PANDA：亚模宽度下的查询求值

Constant delay enumeration with FPT-preprocessing for conjunctive queries of bounded submodular width

Marx (STOC~2010, J.~ACM 2013) introduced the notion of submodular width of a conjunctive query (CQ) and showed that for any class $Φ$ of Boolean CQs of bounded submodular width, the model-checking problem for $Φ$ on the class of all finite structures is fixed-parameter tractable (FPT). Note that for non-Boolean queries, the size of the query result may be far too large to be computed entirely within FPT time. We investigate the free-connex variant of submodular width and generalise Marx's result to non-Boolean queries as follows: For every class $Φ$ of CQs of bounded free-connex submodular wid…

Panda: Partitioned Data Security on Outsourced Sensitive and Non-sensitive Data

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. This paper continues along with the emerging trend in secure data processing that recognizes that the entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome limitations of existing encryption-based approaches. We, first, provide a new security definition, entitled partitioned data security for guaranteeing that the joint processing of non-sensitive data (in cleartext) and sensitive data (in encrypted form) does not lead …

割与标定用于子模函数宽度

Cuts and Gauges for Submodular Width

本文将子模宽度重新表述为几何术语，通过新的分支宽度参数近似子模宽度，连接了超图边分离与子模函数成本，并将其与图论概念如线图树宽和多商品流相关联。

比子模宽度障碍更快地列出偶环

Listing Even Cycles Faster than the Submodular-Width Barrier

针对无向图中的2k-环列出问题，提出一种基于非对称过饱和引理的新算法，将时间复杂度改进为Õ(m^{(2k^2-k+1)/(k^2+1)}+t)，突破了Alon-Yuster-Zwick经典结果，并适用于数据库系统。

Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to support the TPC-H benchmark, and it provides performance that is comparable to, and often better than, that of spec…

Functional Aggregate Queries with Additive Inequalities

Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables. We refer to these queries as FAQ-AI for short. To answer FAQ-AI in the Boolean semiring, we define relaxed tree decompositions and relaxed submodular and fractional hypertree width parameters. We show that an extension of the InsideOut algorithm using Chazelle's geometric data structure for solving the semigroup range search…

Calibration: A Simple Trick for Wide-table Delta Analytics

Data analytics over normalized databases typically requires computing and materializing expensive joins (wide-tables). Factorized query execution models execution as message passing between relations in the join graph and pushes aggregations through joins to reduce intermediate result sizes. Although this accelerates query execution, it only optimizes a single wide-table query. In contrast, wide-table analytics is usually interactive and users want to apply delta to the initial query structure. For instance, users want to slice, dice and drill-down dimensions, update part of the tables and joi…

The Maximum Trajectory Coverage Query in Spatial Databases

With the widespread use of GPS-enabled mobile devices, an unprecedented amount of trajectory data is becoming available from various sources such as Bikely, GPS-wayPoints, and Uber. The rise of innovative transportation services and recent break-throughs in autonomous vehicles will lead to the continued growth of trajectory data and related applications. Supporting these services in emerging platforms will require more efficient query processing in trajectory databases. In this paper, we propose two new coverage queries for trajectory databases: (i) k Maximizing Reverse Range Search on Traject…

基于简单度约束的基数估计与合取查询评估的高效算法

Efficient Algorithms for Cardinality Estimation and Conjunctive Query Evaluation With Simple Degree Constraints

针对简单度约束下的基数估计与合取查询评估，提出了计算polymatroid bound及其证明序列的多项式时间算法，可加速PANDA框架查询，并引入了新的flow bound。

Worst-case Optimal Binary Join Algorithms under General $\ell_p$ Constraints

Worst-case optimal join algorithms have so far been studied in two broad contexts -- $(1)$ when we are given input relation sizes [Atserias et al., FOCS 2008, Ngo et al., PODS 2012, Velduizhen et. al, ICDT 2014] $(2)$ when in addition to size, we are given a degree bound on the relation [Abo Khamis et al., PODS 2017]. To the best of our knowledge, this problem has not been studied beyond these two statistics even for the case when input relations have arity (at most) two. In this paper, we present a worst-case optimal join algorithm when are given $\ell_{p}$-norm size bounds on input relations…

连接查询的因式分解表示：紧界与新二分法

Factorised Representations of Join Queries: Tight Bounds and a New Dichotomy

本文研究连接查询结果的因式分解电路表示，将电路大小下界改进为紧界 $N^{Ω(tw)}$，并给出无界元数情形下 $N^{Ω(subw^{1/4})}$ 的无条件下界。