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

Stream data are often dirty, for example, owing to unreliable sensor reading, or erroneous
extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter …

Scalable execution of expensive continuous queries over massive data streams requires
input streams to be split into parallel sub-streams. The query operators are continuously …

Most scientific databases consist of datasets (or sources) which in turn include samples (or
files) with an identical structure (or schema). In many cases, samples are associated with …

Many stream processing applications require access to a multitude of streaming as well as
stored data sources. Yet there is no clear semantics for correct continuous query execution …

Stream data are often dirty, for example, owing to unreliable sensor reading or erroneous
extraction of stock prices. Most stream data cleaning approaches employ a smoothing filter …

The concept of a data lake is less than 10 years old, but they are already hugely
implemented within large companies. Their goal is to efficiently deal with ever-growing …

Because of the processing of continuous unstructured large streams of data, mining real-
time streaming data is a more challenging research issue than mining static data. The …

Data stream management systems (DSMS) processing long-running queries over large
volumes of stream data must typically deliver time-critical responses. We propose the first …

This tutorial provides an overview of current evaluation techniques for schema matching and
mapping tasks and tools, alongside existing and broadly used evaluation scenarios. The …