Raw Query Research Articles

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywords— Docker, Node-Bench method, Node.js, Podman, VirtualBox.

Object-Agnostic Vision Measurement Framework Based on One-Shot Learning and Behavior Tree.

Vision measurement is important for intelligent systems to obtain the precise structural and spatial information of objects. Beyond the object-specific vision measurement developed for fixed object type, it is appealing to explore the object-agnostic vision measurement, which can be efficiently reconfigured and adapted to various novel objects. This article proposes a framework to mimic the human's versatile visual measurement behavior: extract a set of contour primitives of interest (CPIs) from an image, then utilize the CPIs to calculate the key geometric information. First, a deep convolutional neural network (CNN) CPieNet + is proposed under the one-shot learning scheme, aiming to extract the pixel-level object CPI from a raw query image, given an annotated support image. The fine-grained CPI prototypes are formed by sampling multiple points on the feature map of the support image. To leverage the explicit geometric knowledge in the CNN inference, the annotation map is encoded as a shape descriptor to guide the feature channel attention, and the geometric attribute awareness is realized by supervising the model to predict the direction and size of CPI. Second, the measurement behavior tree (BT) is designed to model the hierarchical geometric calculation procedure, which is flexibly configurable for different measurement requirements and is interpretable for nonexpert users. After the execution of the measurement BT, the pixel-level CPIs are converted to the required key geometric data. The effectiveness of the proposed methods is validated by a series of experiments.

Interactive Character Path-Following Using Long-Horizon Motion Matching With Revised Future Queries

We propose a method for interactively generating a character motion that follows a user-specified path based on motion matching. Unlike basic motion matching that finds the best frame considering only the current state and current control input, our long-horizon motion matching algorithm is performed recursively over multiple levels and finds the matching path that minimizes the total distance, to find the best frame for a longer future interval. The matching query is carefully revised from the raw query directly extracted from the user path to address the following aspects: inherent differences between an actual moving path of a human and a user-drawn path, and possible user errors such as a path drawn with excessive speed or with abrupt changes in direction. Also, two interactive control modes for path-following characters are proposed. The combination of our long-horizon motion matching and carefully revised queries enables smooth and reliable interactive path tracking for a variety of paths. We show the effectiveness of our method using various experiments such as path-following with different shaped paths, game-like interactive control demos, and ablation studies.

Long short‐term memory on abstract syntax tree for SQL injection detection

SQL injection attack (SQLIA) is a code injection technique, used to attack data-driven applications by executing malicious SQL statements. Techniques like pattern matching, software testing and grammar analysis etc. are frequently used to prevent such attack. However, major bottlenecks still remain in detecting SQLIA with bypassing techniques, getting access to source code and requiring an additional manual operation to extract features. The authors propose a novel detection approach based on long short-term memory and abstract syntax tree, which could detect SQLIAs from the raw query strings and work under SQL detection bypassing scenario. Our deep learning technique explicitly uses both context and syntax information that previous methods failed to fully grasp. Experimental results clearly illustrate the superior performance of our method compared to other existing works when detecting with complete SQL raw queries.

Tabula in action

In this paper, we demonstrate Tabula, a middleware that sits between the data system and the geospatial visualization dashboard to increase user interactivity. The proposed system adopts a sampling cube approach that stores prematerialized spatial samples and allows data scientists to define their own accuracy loss function such that the produced samples can be used for various user-defined visualization tasks. The system ensures that the difference between the sample fed into the dashboard and the raw query answer never exceeds the user-specified loss threshold. For demonstration purposes, we connect Apache Zeppelin, a visualization dashboard, to the system and show how Tabula accelerates interactive visualizations on NYC Taxi Trip data, Yelp review data and San Diego Smart Streetlights data.

Preventing SQL Injection Attacks by Automatic Parameterizing Raw Queries Using Lexical and Semantic Analysis Methods

SQL injection (SQLI) is one of the most important security threats against web applications. Many tech-niques have been proposed for counteracting SQLI attacks; however, second-order attacks and the injectionattacks that are raising data-type mismatch errors have been ignored in most of them. In this paper, wepropose a new anomaly-based method (deploying as a proxy between the application server and its databaseserver) for detection and/or prevention of SQLI attacks without requiring any modi cation to the sourcecode of vulnerable applications. The majority of attacks, which lead to a change in the syntax of applica-tion queries, are identi ed in the detection phase by lexical analysis of the queries. The remained types ofattacks, such as second-order attacks and attacks generating data type mismatch errors, are prevented tobe executed in the prevention phase, where each query is automatically converted to a parameterized query(before submitting to its database) using a semantic analysis method.

WideTable

This paper presents a technique called WideTable that aims to improve the speed of analytical data processing systems. A WideTable is built by denormalizing the database, and then converting complex queries into simple scans on the underlying (wide) table. To avoid the pitfalls associated with denormalization, e.g. space overheads, WideTable uses a combination of techniques including dictionary encoding and columnar storage. When denormalizing the data, WideTable uses outer joins to ensure that queries on tables in the schema graph, which are now nested as embedded tables in the WideTable, are processed correctly. Then, using a packed code scan technique, even complex queries on the original database can be answered by using simple scans on the WideTable(s). We experimentally evaluate our methods in a main memory setting using the queries in TPC-H, and demonstrate the effectiveness of our methods, both in terms of raw query performance and scalability when running on many-core machines.

A Conceptual Model of an Intelligent Catalog Search System

In this article, we review the role of electronic catalogs in developing electronic commerce applications within the context of consumer buying behavior models. These models view the customer buying experience as a stagewise process, starting with need identification and proceeding through product and merchant brokering to negotiation, purchase, and service. Electronic product catalogs can be used in all of the just mentioned stages; however, their most common use is in the product and merchant brokering stages. From a customer's perspective, however, the variety and heterogeneity of electronic catalogs create a major problem in integrating information from various sources. There have been several attempts to overcome this problem using architectures and protocols that enable interoperabilty of electronic catalogs using software-agent technology. The extant architectures and protocols emphasize semantic integration of different catalogs as a mean for achieving interoperability. In this article, we expand previous work on this topic and propose a comprehensive agent-based conceptual model of an intelligent catalog search system (ICSS) [1]. The ICSS incorporates knowledge to analyze, enhance, filter, and synthesize a raw query and replies provided by various types of electronic catalogs and intercatalog brokering systems. The main benefit of the proposed architecture is an increase in the ability of a customer to locate, compare, and evaluate relevant product offerings in a network of heterogeneous electronic product catalogs.