PANDADB: A Distributed Graph Database System to Query Unstructured Data in Big Graph

Unstructured data, such as images and videos, are growing stringently, and the unprecedented growth of interconnected unstructured data could be viewed as graphs. Thus, the node properties of Graph would be the unstructured data, and querying such data in a graph is widespread in different real-world applications. Some systems and techniques are proposed to query unstructured data in a graph to meet such demand. However, current query unstructured data in a graph is executed by various tasks in different systems and loses the possibility to optimize such queries in an engine. In this work, we build a graph database namely PandaDB to support querying unstructured data in the graph. We at first introduce CypherPlus, a query language to enable users to express complex graph queries for understanding the semantic of unstructured data. Next, we develop a cost model and related query optimization techniques to speed up the unstructured data processing. In addition, we optimize the data storage and index to speed up the query processing in a distributed setting. The implementation of PandaDB is open source based on Neo4j. The results show PandaDB can support a large scale of unstructured data query processing in a graph e.g., more than a billion unstructured data items. We would present the best of practice while deploying the system into real applications.