Searched for: subject%3A%22Stream%255C+processing%22
(1 - 18 of 18)
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Veneti, Theodoros (author)
Stream Processing Engines (SPEs) are called upon to help solve problems around big and volatile data, while satisfying the needs for near real-time processing. In order for such systems to be considered effective solutions to such problems at scale, efficient elasticity and non dataflow-disturbing reconfiguration operations within are a...
master thesis 2023
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Hernandez Quintanilla, Tomás (author)
Similarity joins are operations which involve identifying similar pairs of records within one or multiple datasets. These operations are typically time-sensitive, as timely identification of relations can lead to increased profitability. Therefore, it is advantageous to analyze them using a stream processing system, which offers real-time...
master thesis 2023
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Kanis, Job (author)
The introduction of cloud hosting has made it possible to elastically provision distributed stream processing systems (SPEs). By dynamically scaling the different operators of the system, resource consumption can be minimised while meeting the system service-level objectives. In the literature, many different auto-scaling techniques are proposed...
master thesis 2023
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Fragkoulis, M. (author), Carbone, Paris (author), Kalavri, Vasiliki (author), Katsifodimos, A (author)
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 numerous worldwide open-source communities. This survey provides a comprehensive overview of fundamental aspects of stream processing systems and their evolution in...
journal article 2023
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Siachamis, G. (author), Kanis, Job (author), Koper, Wybe (author), Psarakis, K. (author), Fragkoulis, M. (author), van Deursen, A. (author), Katsifodimos, A (author)
In this work, we evaluate autoscaling solutions for stream processing engines. Although autoscaling has become a mainstream subject of research in the last decade, the database research community has yet to evaluate different autoscaling techniques under a proper benchmarking setting and evaluation framework. As a result, every newly proposed...
conference paper 2023
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Nadeem, A. (author), Verwer, S.E. (author)
Sequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medoids (PAM) is commonly used to cluster sequences since it supports alignment-based distances, and the k-centers being actual data items helps with cluster...
conference paper 2023
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Koper,ook geschreven Jansen, Wybe (author)
As the world continues to embrace cloud computing, more applications are being scaled elastically. Elastic scaling allows applications to add or remove computing resources based on the load experienced by the application. When the load is high more resources are provisioned enabling the application to keep up with the load. When the load is low...
master thesis 2022
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Kanya Paramita Koesoemo, Kanya (author)
The development of data stream processing has become one of the key themes in the database and distributed system community throughout the world as data has grown on a large scale and in a range of industries over the last several years. Because data stream processing is a relatively new breakthrough in data-driven approaches, several teams at...
master thesis 2021
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Zorgdrager, Wouter (author)
The cloud is widely adopted as a flexible and on-demand computing infrastructure. In recent years, a new and promising cloud paradigm emerged: serverless computing. Serverless computing promises a pay-as-you-go model and offers features such as autoscaling and high availability. Nevertheless, developing scalable cloud applications remains a...
master thesis 2021
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Ploemen, Marlo (author)
In recent years, the interest for serverless computing has grown tremendously. The most common form of serverless computing, Function-as-a-Service (FaaS), uses data centers of large public cloud providers to run simple functions. The cloud providers are responsible for the operational and deployment aspects. Non-trivial function implementations...
master thesis 2021
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Verheijde, Jim (author)
At the moment we are witnessing the maturation of distributed streaming dataflow systems whose use-cases have departed from the mere analysis of streaming windows and complex-event processing, as they now extend to cloud applications, workflows and even e-commerce. The state of streaming operators has been so far hidden from external...
master thesis 2021
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Zwart, Marc (author)
Major advances in the fault tolerance of distributed stream processing systems provided the systems with the capacity to produce strictly consistent results under failures. Consistent fault tolerance has been one of the catalysts fueling the maturity of streaming systems and boosting their widespread adoption not only for analytics use cases,...
master thesis 2021
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Fortunato Silvestre, P.M. (author), Fragkoulis, M. (author), Spinellis, D. (author), Katsifodimos, A (author)
Stream processing lies in the backbone of modern businesses, being employed for mission critical applications such as real-time fraud detection, car-trip fare calculations, traffic management, and stock trading. Large-scale applications are executed by scale-out stream processing systems on thousands of long-lived operators, which are subject...
conference paper 2021
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Smith, Thomas (author)
Stream processing has taken a prominent position in the software engineering industry. Many applications, from a small to large scale, embrace this paradigm to deal with the difficulties of responding to events that happen asynchronously: programmers define "operators" that perform small, independent tasks on individual events. These operators...
master thesis 2020
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Grulich, Philipp M. (author), Traub, Jonas (author), Bress, Sebastian (author), Katsifodimos, A (author), Markl, Volker (author), Rabl, Tilmann (author)
Evaluating modern stream processing systems in a reproducible manner requires data streams with different data distributions, data rates, and real-world characteristics such as delayed and out-of-order tuples. In this paper, we present an open source stream generator which generates reproducible and deterministic out-of-order streams based on...
conference paper 2019
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Traub, Jonas (author), Grulich, Philipp Marian (author), Rodriguez Cuellar, Alejandro (author), Bress, Sebastian (author), Katsifodimos, A (author), Rabl, Tilmann (author), Markl, Volker (author)
Computing aggregates over windows is at the core of virtually every stream processing job. Typical stream processing applications involve overlapping windows and, therefore, cause redundant computations. Several techniques prevent this redundancy by sharing partial aggregates among windows. However, these techniques do not support out-of...
conference paper 2018
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Kuijpers, Jos (author), Quist, Joris (author), Zorgdrager, Wouter (author)
CodeFeedr is a research project at the software engineering division of the Delft University of Technology in collaboration with the Software Improvement Group. The research focuses on a software infrastructure which serves software practitioners in utilizing data-driven decision making. Currently, frameworks like Apache Flink are capable of...
bachelor thesis 2018
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Willems, L. (author)
The amount of collected data increases exponentially and this exponential growth comes with a demand to analyze big volumes of data and to process the data fast and in real time. Business analysts have the expertise in the domain of the desired stream processing applications, but often lack the technical skills to create these kind of...
master thesis 2016
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