Deep Reinforcement Learning for Join Order Enumeration

Join order selection plays a significant role in query performance. However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan. Hence, optimizers often repeatedly choose the same bad plan, as they do not have a mechanism for "learning from their mistakes". In this paper, we argue that existing deep reinforcement learning techniques can be applied to address this challenge. These techniques, powered by artificial neural networks, can automatically improve decision making by incorporating feedback from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator, and present preliminary results indicating that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan quality and join enumeration efficiency

QuickSel: Quick Selectivity Learning with Mixture Models

Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice---i.e., require an exponential number of buckets---or quickly lose their advantage as they observe more queries. In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, QuickSel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is 34.0x-179.4x faster than state-of-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is 26.8%-91.8% more accurate than periodically-updated histograms and samples, respectively

Reaction engineering of benzaldehyde lyase from Pseudomonas fluorescens catalyzing enantioselective C-C-bond formation

The reaction engineering of benzaldehyde lyase (BAL, E.C. 4.1.2.38) from Pseudomonas fluorescens catalyzing the enantioselective carboligation of benzaldehyde and acetaldehyde yielding (R)-2-hydroxy-1-phenylpropanone (HPP) is presented. Based on kinetic studies a continuous process is developed. The developed bioreactor allows focusing the complex reaction system on the production of HPP with simultaneous discrimination of the undesired benzoin formation. The application of a continuous process in combination with membrane technology enables high space time yields (1120 g L-1 d(-1), ee > 99%) of the product as well as high total turnover numbers of the biocatalyst (mol of product/mol of biocatalyst = 188.000). A kinetic model was developed to simulate the continuously operated reactor and to determine optimal production conditions. The synthesis of (R)-(3-chlorophenyl)-2-hydroxy-1-propanone (1214 g L-1 d(-1), ee = 99%) in the bioreactor demonstrates a broad applicability of the presented reactor concept for the production HPP derivatives

Preparative Enantioselective Synthesis of Benzoins and (R)-2-Hydroxy-1-phenyl-propanone Using Benzaldehyde Lyase

Dominguez de Maria P, Stillger T, Pohl M, et al. Preparative Enantioselective Synthesis of Benzoins and (R)-2-Hydroxy-1-phenyl-propanone Using Benzaldehyde Lyase. J. Mol. Catal. B: Enzym. 2006;38(1):43-47

CubeLoad: a Parametric Generator of Realistic OLAP Workloads

Differently from OLTP workloads, OLAP workloads are hardly predictable due to their inherently extemporary nature. Besides, obtaining real OLAP workloads by monitoring the queries actually issued in companies and organizations is quite hard. On the other hand, hardware and software benchmarking in the industrial world, as well as comparative evaluation of novel approaches in the research community, both need reference databases and workloads. In this paper we present CubeLoad, a parametric generator of workloads in the form of OLAP sessions, based on a realistic profile-based model. After describing the main features of CubeLoad, we discuss the results of some tests that show how workloads with very different features can be generated