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International Conference on management of data (SIGMOD)

作者： Kalavri, Vasiliki Salihoglu, Semih Boston Univ Boston MA 02215 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781450392495

GRADES-NDA is the premier workshop on graph data management and analytics that aims to bring together researchers from academia, industry, and government. GRADES-NDA'22 is a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain specific challenges or handling noise in real-world graphs. In 2022, GRADES-NDA is in its fifth edition.

关键词： graph data management network data analytics

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GRADES-NDA 2019: Joint International Workshop on graph data management Experiences & Systems and Network data Analytics 19

GRADES-NDA 2019: Joint International Workshop on Graph Data ...

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ACM SIGMOD International Conference on management of data (SIGMOD)

作者： Arora, Akhil Bhattacharya, Arnab Fletcher, George Ecole Polytech Fed Lausanne Lausanne Switzerland IIT Kanpur Kanpur Uttar Pradesh India TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9781450356435

GRADES-NDA 2019 is the second joint meeting of the GRADES and NDA workshops, which were each independently organized at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The focus of GRADES-NDA is the application areas, usage scenarios, and open challenges in managing large-scale graph-shaped data. To summarize, GRADES-NDA aims to present technical contributions inside graph, RDF, and other data management systems on massive graphs.

关键词： graph data management Network shaped data Big data data mining Machine learning Systems

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April: An Automatic graph data management System Based on Reinforcement Learning 20

April: An Automatic Graph Data Management System Based on Re...

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29th ACM International Conference on Information and Knowledge management (CIKM)

作者： Wang, Hongzhi Qi, Zhixin Zheng, Lei Feng, Yun Ouyang, Junfei Zhang, Haoqi Zhang, Xiangxi Shen, Ziming Liu, Shirong Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781450368599

The great amount and complex structure of graph data bring a big challenge to graph data management. However, traditional management approaches cannot tackle the challenge. Fortunately, reinforcement learning provides a new approach to solve this problem due to its automation and adaptivity in decision making. Motivated by this, we develop April, an automatic graph data management system, which performs storage structure selection, index selection, and query optimization based on reinforcement learning. The system selects storage structure, indices effectively and automatically, and optimizes the SPARQL queries efficiently. April also offers a friendly interface for users, which allows users to interact with the system in a customized mode. We demonstrate the effectiveness and efficiency of April with two graph data benchmarks.

关键词： graph data management reinforcement learning storage structure selection index selection query optimization

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GRADES-NDA'24: 7th Joint Workshop on graph data management Experiences & Systems (GRADES) and Network data Analytics (NDA) 24

GRADES-NDA'24: 7th Joint Workshop on Graph Data Management E...

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ACM International Conference on management of data (SIGMOD)

作者： Hartig, Olaf Kaoudi, Zoi Amazon Web Serv Stockholm Sweden Linkoping Univ Linkoping Sweden IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9798400704222

GRADES-NDA is the premier workshop series on graph data management and analytics that aims to bring together researchers from academia, industry, and governmental organizations. GRADES-NDA'24 is a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as proposing and discussing novel methods and techniques for addressing domain-specific challenges. In 2024, GRADES-NDA is in its seventh edition.

关键词： graph data management network data analytics

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Efficient k-step Weighted Reachability Queries Processing Algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-18页

作者： Mei, Congquan Zhou, Junfeng Du, Ming Chen, Lian Yu, Sheng Tang, Xian Chen, Ziyang Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201620 Peoples R China

Given a data graph G, a source vertex u, and a target vertex v of a reachability query, the reachability query is used to answer whether there exists a path from u to v in G. Reachability query processing is one of the fundamental operations in graph data management, which is widely used in biological networks, communication networks, and social networks to assist data analysis. The data graphs in practical applications usually contain information such as quantization weights associated with the structural relationships, in addition to the structural relationships between vertices. Thus, in addition to the traditional reachability relationships, users may want to further understand whether such reachability relationships satisfy specific constraints. In this paper, we study the problem of efficiently processing k-step reachability queries with weighted constraints in weighted graphs. The k-step weighted reachability query questions are used to answer the question of whether there exists a path from a source vertex u to a goal vertex v in a given weighted graph. If it exists, the path needs to satisfy (1) all edges in the path satisfy the given weight constraints, and (2) the length of the path does not exceed the given distance threshold k. To address the problem, firstly, WKRI indexes supporting k-step weighted reachability query processing and index construction methods based on efficient pruning strategies are proposed. Secondly, the idea of constructing indexes based on part of the vertices is proposed to reduce the size of the indexes and two optimized indexes are designed and implemented based on the vertex coverage set to design and implement two optimized indexes GWKRI and LWKRI. Finally, experiments are conducted on several real datasets. The experimental results verify the efficiency of the method proposed in this paper in answering k-step weighted reachability queries.

关键词： graph data management Reachability query processing k-step reachability queries Weight-constrained reachability queries

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Capsule: An Out-of-Core Training Mechanism for Colossal GNNs

Proceedings of the ACM on Management of Data

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Proceedings of the ACM on management of data 2025年第1期3卷 1-30页

作者： Yongan Xiang Zezhong Ding Rui Guo Shangyou Wang Xike Xie S. Kevin Zhou School of Computer Science and Technology University of Science and Technology of China (USTC) Hefei Anhui China & Data Darkness Lab Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China School of Artificial Intelligence and Data Science USTC Hefei Anhui China & Data Darkness Lab Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China School of Biomedical Engineering USTC Suzhou Jiangsu China & Data Darkness Lab Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China School of Biomedical Engineering USTC Suzhou Jiangsu China & MIRACLE Center Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China

Cutting-edge platforms of graph neural networks (GNNs), such as DGL and PyG, harness the parallel processing power of GPUs to extract structural information from graph data, achieving state-of-the-art (SOTA) performance in fields such as recommendation systems, knowledge graphs, and bioinformatics. Despite the computational advantages provided by GPUs, these GNN platforms struggle with scalability challenges due to the colossal graphical structures processed and the limited memory capacities of GPUs. In response, this work introduces Capsule, a new out-of-core mechanism for large-scale GNN training. Unlike existing out-of-core GNN systems, which use main or secondary memory as operative memory and use CPU kernels during non-backpropagation computation, Capsule uses GPU memory and GPU kernels. By substantially leveraging the parallelization capabilities of GPUs, Capsule significantly enhances GNN training efficiency. In addition, Capsule can be smoothly integrated to mainstream open-source GNN frameworks, DGL and PyG, in a play-and-plug manner. Through a prototype implementation and comprehensive experiments on real datasets, we demonstrate that Capsule can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems.

关键词： gpus graph data management graph neural networks

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graphONE: A data Store for Real-time Analytics on Evolving graphs

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ACM TRANSACTIONS ON STORAGE 2020年第4期15卷 1-40页

作者： Kumar, Pradeep Huang, H. Howie William & Mary Dept Comp Sci 251 Jamestown Rd Williamsburg VA 23185 USA George Washington Univ Dept Elect & Comp Engn 800 22nd St NW Washington DC 20052 USA

There is a growing need to perform a diverse set of real-time analytics (batch and stream analytics) on evolving graphs to deliver the values of big data to users. The key requirement from such applications is to have a data store to support their diverse data access efficiently, while concurrently ingesting fine-grained updates at a high velocity. Unfortunately, current graph systems, either graph databases or analytics engines, are not designed to achieve high performance for both operations;rather, they excel in one area that keeps a private data store in a specialized way to favor their operations only. To address this challenge, we have designed and developed graphONE, a graph data store that abstracts the graph data store away from the specialized systems to solve the fundamental research problems associated with the data store design. It combines two complementary graph storage formats (edge list and adjacency list) and uses dual versioning to decouple graph computations from updates. Importantly, it presents a new data abstraction, graphView, to enable data access at two different granularities of data ingestions (called data visibility) for concurrent execution of diverse classes of real-time graph analytics with only a small data duplication. Experimental results show that graphONE is able to deliver 11.40x and 5.36x average speedup in ingestion rate against LLAMA and Stinger, the two state-of-the-art dynamic graph systems, respectively. Further, they achieve an average speedup of 8.75x and 4.14x against LLAMA and 12.80x and 3.18x against Stinger for BFS and PageRank analytics (batch version), respectively. graphONE also gains over 2,000x speedup against Kickstarter, a state-of-the-art stream analytics engine in ingesting the streaming edges and performing streaming BPS when treating first half as a base snapshot and rest as streaming edge in a synthetic graph. graphONE also achieves an ingestion rate of two to three orders of magnitude higher than graph d

关键词： graph systems graph data management unified graph data store batch analytics stream analytics

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Building self-clustering RDF databases using Tunable-LSH

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VLDB JOURNAL 2019年第2期28卷 173-195页

作者： Aluc, Guenes Ozsu, M. Tamer Daudjee, Khuzaima SAP Labs Waterloo ON Canada Univ Waterloo Waterloo ON Canada

The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF. Recent advances in information extraction, linked data management and the Semantic Web have led to a rapid increase in both the volume and the variety of RDF data that are publicly available. As businesses start to capitalize on RDF data, RDF data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle. Consequently, there is a growing need for developing workload-adaptive and self-tuning RDF data management systems. To realize this objective, we introduce a fast and efficient method for dynamically clustering records in an RDF data management system. Specifically, we assume nothing about the workload upfront, but as SPARQL queries are executed, we keep track of records that are co-accessed by the queries in the workload and physically cluster them. To decide dynamically and in constant-time where a record needs to be placed in the storage system, we develop a new locality-sensitive hashing (LSH) scheme, Tunable-LSH. Using Tunable-LSH, records that are co-accessed across similar sets of queries can be hashed to the same or nearby physical pages in the storage system. What sets Tunable-LSH apart from existing LSH schemes is that it can auto-tune to achieve the aforementioned clustering objective with high accuracy even when the workloads change. Experimental evaluation of Tunable-LSH in an RDF data management system as well as in a standalone hashtable shows end-to-end performance gains over existing solutions.

关键词： RDF SPARQL graph data management Storage and indexing Workload-adaptive tuning Locality-sensitive hashing Clustering Physical database design

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Reasoning on property graphs with graph generating dependencies

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INFORMATION SCIENCES 2024年 672卷

作者： Shimomura, Larissa C. Yakovets, Nikolay Fletcher, George Univ Stuttgart Inst Parallel & Distributed Syst IPVS Univ Str 38 D-70569 Stuttgart Germany Eindhoven Univ Technol Dept Math & Comp Sci POB 513 NL-5600 MB Eindhoven Netherlands

data dependencies are a key concept in data management and have been researched in data integration, data quality and query optimization. With the increasing use of graph-structured data in diverse applications, there is also an increasing interest in the study of graph data dependencies. In this scenario, different classes of graph data dependencies have been proposed in the literature. In this work, we study the class of graph Generating Dependencies (GGDs). graph Generating Dependencies (GGDs) informally express constraints between two (possibly different) graph patterns which enforce relationships on both graph's data (via property value constraints) and its structure (via topological constraints). While most of previously proposed classes of graph data dependencies focus on generalizing equality-generating dependencies for graph data, graph Generating Dependencies (GGDs) can express tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. Given this new class of dependency, in this paper, we discuss the reasoning behind GGDs on Property graphs. We propose algorithms to solve three main reasoning problems: the satisfiability , implication , and validation problems for GGDs and analyze their complexity. By studying these problems, we can understand the expressiveness and the limitations of GGDs in practical applications. To demonstrate the practical use of GGDs, we propose an algorithm that finds inconsistencies in data through validation of GGDs. Our experiments show that even though the validation of GGDs has high computational complexity, GGDs can be used to find data inconsistencies in a feasible execution time on both synthetic and real-world data.

关键词： graph dependencies graph data management Tuple-generating dependencies Property graphs

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Fast Reachability Queries Answering Based on RCN Reduction

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第3期35卷 2590-2609页

作者： Zhou, Junfeng Yu, Jeffrey Xu Qiu, Yaxian Tang, Xian Chen, Ziyang Du, Ming Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Univ Engn Sci Sch Comp Sci & Technol Shanghai 201620 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201620 Peoples R China

Answering reachability queries is one of the fundamental graph operations. One way to make acceleration is directly building index on the input graph. Considering that the size of the input graph has a great impact on the query performance, the other way is reducing the size of the input graph, such that the given queries can be answered over a smaller graph. Although the input graph can be reduced significantly in some cases by existing approaches, a smaller reduced graph does not always mean a positive effect on the query performance. In this paper, we study graph reduction to accelerate reachability queries answering. We propose a novel graph reduction approach, namely RCN reduction, to reduce the input graph G with |V| nodes into a smaller one G(r) with |V-r| nodes. Assume that the probability of a node of G to be a query node is 1/|V|, we show that based on our approach, the lower bound probability that a query q can be answered in constant time is 1 - (|V-T|/|V|)(2), denoting that the probability that q needs to be answered over the reduced graph is (|V-T|/|V|)(2), which means the smaller the reduced graph, the larger the probability that q can be answered in constant time. We show the difficulties of RCN reduction and propose efficient algorithms to improve the reduction ratio. Based on the result of RCN reduction, we further propose a novel labeling scheme to accelerate queries answering. We confirm the efficiency of our approach by extensive experimental results for graph reduction and reachability queries processing using 20 real datasets.

关键词： graph data management reachability queries processing graph reduction

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