Stream Processing (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …

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 …

Carefully balancing load in distributed stream processing systems has a fundamental impact
on execution latency and throughput. Load balancing is challenging because real-world …

Distributed stream processing frameworks are designed to perform continuous computation
on possibly unbounded data streams whose rates can change over time. Devising solutions …

Key-based workload partitioning is a common strategy used in parallel stream processing
engines, enabling effective key-value tuple distribution over worker threads in a logical …

Nowadays, information generated by the Internet interactions is growing exponentially,
creating massive and continuous flows of events from the most diverse sources. These …

Abstract Stream Processing Systems (SPSs) dynamically process input events. Since the
input is usually not a constant flow, presenting rate fluctuations, many works in the literature …

This paper introduces QFrag, a distributed system for graph search on top of bulk
synchronous processing (BSP) systems such as MapReduce and Spark. Searching for …

Nowadays, more and more sources (connected devices, social networks, etc.) emit real-time
data with fluctuating rates over time. Existing distributed stream processing engines (SPE) …

To sustain the input rate of high-throughput streams, modern stream processing systems rely
on parallel execution. However, skewed data yield imbalanced load assignments and create …