K. Psarakis
12 records found
1
Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors to handle failures while ensuring exactly-once processing guarantees. At the moment of writing, virtu
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Although the cloud has reached a state of robustness, the burden of using its resources falls on the shoulders of programmers who struggle to keep up with ever-growing cloud infrastructure services and abstractions. As a result, state management, scaling, operation, and failure m
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While the concept of large-scale stream processing is very popular nowadays, efficient dynamic allocation of resources is still an open issue in the area. The database research community has yet to evaluate different autoscaling techniques for stream processing engines under a ro
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The increasing need for data trading across businesses nowadays has created a demand for data marketplaces. However, despite the intentions of both data providers and consumers, today’s data marketplaces remain mere data catalogs. We believe that marketplaces of the future requir
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The increasing need for data trading has created a high demand for data marketplaces. These marketplaces require a set of valueadded services, such as advanced search and discovery, that have been proposed in the database research community for years, but are yet to be put to pra
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While there are multiple approaches for distributed application programming (e.g., Bloom [2], Hilda [14], Cloudburst [12], AWS Lambda, Azure Durable Functions, and Orleans [3, 4]), in practice developers mainly use libraries of popular general purpose languages such as Spring Boo
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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 set
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How can we perform similarity joins of multi-dimensional streams in a distributed fashion, achieving low latency? Can we adaptively repartition those streams in order to retain high performance under concept drifts? Current approaches to similarity joins are either restricted to
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Serverless computing is currently the fastest-growing cloud services segment. The most prominent serverless offering is Function-as-a-Service (FaaS), where users write functions and the cloud automates deployment, maintenance, and scalability. Although FaaS is a good fit for exec
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Serverless computing is currently the fastest-growing cloud services segment. The most prominent serverless offering is Function-as-a-Service (FaaS), where users write functions and the cloud automates deployment, maintenance, and scalability. Although FaaS is a good fit for exec
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Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of schema matching: the process of establishing correspondences between datasets. Traditionally, schema
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Valentine in Action
Matching Tabular Data at Scale
Capturing relationships among heterogeneous datasets in large data lakes - traditionally termed schema matching - is one of the most challenging problems that corporations and institutions face nowadays. Discovering and integrating datasets heavily relies on the effectiveness of
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