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3rd Edition of Data Ecosystems Workshop at VLDB 2024

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We are very happy to organize this year again the International Workshop on Data Ecosystems in conjunction with the VLDB’24 in Gangzhou, China. We are very happy to win Prof. Stefan Decker from RWTH Aachen University to give the keynote!

Paper published at BiDEDE workshop at SIGMOD

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We are very delighted, that our paper “GALOIS: A Hybrid and Platform-Agnostic Stream Processing Architecture” has been accepted for publication in the Big Data in Emergent Distributed Environments (BiDEDE 2023) workshop at the SIGMOD conference 2023! We are very excited to present our work in Seattle, USA. We provide a preprint of the paper at the arxiv repository.

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Seminar Data Ecosystems

Seminar, RWTH Aachen University, 2023

Organizations in many domains, such as manufacturing or healthcare, have a huge demand to exchange data to enable new services, drive research and innovation, or improve patient care. Hence, organizations require alliance-driven infrastructures capable of supporting controlled data exchange across diverse stakeholders and transparent data management. Data Ecosystems are distributed, open, and adaptive information systems with the characteristics of being self-organizing, scalable, and sustainable trying to fulfil these requirements. But there are many open issues, which make the exchange on a technological, processual, and organizational level a challenge. In this seminar, students will identify and discuss the main challenges in data ecosystems, such as data quality, data transparency, and data integration.

Implementation of Databases (IDB)

Master course, RWTH Aachen University, 2023

The lecture gives an introduction to the implementation of database systems. Besides the rough architecture of a DB system, detailed methods for solving individual DB tasks, such as query processing and transaction management, are presented. The concepts of implementation are demonstrated using classical relational DB systems as well as distributed and NoSQL systems. Concepts, frameworks and components of Big Data architectures, e.g. MapReduce, Apache Spark and are introduced and practically tested.

Data Ecosystems Lab

Seminar, RWTH Aachen University, 2023

In the modern Internet, personal data is often kept by the application provider using this data. This is true for various services (social media, health tracking, personal notes). The disadvantage from a user perspective is a lack of control and access. The external provider decides how the data is used and accessed, resulting in data silos. The Solid specification enables individuals to control the storage of their personal data, by decoupling applications from the personal data they use. In the Solid data ecosystem, every user keeps their own data in a personal store called a Pod and can share the data with specific applications or individuals of their choice.

Seminar Data Stream Management and Analysis

Seminar, RWTH Aachen University, 2024

Low-cost sensors and high communication bandwidths open up new possibilities for applications that benefit from a high amount of data. Such applications produce data continuously, potentially unbounded, and at high rates, which is subsumed under the term data stream. Examples for applications fields are smart manufacturing, high-speed trading, fraud detection, robotics, or social networks. Data stream management systems are special systems which address the specific requirements handling data streams. In this seminar we will research recent topics in data stream management and analysis, such as data compression, online learning, or operator distribution. The seminar is offered as a block seminar.

Data Stream Management and Analysis

Master course, RWTH Aachen University, 2024

In many fields today data is produced continuously, potentially unbounded, and at high rates, which is termed as data stream. Applications in smart manufacturing, aerospace, particle physics, or stock exchange trading have a high demand to handle and analyze the massive data streams created. Due to their challenging characteristics specific technologies and methods for data management and analysis have been developed. In this course, the students will get a deep understanding of these principles and techniques, such as query processing and optimization or data stream mining, especially covering: