PandaDB: Understanding Unstructured Data in Graph Database
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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.
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