Sara Migliorini | Searcharxiv

arXiv2021

Spatial Data Generators

This gem describes a standard method for generating synthetic spatial data that can be used in benchmarking and scalability tests. The goal is to improve the reproducibility and increase the trust in experiments on synthetic data by using standard widely acceptable dataset distributions. In addition, this article describes how to assign a unique identifier to each synthetic dataset that can be shared in papers for reproducibility of results. Finally, this gem provides a supplementary material that gives a reference implementation for all the provided distributions.

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arXiv2018

Cache-based Multi-query Optimization for Data-intensive Scalable Computing Frameworks

In modern large-scale distributed systems, analytics jobs submitted by various users often share similar work, for example scanning and processing the same subset of data. Instead of optimizing jobs independently, which may result in redundant and wasteful processing, multi-query optimization techniques can be employed to save a considerable amount of cluster resources. In this work, we introduce a novel method combining in-memory cache primitives and multi-query optimization, to improve the efficiency of data-intensive, scalable computing frameworks. By careful selection and exploitation of common (sub)expressions, while satisfying memory constraints, our method transforms a batch of queries into a new, more efficient one which avoids unnecessary recomputations. To find feasible and efficient execution plans, our method uses a cost-based optimization formulation akin to the multiple-choice knapsack problem. Extensive experiments on a prototype implementation of our system show significant benefits of worksharing for both TPC-DS workloads and detailed micro-benchmarks.

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