Troubleshooting imperative programs at runtime is very challenging because the final optimized plan is quite different from the original design time model. In this demonstration, we present ProcAnalyzer, an expressive and intuitive tool for troubleshooting issues related to performance, code quality, and security. We propose end-to-end graph (E2EGraph) that provides a holistic view of design time, compile time, and runtime behavior so that end users and engine developers easily find the correlations between design time and runtime. ProcAnalyzer provides suggestions and visualization to find problematic statements through the E2EGraph.

Hybrid transactional and analytical processing (HTAP) systems like SAP HANA make it much simpler to manage both operational load and analytical queries without ETL, separate data warehouses, et al. To represent both transactional and analytical business logic in a single database system, stored procedures are often used to express analytical queries using control flow logic and DMLs. Optimizing these complex procedures requires a fair knowledge of imperative programming languages as well as the declarative query language. Therefore, unified optimization techniques considering both program and query optimization techniques are essential for achieving optimal query performance. In this paper, we propose a novel unified optimization technique for efficient iterative query processing. We present a notion of query motion that allows the movement of SQL queries in and out of a loop. Additionally, we exploit a new cost model that measures the quality of the execution plan with consideration for queries and loop iterations. We describe our experimental evaluation that demonstrates the benefit of our technique using both a standard decision support benchmark and real-world workloads. An extensive evaluation shows that our unified optimization technique enumerates plans that achieve performance improvements of up to an order of magnitude faster than plans generated by the existing loop-invariant code motion technique.

Graph classification is an important topic in graph mining research since it has many applications, such as, social web mining, function prediction of molecules for drug design, XML document classification and anomaly detection in program flows. The key difficulty in graph classification lies in selecting a subset of optimal features from a huge number of structural features. The features need to be highly discriminative and small in numbers for better classification accuracy and running time. In this paper, we propose a novel feature selection framework that selects an optimal feature subset by removing redundant subgraphs. Topologically similar subgraphs have similar discriminative powers and coverage. We cluster these subgraphs and select one subgraph as a feature. We also propose an efficient topological similarity-based clustering method that guarantees the efficiency of our framework. Empirical results show that the proposed framework achieves significantly improved classification accuracy and running time in comparison with the state-of-the-art methods.