Graph-structured databases have a wide range of emerging applications, e.g., the Semantic Web, eXtensible Markup Language (XML), biological databases and network topologies. To-date, there has already been voluminous real-world (possibly cyclic and schemaless) graph-structured data. Therefore, data engineering in graph-structured databases has recently received a lot of attention, where there are limitations as well as scope for significant developments. In these databases, there exist many different indexes and different query languages, e.g., XQuery, regular expressions, Web Ontology Langauge and subgraph isomorphism, while there are few graphical user interfaces for effectively querying subgraphs. In this paper, we examine and evaluate the current state of-the-art in graph-structured databases with respect to (i) query languages, (ii) dynamic aspects, (iii) data mining, (iv) graphical user interfaces, and (v) modern computer architecture on graph-structured data. In addition, the incremental maintenance of graph indexes/views will be addressed.