What is the translation of " GRAPH DATABASES " in Chinese?

• 图形数据库
• 的图数据库
• 图数据库
• 图表数据库

Examples of using Graph databases in English and their translations into Chinese

Popular graph databases include Neo4j and OrientDB.
常见的图数据库有Neo4J、OrientDB等。

Select the best framework for a given task(from graph databases to batch processing frameworks.).
为给定任务选择最佳框架（从图形数据库到批处理框架）.

Graph databases have found increasing use in web applications.
图表数据库已经越来越多地在Web应用程序中使用。

Facebook and LinkedIn use graph databases for social applications.
Facebook和LinkedIn都对社交应用使用了图数据库。

Also, graph databases don't scale out well in NoSQL databases..
此外，图形数据库在NoSQL数据库中无法很好地扩展。

Besides specifics around storage and processing, graph databases also adopt distinct data models.
除了存储和处理方面的差异，图数据库还采用了不同的数据模型。

Widely used graph databases include Neo4j, OrientDB, and Titan.
常见的图数据库有Neo4J、OrientDB等。

The bioinformatics research community has largely adopted graph databases and the SPARQL query language.
生物信息学研究界基本上采用了图形数据库和SPARQL查询语言。

Conversely, in graph databases, relationships are conveyed as concrete entities.
反过来说，在图形数据库中，关系被表达成具体实体。

A truly sophisticated, effective analytics environment will include both relational and graph databases.
一个真正成熟、有效的分析环境应该包括关系型数据库和图数据库的。

Document databases and graph databases can be consistent or eventually consistent.
文檔数据库和图形数据库可以是一致的或最终一致的。

Analysts expect technologies like in-memory and persistent memory databases, data fabric and graph databases to become more important in the coming year.
分析师预计，内存和持久性内存数据库、数据结构和图形数据库等技术在来年将变得越来越重要。

Graph databases are technologies that are translations of the relational OLTP databases..
图数据库是转换关系OLTP数据库的技术。

Gremlin works for both OLTP-based graph databases as well as OLAP-based graph processors.
Gremlin适用于基于OLTP的图数据库以及基于OLAP的图处理器。

In graph databases, relations between two entities are more important than entities themselves.
在图形数据库中，两个实体之间的关系比实体本身更重要。

Social network relations are graphs by nature, and graph databases such as Neo4J make operations on them simpler and more efficient.
社交网络关系本质上是图形化的，用图形数据库（如Neo4j）对数据进行操作会更简单、更高效。

Graph databases, like Amazon Neptune, are purpose-built to store and navigate relationships.
AmazonNeptune等图形数据库专门用于存储和导航关系。

Neo4j is one of the oldest and most mature graph databases, but still suffers from scalability issues since it doesn't support sharding yet.
Neo4j算是最老牌、最成熟的图数据库之一，但因不支持分片而依然存在可伸缩性的问题。

Graph databases aren't particularly well-suited for frequently changing data and real-time updates across large amounts of data.
图型数据库不适合用于数据经常改变，常发生大规模数据之间实时更新的情况。

For very large collections of diverse, unstructured information, graph databases have emerged as a technology to help collect, manage, and search large sets of data.
对于海量的各种非结构化信息来说，图数据库已经成为帮助收集、管理和搜索大量数据的技术。

Graph databases are related to Document databases because many implementations allow one model value as a map or document.
图形数据库和文件数据库息息相关，因为许多实现允许建模的值是映射或者文档。

With data relationships at their center, graph databases are incredibly efficient when it comes to query speeds, even for deep and complex queries.
在数据关系中心，图形数据库在查询速度方面非常高效，即使对于深度和复杂的查询也是如此。

Graph databases are obviously a perfect solution for this area, but actually most of NoSQL solutions are surprisingly strong for such problems.
图形数据库显然是这个领域的完美解决方案，但实际上大多数的NoSQL也都非常善于解决这样的问题。

Unlike traditional graph databases, TigerGraph can scale real-time multi-hop queries to trillions of relationships.
不同于传统的图数据库，TigerGraph可以将实时多跳查询扩展到数万亿个关联。

Graph databases are built for use with transactional(OLTP) systems and are engineered with transactional integrity and operational availability in mind.
图形数据库是为与事务（OLTP）系统一起使用而构建的，并且在设计时考虑了事务完整性和操作可用性。

Key-Value Stores and Graph Databases typically do not place constraints on values, so values can be comprised of arbitrary format.
键值存储和图形数据库通常不对值进行约束，所以值可能是任意格式。

Graph databases are useful when traversing relationships are core to the application such as navigating social network connections, network topologies, or supply chains.
应用：图表数据库适用于遍历关系是应用程序核心的情况，例如导航社交网络连接、网络拓扑或供应链。

These properties make graph databases naturally suited to types of searches that are increasingly common in online systems, and in big data environments.
图形数据库自然适用于在线系统和大数据环境中日益普遍的搜索类型。

Relational database, graph databases, time-series databases, HDFS, and object stores all have their respective strengths and weakness.
关系数据库、图形数据库、时间序列数据库、HDF和对象存储都有各自的优缺点。

Applications: Graph databases are useful in cases where traversing relationships are core to the application, like navigating social network connections, network topologies or supply chains.
应用：图表数据库适用于遍历关系是应用程序核心的情况，例如导航社交网络连接、网络拓扑或供应链。