Graph database systems (GDBS) have become popular for representing real-world entities and their relationships, and offering convenient query languages based on graph pattern matching. As graphs increase in size and complexity, GDBS need to provide the appropriate support for abstraction for which views have demonstrated to be an effective tool, facilitating query writing and improving query execution time via materialization techniques. This paper explores how views can be defined and used in GDBS. We propose view-based extensions to the widely used graph query language Cypher, explore a wide range of possible view types, and outline several implementation strategies for view materialization. Using a set of micro- and macro-benchmarks, we provide insight into how expressive different view types are and how effective the proposed implementation strategies are for different GDBS. Our results show that views can be a powerful tool for GDBS, offering great flexibility in query expression and providing performance improvements if materialized.

Automatic identification of the service type used by network flows (e.g., HTTP and MySQL) is an essential part of many cloud management and monitoring tasks for quality of service, security monitoring, resource allocation, etc. Several studies have adapted deep learning models for accurate service type identification of network traffic. These models vary in how the message flow data is used and what datasets are considered. There are no published guidelines on selecting the best approach for automating the service identification process. In this paper, we opt to fill such a technical gap and provide a detailed study of the trade-offs of different deep-learning based approaches for service type identification of network traffic. Towards this end, we generate flow-based datasets for a wide range of service types that are commonly deployed in the cloud. We consider two different deep learning models that have shown promising results in this context, and show their performance for both payload- and header-based datasets, considering fundamental parameters such as dynamic service port configuration, flow direction and the packet order in the flow stream.