WO2021208174A1 - Procédé de calcul de graphe de type distribué, terminal, système, et support de stockage - Google Patents

Procédé de calcul de graphe de type distribué, terminal, système, et support de stockage Download PDF

Info

Publication number
WO2021208174A1
WO2021208174A1 PCT/CN2020/090238 CN2020090238W WO2021208174A1 WO 2021208174 A1 WO2021208174 A1 WO 2021208174A1 CN 2020090238 W CN2020090238 W CN 2020090238W WO 2021208174 A1 WO2021208174 A1 WO 2021208174A1
Authority
WO
WIPO (PCT)
Prior art keywords
graph
data
distributed
preprocessing
algorithm
Prior art date
Application number
PCT/CN2020/090238
Other languages
English (en)
Chinese (zh)
Inventor
华井雅俊
泽奥多洛保罗斯乔治斯
Original Assignee
南方科技大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 南方科技大学 filed Critical 南方科技大学
Publication of WO2021208174A1 publication Critical patent/WO2021208174A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

  • the embodiments of the present application relate to, but are not limited to, the field of graph computing technology, and in particular, to a distributed graph computing method, terminal, system, and storage medium.
  • Graph is an abstract data structure used to represent the association relationship between objects. It is described by using vertices (Vertex) and edges (Edge). The vertices represent objects, and the edges represent the relationships between objects. Based on this, data that can be abstracted into graphs is graph data.
  • Graph computing is a process in which graphs are used as data models to express and solve problems.
  • distributed computing is used to analyze large-scale graph data.
  • the large-scale graph is divided into several subgraphs, and multiple slave nodes are used for calculation, which can effectively utilize multiple computing resources.
  • high-quality partitioning methods consume a lot of time during the calculation, which leads to higher energy consumption in the partitioning phase.
  • high-speed generation of partitions will result in low-quality partitions, causing serious performance losses.
  • the embodiment of the application provides a distributed graph calculation method, terminal, system and storage medium, which uses graph data preprocessing, and when large-scale graph data analysis is performed, the graph data is only transmitted once, which can segment the graph with high quality and efficiency. Data, increase the speed of distributed graph calculation and reduce energy consumption.
  • an embodiment of the present application provides a distributed graph calculation method, including:
  • the distributed architecture and the first intermediate preprocessing graph the first division graph is obtained
  • first distributed graph analysis data is obtained.
  • the distributed graph calculation method further includes:
  • a second division graph is obtained according to the graph division algorithm, the distributed architecture, and the first intermediate preprocessing graph;
  • the distributed graph calculation method further includes:
  • first graph data is not the same as the second graph data, obtaining difference data between the second graph data and the first graph data;
  • a third distributed graph analysis data is obtained.
  • the difference map preprocessing algorithm includes:
  • a second intermediate preprocessed graph is obtained according to the difference data and the incremental graph preprocessing algorithm.
  • the incremental graph preprocessing algorithm further includes:
  • an incremental graph edge sort is obtained.
  • the incremental graph edge sorting algorithm is applied to the main computing node, and the incremental graph edge sorting algorithm includes:
  • the difference map preprocessing algorithm further includes:
  • the second graph data after removing the decremented data and the graph preprocessing algorithm obtain a second intermediate preprocessed graph.
  • the distributed graph preprocessing algorithm further includes preprocessing graph edge sorting
  • the preprocessing graph edge sorting includes:
  • the first intermediate preprocessed graph is obtained.
  • the obtaining the first intermediate preprocessing graph according to the edge data and the vertex data includes:
  • the priority queue is obtained
  • the first intermediate preprocessing diagram includes:
  • the starting vertex ID of the edge and the ending vertex ID of the edge are stored in binary format.
  • the graph division algorithm includes:
  • node configuration information includes one or more of the number of nodes, node specifications, and node performance
  • an embodiment of the present application provides a terminal, including: a first memory, a first processor, and a computer program stored on the first memory and running on the first processor, the first processor Realize when executing the program:
  • an embodiment of the present application provides a distributed graph computing system, including a first distributed computing device and a second distributed computing device;
  • the first distributed computing device includes: a second memory, a second processor, and a first computer program that is stored on the second memory and can run on the second processor; the second processor executes the first computer program
  • a computer program is implemented: the distributed graph calculation method described in the first aspect
  • the second distributed computing device includes: a third memory, a third processor, and a second computer program that is stored on the third memory and can run on the third processor; the third processor executes the first
  • the computer program implements the distributed graph calculation method described in the first aspect.
  • an embodiment of the present application provides a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to:
  • the embodiment of the application converts the original graph data into computer-readable intermediate graph data based on the preprocessing of graph data, graph division algorithm and incremental graph edge sorting algorithm, which enables the subsequent graph division to be carried out quickly, and also provides High-quality graph partitioning greatly reduces communication overhead and speeds up the calculation and analysis of distributed graphs.
  • FIG. 1 is a schematic flowchart of a distributed graph calculation method provided by an embodiment of the application
  • FIG. 2 is a schematic flowchart of a distributed graph calculation method provided by another embodiment of the application.
  • FIG. 3 is a schematic flowchart of a distributed graph computing method provided by another embodiment of this application.
  • FIG. 4 is a schematic structural diagram of a graph partition algorithm provided by an embodiment of this application.
  • FIG. 5 is a schematic structural diagram of an incremental graph edge sorting algorithm provided by an embodiment of the application.
  • graph In the distributed graph computing technology of known technology, graph is a basic and ubiquitous abstract concept, which is widely used in modeling various problems in the real world.
  • the vertices in the graph represent users, and the edges represent friendship relationships between users; in e-commerce services, the vertices represent users and products, and the edges represent purchase history.
  • graph data has been growing naturally.
  • one of the world's largest online social network services already contains about one trillion friendships.
  • the embodiments of the present application provide a distributed graph computing method, terminal, system, and storage medium, which can convert original graph data into computer-readable intermediate graph data, which enables the subsequent graph division to be carried out quickly, while also providing The high-quality graph partition is created, and the generated high-quality partition greatly reduces the communication overhead, and accelerates the calculation and analysis speed of the distributed graph.
  • the terminal may be a mobile terminal device or a non-mobile terminal device.
  • Mobile terminal devices can be mobile phones, tablet computers, laptops, palmtop computers, vehicle-mounted terminal devices, wearable devices, ultra-mobile personal computers, netbooks, digital cameras, video cameras or personal digital assistants, etc.
  • non-mobile terminal devices can be personal computers, Workstations, servers, televisions, teller machines, self-service machines, surveillance cameras or box cameras, etc.
  • An embodiment of the present application discloses a distributed graph calculation method.
  • Fig. 1 is a flowchart of a distributed graph calculation method.
  • the calculation method shown in Fig. 1 at least includes the following steps:
  • Step S100 Obtain the data of the first image
  • Step S101 graph preprocessing algorithm
  • Step S102 Obtain a first intermediate preprocessing map
  • Step S103 Obtain distributed architecture information
  • Step S104 graph partition algorithm
  • Step S105 the first division map
  • Step S106 Distributed graph analysis.
  • the graph preprocessing algorithm is used to form the first intermediate preprocessing graph.
  • the graph preprocessing algorithm uses graph edge sorting and converts the first intermediate preprocessing graph into a computer-readable binary graph format.
  • the graph partition algorithm is applied to skip the redundant data scrambling of the graph elements to use the first intermediate preprocessing graph, and combine the distributed architecture information to generate the first partition graph. Performing distributed graph analysis according to the first division graph can obtain high-quality and high-efficiency distributed graph calculations.
  • the first intermediate preprocessing image is in a computer-readable binary image format.
  • Each box unit represents a 32-bit or 64-bit integer. Every two boxes store the starting vertex ID and ending vertex ID of the edge.
  • the graph data can be read from the machine without any communication overhead.
  • the graph preprocessing algorithm converts the first graph data into a first intermediate preprocess graph.
  • First convert the first image data into the first intermediate preprocessing image.
  • the first intermediate preprocessing graph is a computer-readable binary graph format and is expressed as an edge sequence.
  • the expression of the conversion algorithm is:
  • E ⁇ is an edge sequence E sorted by the sorting function ⁇ :E ⁇ N.
  • the expression of the graph preprocessing algorithm is:
  • V(E) is a set of vertices of edge E.
  • Fig. 2 is another flowchart of the distributed graph calculation method.
  • the calculation method shown in Fig. 2 includes at least the following steps:
  • Step S200 Obtain the second image data
  • Step S201 Compare the data of the second image with the data of the first image
  • Step S202 the second image data is the same as the first image data
  • Step S203 Obtain distributed architecture information
  • Step S204 the first intermediate preprocessing map
  • Step S205 graph partition algorithm
  • Step S206 the second division map
  • Step S207 Distributed graph analysis.
  • the first intermediate preprocessing graph is used for analysis.
  • the first intermediate preprocessing graph may be in a computer-readable binary graph format.
  • the graph partition algorithm is applied to skip the redundant data scrambling of the graph elements to use the first intermediate preprocessing graph and combine the distributed architecture information to generate the second partition graph.
  • Performing distributed graph analysis according to the second division graph can obtain high-quality and high-efficiency distributed graph calculations. In the calculation process, there is no need to repeat the data preprocessing process, which improves the efficiency of the calculation.
  • Fig. 3 is another flowchart of the distributed graph calculation method.
  • the calculation method shown in Fig. 3 includes at least the following steps:
  • Step S300 the data of the second picture is different from the data of the first picture
  • Step S301 Obtain a first division map
  • Step S302 graph preprocessing algorithm
  • Step S303 the second intermediate processing diagram
  • Step S304 Obtain distributed architecture information
  • Step S305 Obtain the change data of the first image data
  • Step S306 graph partition algorithm
  • Step S307 the third division map.
  • the second intermediate preprocessing graph is obtained according to the first division graph, the change data of the first graph data and the graph preprocessing algorithm.
  • the second intermediate preprocessing image is a computer-readable binary image format.
  • the graph partition algorithm is applied to skip the redundant data scrambling of the graph elements to use the second intermediate preprocessing graph and combine the distributed architecture information to generate the third partition graph.
  • the first image data includes users, products, and purchase history.
  • the users and products are represented by the vertices of the graph, and the purchase history is represented by the edges.
  • the graph preprocessing algorithm converts the first graph data into the second intermediate processing graph, so that the graph division algorithm can immediately generate high-quality divisions. After that, perform distributed graph analysis. For example, discovering user preferences and predicting products that may be purchased, so as to make corresponding recommendations. Due to the purchase history, new users and new product updates, the graph data will change periodically, so repeated analysis is required.
  • the vertex data of at least one first graph data is obtained, the priority queue is obtained according to the graph edge sorting algorithm, and the first intermediate preprocessing graph is obtained according to the breadth first search (BFS) and the priority queue .
  • BFS breadth first search
  • Priority queue sorting is required before breadth-first search.
  • the expression of the priority queue sorting on the edge of the graph is:
  • D[v] is the number of unvisited vertices of v in the breadth-first search process
  • M[v] is the order of the largest edge among the adjacent edges of v during BFS (if the edges are not already sorted, then M[v] is 0).
  • the vertices are sorted in ascending order.
  • the graph preprocessing algorithm includes an incremental graph preprocessing algorithm.
  • Incremental graph preprocessing algorithms include incremental graph edge sorting algorithms.
  • FIG 4 is a structural diagram of the graph partitioning algorithm.
  • the computing node obtains the number of broadcast edges from the distributed file system through the network and obtains the node configuration from the infrastructure. According to the number of edges and node configuration, each node finds a cross pointer to determine the starting point and ending point for dividing the graph data. The pointer is transferred to the file system via the network. After that, the distributed file system divides the edge into multiple partitions and sends these partitions back to the computing node. Efficiently forward partitions by dividing data into blocks. Finally, each node obtains the partition before starting the distributed graph calculation. In the existing method, the huge entire graph data is transmitted twice via the network. However, the method of the present application only transmits the graph data once, because it can calculate the partition using only metadata (ie, the number of edges and node configuration). Therefore, communication overhead can be saved and the work efficiency of each node in distributed graph calculation can be improved.
  • the graph partition algorithm needs to use a distributed file system, the graph partition becomes faster, node configuration information, compute nodes, calculate split pointers, and obtain partitions.
  • the graph partition algorithm obtains the forward pointer and the forward chunk through the network broadcast edge number during calculation.
  • the node configuration information includes, for example, the number of CPUs, CPU specifications, memory size, network performance, node reliability, and so on.
  • the edge sequence is divided, so that the workload of each node in the process of distributed graph calculation and analysis is balanced.
  • the graph partitioning algorithm is executed on the cloud infrastructure.
  • the computing node is a virtual machine, and the network is a virtual network.
  • Distributed file systems are usually located in different clusters or data centers. Therefore, the delay and bandwidth of the network are usually limited.
  • the algorithm obtains the node configuration of the virtual host, and the node configuration of each virtual host may be different.
  • Each node takes into account the differences in specifications, and splits the data in such a way that in the process of distributed graph analysis, the workload among the virtual hosts becomes balanced. The efficiency of moving large graph data from the file system to the virtual host is improved.
  • the computing power therein is delivered in the pay-as-you-go model, saving computing power directly reduces the payment cost of graph analysis.
  • the distributed graph computing method when used on a private cluster, the graph data only needs to be transmitted twice, which will result in a private cluster.
  • the use of this distributed graph computing method can reduce energy costs, so that the graph data can be compared. Perform a more economical analysis.
  • the distributed graph calculation method can be used in page ranking (PageRank) calculation, because more iterations can be performed, so that a more accurate ranking can be obtained.
  • PageRank page ranking
  • the distributed graph calculation method can be used in top-k type algorithms (such as top-k similarity analysis or top-k graph pattern matching), and more results can be obtained (k can be increased) .
  • the distributed graph computing method can be used in graph-based machine learning. Since the distributed graph calculation method can obtain calculation results more quickly, more time can be used in the learning phase in the process of machine learning, and the prediction task will become more accurate.
  • the distributed graph computing method enables real-time analysis and data-driven analysis. Make graph analysis more interactive.
  • FIG. 5 is a schematic diagram of the structure of the incremental graph edge sorting algorithm.
  • the incremental graph edge sorting algorithm is implemented in a distributed computing manner.
  • An embodiment of the incremental graph edge sorting algorithm uses a master-slave architecture, which includes a master computing node and a slave computing node.
  • the changed graph data is broadcast to the subordinate computing nodes.
  • each local optimal search algorithm obtains changed graph data and sorted partitions in its nodes.
  • the algorithm calculates the approximate solution of the optimization problem of the partitioned graph locally and in parallel.
  • the main computing node collects local solutions and calculates optimized solutions.
  • the optimized local solution is broadcast to the slave node, so that the slave node obtains the local optimal order with the smallest increment of the objective function.
  • the master node distributes the graph difference data to the slave nodes.
  • the slave node obtains the local optimal solution according to the partition graph preprocessed in the last iteration and the local optimal solution search algorithm, and sends the local optimal solution to the master node.
  • the master node collects the local optimal solution, it calculates the optimal solution, then uses the optimal solution to calculate the local optimal solution, and sends the local optimal solution to the slave computing node.
  • the subsequent calculation process is performed after the data is removed.
  • an incremental graph preprocessing algorithm is used for calculation.
  • the expression of the incremental graph preprocessing algorithm is:
  • the incremental graph preprocessing algorithm only processes a part of the entire graph, that is, only scans the adjacent edges of the starting vertex and the ending vertex of the new edge. Then, a new edge order is calculated to minimize the increment of the objective function.
  • Using the incremental graph preprocessing algorithm can reduce the complexity of the calculation when the first graph data is updated, thereby reducing energy consumption.
  • the present application provides a terminal for executing a distributed graph calculation method.
  • the present application provides a distributed graph computing system for executing a distributed graph computing method.
  • the present application provides a computer-readable medium for executing a distributed graph computing method.
  • the device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • computer storage medium includes volatile and non-volatile data implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data).
  • Information such as computer-readable instructions, data structures, program modules, or other data.
  • Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or Any other medium used to store desired information and that can be accessed by a computer.
  • communication media usually contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as carrier waves or other transmission mechanisms, and may include any information delivery media. .

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Image Processing (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Editing Of Facsimile Originals (AREA)
  • Image Generation (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Procédé de calcul de graphe de type distribué, terminal, système, et support de stockage. Sur la base d'algorithmes de pré-traitement et de partitionnement de graphe pour des données de graphe ainsi qu'un algorithme de tri de bords de graphe incrémentiel, des données de graphe originales sont converties en données de graphe intermédiaires lisibles par ordinateur, ce qui permet une exécution rapide d'un partitionnement de graphe ultérieur, et un partitionnement de graphe de haute qualité est en outre fourni, les partitions de haute qualité générées réduisant significativement le surdébit de communication, accélérant le calcul de graphe distribué et les vitesses d'analyse.
PCT/CN2020/090238 2020-04-16 2020-05-14 Procédé de calcul de graphe de type distribué, terminal, système, et support de stockage WO2021208174A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010298360.XA CN111581443B (zh) 2020-04-16 2020-04-16 分布式图计算方法、终端、系统及存储介质
CN202010298360.X 2020-04-16

Publications (1)

Publication Number Publication Date
WO2021208174A1 true WO2021208174A1 (fr) 2021-10-21

Family

ID=72122402

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/090238 WO2021208174A1 (fr) 2020-04-16 2020-05-14 Procédé de calcul de graphe de type distribué, terminal, système, et support de stockage

Country Status (2)

Country Link
CN (1) CN111581443B (fr)
WO (1) WO2021208174A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113326125B (zh) * 2021-05-20 2023-03-24 清华大学 大规模分布式图计算端到端加速方法及装置
CN117251380B (zh) * 2023-11-10 2024-03-19 中国人民解放军国防科技大学 一种面向单调流式图的优先级异步调度方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103631878A (zh) * 2013-11-08 2014-03-12 中国科学院深圳先进技术研究院 一种大规模图结构数据处理方法、装置和系统
CN106250563A (zh) * 2016-08-30 2016-12-21 江苏名通信息科技有限公司 基于GPS平台的k‑bisimulation计算算法
WO2017076296A1 (fr) * 2015-11-03 2017-05-11 华为技术有限公司 Procédé et dispositif de traitement de données de graphique
CN106777351A (zh) * 2017-01-17 2017-05-31 中国人民解放军国防科学技术大学 基于art树分布式系统图存储计算系统及其方法
WO2018137346A1 (fr) * 2017-01-26 2018-08-02 华为技术有限公司 Procédé et appareil de traitement de données graphiques
CN110619595A (zh) * 2019-09-17 2019-12-27 华中科技大学 一种基于多fpga加速器互联的图计算优化方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102411679B (zh) * 2010-09-26 2014-04-16 中国科学院计算技术研究所 一种蛋白质鉴定的大规模分布式并行加速方法及其系统
CN102411666B (zh) * 2010-09-26 2014-04-16 中国科学院计算技术研究所 一种蛋白质鉴定的大规模分布式并行加速方法及其系统
CN108683738B (zh) * 2018-05-16 2020-08-14 腾讯科技（深圳）有限公司 图数据处理方法和图数据的计算任务发布方法
CN108804226B (zh) * 2018-05-28 2021-09-03 中国人民解放军国防科技大学 一种用于分布式图计算的图分割划分方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103631878A (zh) * 2013-11-08 2014-03-12 中国科学院深圳先进技术研究院 一种大规模图结构数据处理方法、装置和系统
WO2017076296A1 (fr) * 2015-11-03 2017-05-11 华为技术有限公司 Procédé et dispositif de traitement de données de graphique
CN106250563A (zh) * 2016-08-30 2016-12-21 江苏名通信息科技有限公司 基于GPS平台的k‑bisimulation计算算法
CN106777351A (zh) * 2017-01-17 2017-05-31 中国人民解放军国防科学技术大学 基于art树分布式系统图存储计算系统及其方法
WO2018137346A1 (fr) * 2017-01-26 2018-08-02 华为技术有限公司 Procédé et appareil de traitement de données graphiques
CN110619595A (zh) * 2019-09-17 2019-12-27 华中科技大学 一种基于多fpga加速器互联的图计算优化方法

Also Published As

Publication number Publication date
CN111581443A (zh) 2020-08-25
CN111581443B (zh) 2023-05-30

Similar Documents

Publication Publication Date Title
US10997176B2 (en) Massive time series correlation similarity computation
US8959138B2 (en) Distributed data scalable adaptive map-reduce framework
CN102307206B (zh) 基于云存储的快速访问虚拟机镜像的缓存系统的缓存方法
WO2021208174A1 (fr) Procédé de calcul de graphe de type distribué, terminal, système, et support de stockage
EP4198771A1 (fr) Procédé et appareil de traitement de données, support lisible par ordinateur et dispositif électronique
CN111815738B (zh) 一种构建地图的方法和装置
US20170139913A1 (en) Method and system for data assignment in a distributed system
CN110659278A (zh) 基于cpu-gpu异构架构的图数据分布式处理系统
CN107341210B (zh) Hadoop平台下的C-DBSCAN-K聚类算法
US10162830B2 (en) Systems and methods for dynamic partitioning in distributed environments
CN106599190A (zh) 基于云计算的动态Skyline查询方法
CN108052535B (zh) 基于多处理器平台的视觉特征并行快速匹配方法和系统
US20210365300A9 (en) Systems and methods for dynamic partitioning in distributed environments
CN106599189A (zh) 基于云计算的动态Skyline查询装置
CN106202152B (zh) 一种云平台的数据处理方法及系统
KR20160081231A (ko) 이미지 기반 검색을 위한 맵리듀스 기반의 이미지 특징 추출 방법 및 시스템
EP4012573A1 (fr) Procédé et appareil de reconstruction de graphe
CN109254844B (zh) 一种大规模图的三角形计算方法
Adam et al. Big data management and analysis
WO2023083241A1 (fr) Division de données de graphe
CN110309367B (zh) 信息分类的方法、信息处理的方法和装置
Hao et al. An association-oriented partitioning approach for streaming graph query
US11442792B2 (en) Systems and methods for dynamic partitioning in distributed environments
Khan et al. BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU
CN104991912A (zh) 一种基于MapReduce架构的大规模图数据聚类算法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20931149

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20931149

Country of ref document: EP

Kind code of ref document: A1