arXiv subjects

Zhekai Jiang

Publications and source records attributed to Zhekai Jiang.

3 recordsLinked to original sources

arXiv2026

Rerootable Hypertree Decompositions

Hypertree decompositions are a cornerstone in the theory of answering conjunctive queries efficiently. However, they are not yet widely adopted in practice. Problems related to, e.g., the uniqueness of decompositions and succinct representations of all decompositions have so far mostly been neglected by the theory literature. In this paper, we present the first in-depth discussion of rerootability in hypertree decompositions---a property which we argue is essential for such problems. Rerootability leads us to projection-freeness, and we have to discuss normal form to recover tractability. Normal form, however, again obstructs rerootability, and for this reason, we define a relaxed notion of normal form which leads to a truly rerootable and tractable class. Experimental evidence suggests that the price we pay in terms of width increase for transitioning to this class of decompositions is moderate in practice.

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arXiv2026

Size Bound-Adorned Datalog

We introduce EDB-bounded datalog, a framework for deriving upper bounds on intermediate result sizes and the asymptotic complexity of recursive queries in datalog. We present an algorithm that, given an arbitrary datalog program, constructs an EDB-bounded datalog program in which every rule is adorned with a (non-recursive) conjunctive query that subsumes the result of the rule, thus acting as an upper bound. From such adornments, we define a notion of width based on (integral or fractional) edge-cover widths. Through the adornments and the width measure, we obtain, for every IDB predicate, worst-case upper bounds on their sizes, which are polynomial in the input data size, given a fixed program structure. Furthermore, with these size bounds, we also derive fixed-parameter tractable, output-sensitive asymptotic complexity bounds for evaluating the entire program. Additionally, by adapting our framework, we obtain a semi-decision procedure for datalog boundedness that efficiently rewrites most practical bounded programs into non-recursive equivalent programs.

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arXiv2026

Succinct Structure Representations for Efficient Query Optimization

Structural decomposition methods offer powerful theoretical guarantees for join evaluation, yet they are rarely used in real-world query optimizers. A major reason is the difficulty of combining cost-based plan search and structure-based evaluation. In this work, we bridge this gap by introducing meta-decompositions for acyclic queries, a novel representation that succinctly represents all possible join trees and enables their efficient enumeration. Meta-decompositions can be constructed in polynomial time and have sizes linear in the query size. We design an efficient polynomial-time cost-based optimizer based directly on the meta-decomposition, without the need to explicitly enumerate all possible join trees. We characterize plans found by this approach using a novel notion of width, which effectively implies the theoretical worst-case asymptotic bounds of intermediate result sizes and running time of any query plan. Experimental results demonstrate that, in practice, the plans in our class are consistently comparable to---even in many cases better than---the optimal ones found by the state-of-the-art dynamic programming approach, especially on large and complex queries, while our planning process runs by orders of magnitude faster, comparable to the time taken by common heuristic methods.

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