Relation-Aware Graph Transformer for SQL-to-Text Generation

Generating natural language descriptions for structured representation (e.g., a graph) is an important yet challenging task. In this work, we focus on SQL-to-text, a task that maps a SQL query into the corresponding natural language question. Previous work represents SQL as a sparse graph and utilizes a graph-to-sequence model to generate questions, where each node can only communicate with k-hop nodes. Such a model will degenerate when adapted to more complex SQL queries due to the inability to capture long-term and the lack of SQL-specific relations. To tackle this problem, we propose a relation-aware graph transformer (RGT) to consider both the SQL structure and various relations simultaneously. Specifically, an abstract SQL syntax tree is constructed for each SQL to provide the underlying relations. We also customized self-attention and cross-attention strategies to encode the relations in the SQL tree. Experiments on benchmarks WikiSQL and Spider demonstrate that our approach yields improvements over strong baselines.

Teknik Uji Penetrasi Web Server Menggunakan SQL Injection dengan SQLmap di Kalilinux

<p><em>In recent years cases of cyber attacks that lead to website security have increased. The most widely used website hacking threat is sql injection. By using the sqlmap tool that runs on the Kalilinux operating system, attackers can easily take over very important user authentication data with their passwords. Attackers only use a special SQL query script using the python programming language will force the web server to output database information, tables, columns and data contents. This sql injection technique is not difficult, knowing how sql injection works is expected to be useful for web admins and web application developers to be able to secure user access from attackers. This attack simulation uses a virtual machine, by creating two virtual computers that are scripted as the attacker and the target server. By testing through this simulation, we can find out how the attack process and the consequences of attacks carried out by attackers.</em></p>

Random Forest Bagging and X-Means Clustered Antipattern Detection from SQL Query Log for Accessing Secure Mobile Data

In the current ongoing crisis, people mostly rely on mobile phones for all the activities, but query analysis and mobile data security are major issues. Several research works have been made on efficient detection of antipatterns for minimizing the complexity of query analysis. However, more focus needs to be given to the accuracy aspect. In addition, for grouping similar antipatterns, a clustering process was performed to eradicate the design errors. To address the above-said issues and further enhance the antipattern detection accuracy with minimum time and false positive rate, in this work, Random Forest Bagging X-means SQL Query Clustering (RFBXSQLQC) technique is proposed. Different patterns or queries are initially gathered from the input SQL query log, and bootstrap samples are created. Then, for each pattern, various weak clusters are constructed via X-means clustering and are utilized as the weak learner (clusters). During this process, the input patterns are categorized into different clusters. Using the Bayesian information criterion, the similarity measure is employed to evaluate the similarity between the patterns and cluster weight. Based on the similarity value, patterns are assigned to either relevant or irrelevant groups. The weak learner results are aggregated to form strong clusters, and, with the aid of voting, a majority vote is considered for designing strong clusters with minimum time. Experiments are conducted to evaluate the performance of the RFBXSQLQC technique using the IIT Bombay dataset using the metrics like antipattern detection accuracy, time complexity, false-positive rate, and computational overhead with respect to the differing number of queries. The results revealed that the RFBXSQLQC technique outperforms the existing algorithms by 19% with pattern detection accuracy, 34% minimized time complexity, 64% false-positive rate, and 31% in terms of computational overhead.

A learned query rewrite system using Monte Carlo tree search

Query rewrite transforms a SQL query into an equivalent one but with higher performance. However, SQL rewrite is an NP-hard problem, and existing approaches adopt heuristics to rewrite the queries. These heuristics have two main limitations. First, the order of applying different rewrite rules significantly affects the query performance. However, the search space of all possible rewrite orders grows exponentially with the number of query operators and rules and it is rather hard to find the optimal rewrite order. Existing methods apply a pre-defined order to rewrite queries and will fall in a local optimum. Second, different rewrite rules have different benefits for different queries. Existing methods work on single plans but cannot effectively estimate the benefits of rewriting a query. To address these challenges, we propose a policy tree based query rewrite framework, where the root is the input query and each node is a rewritten query from its parent. We aim to explore the tree nodes in the policy tree to find the optimal rewrite query. We propose to use Monte Carlo Tree Search to explore the policy tree, which navigates the policy tree to efficiently get the optimal node. Moreover, we propose a learning-based model to estimate the expected performance improvement of each rewritten query, which guides the tree search more accurately. We also propose a parallel algorithm that can explore the tree search in parallel in order to improve the performance. Experimental results showed that our method significantly outperformed existing approaches.

Performance Evaluation of Policy-Based SQL Query Classification for Data-Privacy Compliance

Data science must respect privacy in many situations. We have built a query repository with automatic SQL query classification according to data-privacy directives. It can intercept queries that violate the directives, since a JDBC proxy driver inserted between the end-users’ SQL tooling and the target data consults the repository for the compliance of each query. Still, this slows down query processing. This paper presents two optimizations implemented to increase classification performance and describes a measurement environment that allows quantifying the induced performance overhead. We present measurement results and show that our optimized implementation significantly reduces classification latency. The query metadata (QM) is stored in both relational and graph-based databases. Whereas query classification can be done in a few ms on average using relational QM, a graph-based classification is orders of magnitude more expensive at 137 ms on average. However, the graphs contain more precise information, and thus in some cases the final decision requires to check them, too. Our optimizations considerably reduce the number of graph-based classifications and, thus, decrease the latency to 0.35 ms in $$87\%$$ 87 % of the classification cases.