to support applications composed of linked distributed actors that continuously analyze boundless streams of events from millions of sources, to respond in-sync with the real-world. Swim builds a running…
Data streams are a sequence of data flowing between source and destination processes. Streaming is widely used for signal, image and video processing for its efficiency in pipelining and effectiveness in reducing demand for memory. The goal…
Despite many advances in query optimization, indexing techniques, and data storage, modern data platforms still face difficulties in delivering robust query performance under high concurrency and computationally intensive queries. This…
Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve real-time data analytics, recent researches keep focusing on optimizing the system latency and…
Today most applications continuously produce information under the form of streams, due to the advent of the means of collecting data. Sensors and social networks collect an immense variety and volume of data, from different real-life…
Distributed Stream Processing Systems (DSPSs) are among the currently most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. The…
Streaming computing enables the real-time processing of large volumes of data and offers significant advantages for various applications, including real-time recommendations, anomaly detection, and monitoring. The multi-way stream join…
Context: The combination of distributed stream processing with microservice architectures is an emerging pattern for building data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka…
With the rapid growth in the number of devices of the Internet of Things (IoT), the volume and types of stream data are rapidly increasing in the real world. Unfortunately, the stream data has the characteristics of infinite and periodic…
Many problems in Computer Science can be framed as the computation of queries over sequences, or "streams" of data units called events. The field of Complex Event Processing (CEP) relates to the techniques and tools developed to efficiently…
This paper addresses online query processing for large-scale, incremental data analysis on a distributed stream processing engine (DSPE). Our goal is to convert any SQL-like query to an incremental DSPE program automatically. In contrast to…
Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire…
The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more…
We consider two classes of stream-based computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. The dataflow architecture is a natural platform for programming with streams.…
This paper presents a stream-oriented architecture for structuring cluster applications. Clusters that run applications based on this architecture can scale to tenths of thousands of nodes with significantly less performance loss or…
Real-time Big Data architectures evolved into specialized layers for handling data streams' ingestion, storage, and processing over the past decade. Layered streaming architectures integrate pull-based read and push-based write RPC…
Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale stream processing applications. Given an application, SN…
Streaming computations on massive data sets are an attractive candidate for parallelization, particularly when they exhibit independence (and hence data parallelism) between items in the stream. However, some streaming computations are…