Columnar databases start to leverage indexes to enhance bottleneck queries efficiency. To decide if an index may be useful or not in row-oriented systems, a commonly used tool is a “What-If” API. It returns an estimation of the benefit of an index without constructing it, saving time and resources at the cost of possible mistakes in the prediction. Moreover, to have a complete auto-tuning database, recent researches leverage Machine Learning to have an automatic database administrator that tunes the database configuration using workload knowledge and “What-If” calls. As column-oriented databases are just starting to leverage indexes, there is yet no such tool specifically designed for these systems. In this work, we propose three contributions: heuristics for segment-aware access paths costs estimations in column-oriented databases, a hypothetical index benefit estimation designed for column-oriented databases (limited to hash indexes), and the use of quantile regression to trade-off precision and risk.

This work studies the enumeration of answers to conjunctive queries. Research on this topic typically focuses on conjunctive queries without self-joins. We want to work towards a dichotomy that allows us to know if a conjunctive query with self-joins can be enumerated with optimal time guarantees: linear time before the first answer and constant delay between answers. In this work, we have found a sufficient and necessary conditions. However, we have examples that we can not classify and can lead to new work on the subject.

Some CQs are in DelayCLin: we can enumerate all their answers with constant delay between answers after linear preprocessing. We have a dichotomy for classifying which CQs without self-joins are in DelayCLin, but this dichotomy is not correct for CQs with self-joins. As CQs often have self-joins, our goal with this internship is to find a sufficient condition and a necessary condition for acyclic CQs with self-joins.

Columnar databases start to leverage indexes to enhance bottleneck queries efficiency. To decide if an index may be useful or not in row-oriented systems, a commonly used tool is a “What-If” API. It returns an estimation of the benefit of an index without constructing it, saving time and resources at the cost of possible mistakes in the prediction. Moreover, to have a complete auto-tuning database, recent researches leverage Machine Learning to have an automatic database administrator that tunes the database configuration using workload knowledge and “What-If” calls. As column-oriented databases are just starting to leverage indexes, there is yet no such tool specifically designed for these systems. In this work, we propose three contributions: heuristics for segment-aware access paths costs estimations in column-oriented databases, a hypothetical index benefit estimation designed for column-oriented databases (limited to hash indexes), and the use of quantile regression to trade-off precision and risk.

This work studies the enumeration of answers to conjunctive queries. Research on this topic typically focuses on conjunctive queries without self-joins. We want to work towards a dichotomy that allows us to know if a conjunctive query with self-joins can be enumerated with optimal time guarantees: linear time before the first answer and constant delay between answers. In this work, we have found a sufficient and necessary conditions. However, we have examples that we can not classify and can lead to new work on the subject.