arXiv subjects

Jaehyun Ha

Publications and source records attributed to Jaehyun Ha.

6 recordsLinked to original sources

arXiv2026

Choose your own adventure: How data source selection shapes urban accessibility analyses

Urban park accessibility shapes mental health, physical well-being, and community cohesion. Measuring it is thus central to urban and landscape planning. Although researchers have debated the impacts of different accessibility measures on analysis results, scant attention has been paid to variability from data source selection. Do the results of accessibility analyses vary depending on the dataset used to define park locations and attributes? We compare four widely used geospatial data sources - OpenStreetMap, Esri, SafeGraph, and The Trust for Public Land - by validating their data and measuring park access across two cities. These datasets purport to cover the same spatial features, but our findings reveal wide discrepancies in accessibility results, driven by differences in dataset completeness, coverage, and accuracy between the sources. We provide guidelines for selecting and carefully validating data for accessibility analyses, as these decisions directly impact the accuracy and equity of downstream urban park planning and decision-making.

stat.AP↗

arXiv2026

The Case for TPU-Accelerated Analytical Query Processing

Large-scale analytical query processing requires computing substrates capable of immense distributed scale-out. Tensor Processing Units (TPUs) perfectly embody this extreme-scale architecture, offering massive computational density and highly scalable optical pod-level interconnects. Despite these advantages, TPUs have so far remained largely unexplored for analytical database workloads. In this paper, we present the first comprehensive case for TPU-accelerated analytical query processing by running all the TPC-H queries entirely on the TPUs. We introduce Native TPU Accelerator (NTA) for analytical query processing, which provides hardware-conscious algorithm design and optimizations in JAX/XLA. By exposing the TPU's structural mechanics, we demonstrate how dynamic, memory-bound database operations can be rewritten into deterministic JAX programs. We engineer a suite of novel execution strategies that leverage both the TPU's TensorCore and SparseCore to trade sparse, unpredictable memory accesses for dense, hardware-aligned ones. At the core of NTA, a compiler uses catalog statistics to assign guarded static capacities and a hardware-calibrated local cost model to select a TPU-native implementation for each relational operator before JIT compilation. We evaluate our system using the TPC-H benchmark. Taking full advantage of TPU slices and JAX distributed computing, we seamlessly scale our execution environment to run TPC-H benchmark up to Scale Factor 1000 (SF1000). Evaluated against a rigorous CPU ground truth (DuckDB), our native TPU accelerator achieves state-of-the-art performance, achieving a 7.0x single-device speedup over DuckDB at SF100 and a 68.6x speedup with 16 TPUs at SF1000 (1TB). The results establish TPUs not merely as dense neural compute hardware, but as a highly competitive, massively scalable substrate for the next generation of analytical query processing.

cs.DB↗

arXiv2026

CADENZA: Compiling Natural-Language Intent into Task-Specific Operator DAGs for Semantic Query Processing

Semantic query processing engines (SQPEs) extend relational query processing with semantic operators that are executed via model inference over unstructured data. Optimizing such queries is inherently multi-objective: model inference dominates latency and monetary cost, and outputs are stochastic and backend-dependent, so quality must be optimized alongside efficiency. Existing SQPE optimizers do not expose each semantic operator instance's intermediate task outputs as a relational optimization object, leaving optimization unable to filter, reorder, route, threshold, or jointly tune them. We present CADENZA, which compiles each semantic operator instance--a template bound to a natural-language intent--into an intent-specific plan space of typed task DAGs and selects an executable plan under user-specified quality-latency-cost trade-offs. CADENZA introduces task-extended relational algebra (TxRA), a conservative extension of relational algebra with task-specific operators. The logical planner synthesizes seed TxRA plans, applies structural rewrites whose safety conditions are checked from operator dependencies, and enumerates semantics-guided alternatives from alternative-generation templates. The physical planner compiles each task-specific operator into a router over heterogeneous backends and jointly tunes routing cutpoints, backend parameters, and relational thresholds with Bayesian optimization. On SemBench, CADENZA improves the scenario-level averages of quality, latency, and cost by up to +0.49, 165.7x, and 310.3x, respectively, relative to state-of-the-art.

cs.DB↗

arXiv2026

CADENZA in Action: Breaking the Monolith with Intent-Dependent Plan Spaces for Semantic Queries

Semantic query processing engines execute semantic operators, whose behavior is specified by natural-language intents, via model inference over multimodal data. Most existing optimizers optimize the operators at the granularity of monolithic implementations -- such as LLMs and embedding models -- forcing a trade-off between expensive model calls and cheaper alternatives that fail to capture intent-dependent semantics. We present CADENZA, a semantic operator optimizer that compiles an intent into decomposed steps, selects concrete physical implementations for each step, and tunes their parameters under user-specified quality-latency-cost preferences. In this demonstration, users interact with CADENZA through a web interface over multimodal databases, exploring how an intent is decomposed into alternative plans, how each plan is optimized, and how different preferences yield different winning plans.

cs.DB↗

arXiv2024

Resilient by Design: Simulating Street Network Disruptions across Every Urban Area in the World

Street networks allow people and goods to move through cities, but they are vulnerable to disasters like floods, earthquakes, and terrorist attacks. Well-planned network design can make a city more resilient and robust to such disruptions, but we still know little about worldwide patterns of vulnerability, or worldwide empirical relationships between specific design characteristics and resilience. This study quantifies and measures the vulnerability of the street networks of every urban area in the world then models the relationships between vulnerability and street network design characteristics. To do so, we simulate over 2.4 billion trips across more than 8,000 urban areas in 178 countries, while also simulating network disruption events representing floods, earthquakes, and targeted attacks. We find that disrupting high-centrality nodes severely impacts network function. All else equal, networks with higher connectivity, fewer chokepoints, or less circuity are less vulnerable to disruption's impacts. This study thus contributes a new global understanding of network design and vulnerability to the literature. We argue that these design characteristics offer high leverage points for street network resilience and robustness that planners should emphasize when designing or retrofitting urban networks.

physics.soc-ph↗

arXiv2023

Guaranteeing the Õ(AGM/OUT) Runtime for Uniform Sampling and OUT Size Estimation over Joins

We propose a new method for estimating the number of answers OUT of a small join query Q in a large database D, and for uniform sampling over joins. Our method is the first to satisfy all the following statements. - Support arbitrary Q, which can be either acyclic or cyclic, and contain binary and non-binary relations. - Guarantee an arbitrary small error with a high probability always in Õ(AGM/OUT) time, where AGM is the AGM bound OUT (an upper bound of OUT), and Õ hides the polylogarithmic factor of input size. We also explain previous join size estimators in a unified framework. All methods including ours rely on certain indexes on relations in D, which take linear time to build offline. Additionally, we extend our method using generalized hypertree decompositions (GHDs) to achieve a lower complexity than Õ(AGM/OUT) when OUT is small, and present optimization techniques for improving estimation efficiency and accuracy.

cs.DB↗