PandaDB: Understanding Unstructured Data in Graph Database

07/05/2021
by   Zihao Zhao, et al.
0

At present, graph model is widely used in many applications, such as knowledge graph, financial anti-fraud. Unstructured data(such as images, videos, and audios) is under explosive growing. So, queries of unstructured data content on graph are widespread in a rich vein of real-world applications. Many graph database systems have started to support unstructured data to meet such demands. However, queries over structured and unstructured data on graph are often treated as separate tasks in most systems. These tasks are executed on different module of the tools chain. Collaborative queries (i.e., involving both data types) are not yet fully supported.This paper proposes a graph database supporting collaborative queries on property graph, named PandaDB. Its to fulfill the emerging demands about querying unstructured data on property graph model. PandaDB introduces CypherPlus, a query language which enables the users to express collaborative queries using cypher semantics by introducing sub-property and a series of logical operators. PandaDB is built based on Neo4j, manage the unstructured data in the format of BLOB. The computable pattern is proposed to introduce the content of unstructured data into computation. Moreover, to support the large-scale query, this paper proposes the semantic index, cache and index the extracted computable pattern. The collaborative query on graph is optimized by the min-cost optimization method. Experimental results on both public and in-house datasets show the performance achieved by PandaDB and its effectiveness.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/02/2023

S2CTrans: Building a bridge from SPARQL to Cypher

In graph data applications, data is primarily maintained using two model...
research
02/10/2020

RDFFrames: Knowledge Graph Access for Machine Learning Tools

Knowledge graphs represented as RDF datasets are becoming increasingly p...
research
10/22/2018

Fast Dual Simulation Processing of Graph Database Queries (Supplement)

Graph database query languages feature expressive, yet computationally e...
research
09/11/2022

SymphonyDB: A Polyglot Model for Knowledge Graph Query Processing

Unlocking the full potential of Knowledge Graphs (KGs) to enable or enha...
research
08/20/2022

gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus

We design a user-friendly and scalable knowledge graph construction (KGC...
research
03/25/2022

Navigable Proximity Graph-Driven Native Hybrid Queries with Structured and Unstructured Constraints

As research interest surges, vector similarity search is applied in mult...
research
09/09/2020

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data

Unstructured data is now commonly queried by using target deep neural ne...

Please sign up or login with your details

Forgot password? Click here to reset