Publications by Ioannis Mytilinis

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To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...

Analytical applications, such as exploratory data analysis and decision support, process complex workloads that include sequences of inter-dependent queries. While modern OLAP systems exploit data parallelism, dependencies force execution ordering constrai ...

Data lakes are complex ecosystems where heterogeneity prevails. Raw data of diverse formats are stored and processed, while long and expensive ETL processes are avoided. Apart from data heterogeneity, data lakes also entail hardware heterogeneity. Typical ...

Modern big data systems process not only a huge volume of data, but also numerous concurrent queries. These queries usually span over distributed data and need to be processed within a strict deadline (e.g.: report generation, SLA, etc.). While users expec ...

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are acce ...

Ioannis Mytilinis

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Dalton: Learned Partitioning for Distributed Data Streams

Accelerating Complex Analytics using Speculation

Storage Management in Smart Data Lake

Optimizing Goodput through Sharing for Batch Analytics with Deadlines

Clouseau: Blockchain-based Data Integrity for HDFS Clusters