arXiv subjects

Zhanhao Zhao

Publications and source records attributed to Zhanhao Zhao.

13 recordsLinked to original sources

arXiv2026

TVA: A Version-aware Temporal Graph Storage System for Real-time Analytics

Analyzing temporal graphs can reveal valuable insights that are typically hidden in static graphs. Unfortunately, existing graph storage systems either lack native temporal support or suffer from high latency when querying temporal graphs. This paper presents TVA, a new temporal graph storage system designed for efficient temporal query processing. First, TVA introduces a specialized multi-version storage architecture that separates version metadata from actual data, i.e., the property values associated with different versions of vertices and edges. This architecture enables efficient version retrieval for a vertex or edge by quickly locating valid version metadata and directly dereferencing it to access the corresponding property values. Second, we design tailored data structures, namely the temporal table and enhanced hopscotch-based hashing, to compactly organize the version metadata of adjacent vertices and edges, thus reducing random I/O for metadata lookups during the neighborhood scan initiated from a vertex. Finally, to further accelerate neighborhood scans over multiple vertices, we propose a version-kipping strategy that reuses temporal information obtained from prior scans, thereby avoiding redundant metadata lookups across scans. Empirical evaluations demonstrate that TVA achieves up to 9.9x lower temporal query latency and 2.2x lower storage overhead compared to state-of-the-art temporal graph storage systems.

cs.DB↗

arXiv2026

Towards Effective Orchestration of AI x DB Workloads

AI-driven analytics are increasingly crucial to data-centric decision-making. The practice of exporting data to machine learning runtimes incurs high overhead, limits robustness to data drift, and expands the attack surface, especially in multi-tenant, heterogeneous data systems. Integrating AI directly into database engines, while offering clear benefits, introduces challenges in managing joint query processing and model execution, optimizing end-to-end performance, coordinating execution under resource contention, and enforcing strong security and access-control guarantees. This paper discusses the challenges of joint DB-AI, or AIxDB, data management and query processing within AI-powered data systems. It presents various challenges that need to be addressed carefully, such as query optimization, execution scheduling, and distributed execution over heterogeneous hardware. Database components such as transaction management and access control need to be re-examined to support AI lifecycle management, mitigate data drift, and protect sensitive data from unauthorized AI operations. We present a design and preliminary results to demonstrate what may be key to the performance for serving AIxDB queries.

cs.DB↗

arXiv2025

NeurStore: Efficient In-database Deep Learning Model Management System

With the prevalence of in-database AI-powered analytics, there is an increasing demand for database systems to efficiently manage the ever-expanding number and size of deep learning models. However, existing database systems typically store entire models as monolithic files or apply compression techniques that overlook the structural characteristics of deep learning models, resulting in suboptimal model storage overhead. This paper presents NeurStore, a novel in-database model management system that enables efficient storage and utilization of deep learning models. First, NeurStore employs a tensor-based model storage engine to enable fine-grained model storage within databases. In particular, we enhance the hierarchical navigable small world (HNSW) graph to index tensors, and only store additional deltas for tensors within a predefined similarity threshold to ensure tensor-level deduplication. Second, we propose a delta quantization algorithm that effectively compresses delta tensors, thus achieving a superior compression ratio with controllable model accuracy loss. Finally, we devise a compression-aware model loading mechanism, which improves model utilization performance by enabling direct computation on compressed tensors. Experimental evaluations demonstrate that NeurStore achieves superior compression ratios and competitive model loading throughput compared to state-of-the-art approaches.

cs.DB↗

arXiv2025

Observatory Science with eXTP

Scheduled for launch in 2030, the enhanced X-ray Timing and Polarization (eXTP) telescope is a Chinese space-based mission aimed at studying extreme conditions and phenomena in astrophysics. eXTP will feature three main payloads: Spectroscopy Focusing Arrays (SFAs), Polarimetry Focusing Arrays (PFAs), and a Wide-field Camera (W2C). This white paper outlines observatory science, incorporating key scientific advances and instrumental changes since the publication of the previous white paper [1]. We will discuss perspectives of eXTP on the research domains of flare stars, supernova remnants, pulsar wind nebulae, cataclysmic variables, X-ray binaries, ultraluminous X-ray sources, AGN, and pulsar-based positioning and timekeeping.

astro-ph.IM↗

arXiv2025

NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling

Learned database components, which deeply integrate machine learning into their design, have been extensively studied in recent years. Given the dynamism of databases, where data and workloads continuously drift, it is crucial for learned database components to remain effective and efficient in the face of data and workload drift. Robustness, therefore, is a key factor in assessing their practical applicability. Although recent works examine learned database components under specific drift, they fail to enable systematic performance evaluations across a broad range of drift or under customized drift as needed. This paper presents NeurBench, a new benchmark suite that supports evaluating learned database components under measurable and controllable data and workload drift. We quantify diverse types of drift by introducing a key concept called the drift factor. Building on this formulation, we propose a drift-aware data and workload generation framework that effectively simulates real-world drift while preserving inherent correlations. Experimental results demonstrate the effectiveness of NeurBench in generating realistic data and workload drift, while providing insights into the performance of representative learned database components under different drift scenarios.

cs.DB↗

arXiv2025

Flaring Activities of Fast Rotating Stars have Solar-like Latitudinal Distribution

The dynamo theory has always been one of the biggest mysteries in stellar physics. One key reason for its uncertainty is poor knowledge of the dynamo process on stars except the Sun. The most important observation feature of solar dynamo is that active regions only appear at low latitudes, which provides a crucial constraint to the dynamo theory, while Doppler imaging, the current technique to spatially resolve stellar hemisphere, is difficult to distinguish the equatorial region . Hence, the latitudinal distribution of active regions (LDAR) of stars is ambiguous and controversial, mainly due to the limit of the current technique for spatially resolving the stellar surface. Fast rotating stars, which are young and active, are thought to operate with a different dynamo process than the Sun. We study their LDAR and compare them with the Sun to reveal the underlying dynamo process. Flares are drastic and observational activity events, which occur in active regions. Here, we propose a new method to study how the apparent flaring activity varies with respect to the inclination to determine the LDAR of fast rotating stars.We find that the LDAR of fast rotating stars is consistent with that of the Sun, contrary to expectations. Our results provide a crucial constraint to stellar dynamo, indicating that the solar-like dynamo also applies to fast rotating stars, even spanning different stages of their evolution.

astro-ph.SR↗

arXiv2025

TxnSails: Achieving Serializable Transaction Scheduling with Self-Adaptive Isolation Level Selection

Achieving the serializable isolation level, regarded as the gold standard for transaction processing, is costly. Recent studies reveal that adjusting specific query patterns within a workload can still achieve serializability even at lower isolation levels. Nevertheless, these studies typically overlook the trade-off between the performance advantages of lower isolation levels and the overhead required to maintain serializability, potentially leading to suboptimal isolation level choices that fail to maximize performance. In this paper, we present TxnSails, a middle-tier solution designed to achieve serializable scheduling with self-adaptive isolation level selection. First, TxnSails incorporates a unified concurrency control algorithm that achieves serializability at lower isolation levels with minimal additional overhead. Second, TxnSails employs a deep learning method to characterize the trade-off between the performance benefits and overhead associated with lower isolation levels, thus predicting the optimal isolation level. Finally, TxnSails implements a cross-isolation validation mechanism to ensure serializability during real-time isolation level transitions. Extensive experiments demonstrate that TxnSails outperforms state-of-the-art solutions by up to 26.7x and PostgreSQL's serializable isolation level by up to 4.8x.

cs.DB↗

arXiv2025

NeurDB: On the Design and Implementation of an AI-powered Autonomous Database

Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry sectors. Nonetheless, most existing approaches fail to account for the dynamic nature of databases, which renders them ineffective for real-world applications characterized by evolving data and workloads. This paper introduces NeurDB, an AI-powered autonomous database that deepens the fusion of AI and databases with adaptability to data and workload drift. NeurDB establishes a new in-database AI ecosystem that seamlessly integrates AI workflows within the database. This integration enables efficient and effective in-database AI analytics and fast-adaptive learned system components. Empirical evaluations demonstrate that NeurDB substantially outperforms existing solutions in managing AI analytics tasks, with the proposed learned components more effectively handling environmental dynamism than state-of-the-art approaches.

cs.DB↗

arXiv2024

GeoTP: Latency-aware Geo-Distributed Transaction Processing in Database Middlewares (Extended Version)

The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed across national and international boundaries. However, transaction processing performance significantly drops due to the high network latency between the middleware and data sources and the long lock contention span, where transactions may be blocked while waiting for the locks held by concurrent transactions. In this paper, we propose GeoTP, a latency-aware geo-distributed transaction processing approach in database middlewares. GeoTP incorporates three key techniques to enhance geo-distributed transaction performance. First, we propose a decentralized prepare mechanism, which diminishes the requirement of network round trips for distributed transactions. Second, we design a latency-aware scheduler to minimize the lock contention span by strategically postponing the lock acquisition time point. Third, heuristic optimizations are proposed for the scheduler to reduce the lock contention span further. We implemented GeoTP on Apache Shardingsphere, a state-of-the-art middleware, and extended it into Apache ScalarDB. Experimental results on YCSB and TPC-C demonstrate that GeoTP achieves up to 17.7x performance improvement over Shardingsphere.

cs.DB↗

arXiv2024

NeurDB: An AI-powered Autonomous Data System

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

cs.DB↗

arXiv2024

AeonG: An Efficient Built-in Temporal Support in Graph Databases

Real world graphs are often dynamic and evolve over time. It is crucial for storing and querying graph evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73X lower storage consumption and 2.57X lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features

cs.DB↗

arXiv2024

Lion: Minimizing Distributed Transactions through Adaptive Replica Provision (Extended Version)

Distributed transaction processing often involves multiple rounds of cross-node communications, and therefore tends to be slow. To improve performance, existing approaches convert distributed transactions into single-node transactions by either migrating co-accessed partitions onto the same nodes or establishing a super node housing replicas of the entire database. However, migration-based methods might cause transactions to be blocked due to waiting for data migration, while the super node can become a bottleneck. In this paper, we present Lion, a novel transaction processing protocol that utilizes partition-based replication to reduce the occurrence of distributed transactions. Lion aims to assign a node with one replica from each partition involved in a given transaction's read or write operations. To ensure such a node is available, we propose an adaptive replica provision mechanism, enhanced with an LSTM-based workload prediction algorithm, to determine the appropriate node for locating replicas of co-accessed partitions. The adaptation of replica placement is conducted preemptively and asynchronously, thereby minimizing its impact on performance. By employing this adaptive replica placement strategy, we ensure that the majority of transactions can be efficiently processed on a single node without additional overhead. Only a small fraction of transactions will need to be treated as regular distributed transactions when such a node is unavailable. Consequently, Lion effectively minimizes distributed transactions while avoiding any disruption caused by data migration or the creation of a super node. We conduct extensive experiments to compare Lion against various transaction processing protocols. The results show that Lion achieves up to 2.7x higher throughput and 76.4% better scalability against these state-of-the-art approaches.

cs.DC↗

arXiv2023

A Statistical Study of Soft X-ray Flares on Solar-type Stars

The statistical characteristic of stellar flares at optical bands has received an extensive study, but it remains to be studied at soft X-ray bands, in particular for solar-type stars. Here, we present a statistical study of soft X-ray flares on solar-type stars, which can help understand multi-wavelength behaviors of stellar flares. We mainly use Chandra Source Catalog Release 2.0, which includes a number of flaring stars with denoted variability, and Gaia Data Release 3, which includes necessary information for classifying stars. We also develop a set of methods for identifying and classifying stellar soft X-ray flares and estimating their properties. A detailed statistical investigation for 129 flare samples on 103 nearby solar-type stars as selected yields the following main results. (1) The flare energy emitted at the soft X-ray band in our sample ranges from $\sim 10^{33}$ to $\sim 10^{37} \ \mathrm{erg}$, and the majority of them are superflares with the most energetic one having energy of $6.0_{-4.7}^{+3.2} \times 10^{37} \ \mathrm{erg}$. (2) The flare duration is related to its energy as formulated by $T_\mathrm{duration,SXR} \propto E_\mathrm{flare,SXR}^{\ 0.201 \pm 0.024}$, which is different from those derived at optical and NIR bands, indicating distinct radiation mechanisms at different bands. (3) The frequency distribution of stellar flares as a function of energy is formulated as $\mathrm{d} N_\mathrm{flare} / \mathrm{d} E_\mathrm{flare,SXR} \propto E_\mathrm{flare,SXR}^{\ -1.77}$, which is similar to the results found at other bands and on other types of stars, indicating that the energy emitted at the soft X-ray band could be a constant fraction of the full-band bolometric energy.

astro-ph.SR↗