Stream Processing (SP) engines play a crucial role in realtime analysis within the Big Data landscape, handling infinite data streams to analyze massive, noisy, and heterogeneous information flows.While initially inheriting programming interfaces from Hadoop MapReduce, a recent trend involves adopting declarative languages for expressing analyses.The Event Processing Language (EPL) and its implementation Esper, a mature query language in streaming and event processing, have gained prominence. EPL, with SQL-like syntax, uniquely combines Complex Event Processing (CEP) and streaming analytics.However, it lacks formal semantics.This work addresses this gap by formalizing a core fragment of EPL, focusing on the aspects of Data Definition Language (DDL) and Data Manipulation Language (DML).The formalization resolves semantic ambiguities, identifies potentially harmful constructs, and specifies EPL's data and processing model.This effort addresses a major gap in the formalization of stream processing languages, aligning with recent initiatives from similar domains like graph query languages.

Continuous Queries (CQs) are designed to operate over infinite data streams; the paradigm gained prominence with the rise of Stream Processing (SPs). Central to CQs are window operators as they enforce bounded computation by partitioning streams into finite subsets. Although several window operators exist —e.g., slide-by-tuple, session-window, and frames—commercial systems largely adopt a few due to implementation complexity, theoretical opacity, and input-dependent non-determinism. This demonstration shows Play2Win, an interactive playground that empowers users to explore and compare various windowing strategies under a unified system semantics. Our platform offers three key contributions: (I) a real-time environment for experimenting with different window operators; (II) a graph-based representation of the window state that eases direct comparison; and (III) a compositional framework for rapid prototyping of novel windowing mechanisms. The demonstration explore multiple datasets across different scenarios, fostering a deeper understanding of window operators for querying streams.

Continuous Queries (CQs) run indefinitely, processing infinite data streams and producing continuous outputs. They commonly use window functions to segment streams intofi nite chunks for computation. Ensuring data integrity in CQs is challenging, involving, for example, streaming joins for binary constraints. Current methods, like dropping or repairing inconsistent data, can harm throughput and increase latency. This paper proposes a novel approach using provenance-based techniques to map violations in input streams to CQ results with minimal overhead. This ensures continuous dataflow and maintains the analytical integrity of CQs. Our study explores the feasibility and efficiency of this method, addressing a significant gap in applying provenance techniques to streaming data. While provenance-based techniques have proven effective for static data, their application in streaming contexts remains unexplored. Our solution addresses this gap, achieving a stable throughput across increasingly demanding memory loads wrt to the baselines, spacing between a 10% increase for medium-sized buffers (i.e., the windows), up to 80% for heavier loads. Moreover, results show the minimal impact of annotation (up to 25%) in the total execution runtime, demonstrating the effectiveness of our graph-based approach.

Stream processing is designed to query unbounded and timely-ordered data flows in real-time while guaranteeing low latency and high throughput.

Streaming Analytics (SA) and Complex Event Recognition (CER) are of paramount importance in searching for an ultimate Big Data solution that can simultaneously address Data Velocity, Variety, and Volume. Indeed, the growing popularity of streaming data has pushed the boundaries of existing data systems, fostering the rise of Stream Processing Engines (SPE). However, Data Velocity never appears isolated. Streams are huge, heterogeneous, and noisy as they come from multiple sources. Horizontally-scalable SPEs like Flink and KSQL-DB allow continuous stream analytics using SQL-like languages. On the other hand, CER engines like OracleCEP and DroolFusion use regular languages for (parallel) pattern detection over heterogeneous streams. This paper takes a first step towards a unifying solution. To this extent, we present KELPr, an in-memory distributed CER engine designed extending the Dual Streaming Model and implemented on top of Kafka Streams.