A Survey on String Constraint Solving

String constraint solving refers to solving combinatorial problems involving constraints over string variables. String solving approaches have become popular over the past few years given the massive use of strings in different application domains like formal analysis, automated testing, database query processing, and cybersecurity. This article reports a comprehensive survey on string constraint solving by exploring the large number of approaches that have been proposed over the past few decades to solve string constraints.

Aggregate-based Training Phase for ML-based Cardinality Estimation

Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches may deliver more accurate cardinality estimations than traditional approaches. However, a lot of training queries have to be executed during the model training phase to learn a data-dependent ML model making it very time-consuming. Many of those training or example queries use the same base data, have the same query structure, and only differ in their selective predicates. To speed up the model training phase, our core idea is to determine a predicate-independent pre-aggregation of the base data and to execute the example queries over this pre-aggregated data. Based on this idea, we present a specific aggregate-based training phase for ML-based cardinality estimation approaches in this paper. As we are going to show with different workloads in our evaluation, we are able to achieve an average speedup of 90 with our aggregate-based training phase and thus outperform indexes.

Technical Perspective

Hash tables are possibly the single most researched element of the database query processing layers. There are many good reasons for that. They are critical for some key operations like joins and aggregation, and as such are one of the largest contributors to the overall query performance. Their efficiency is heavily impacted by variations of workloads, hardware and implementation, leading to many research opportunities. At the same time, they are sufficiently small and local in scope, allowing a starting researcher, or even a student, to understand them and contribute novel ideas. And benchmark them. . . Oh, the benchmarks. . . :)

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

FPGAs are promising target architectures for hardware acceleration of database query processing, as they combine the performance of hardware with the programmability of software. In particular, they are partially reconfigurable at runtime, which allows for the runtime adaptation to a variety of queries. However, reconfiguration costs some time, and a region of the FPGA is not available for computations during its reconfiguration. Techniques to avoid or at least hide the reconfiguration latencies can improve the overall performance. This paper presents optimizations based on query look-ahead, which follows the idea of exploiting knowledge about subsequent queries for scheduling the reconfigurations such that their overhead is minimized. We evaluate our optimizations with a calibrated model for various parameter settings. Improvements in execution time can be “calculated” even if only being able to look one query ahead.