JSON Objects Research Articles

The Application of AJAX and JSON in the Case

In modern Web development, it is very important to master and utilize AJAX and JSON technology to achieve high-efficiency development. This paper gives an overview of some examples to analyze how to use Bootstrap to display data on the front-end page, then send an asynchronous request to the background with Ajax, and after receiving the request, parse the JSON string to perform the corresponding database operation and obtain the required data from the database. the data package to be returned is then packaged as a JSON object array and returned to the front end of the web, the front end of the data package is analyzed to the desired data and the front end of the webpage is not refreshed, And then the whole interaction process is completed.

Machine learning and evolutionary optimization approach for solving the flexible job-shop scheduling problem

This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP.

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

The study of performance and methods of storage of JSON objects in PostgreSQL database management system

Currently there are several methods of data storage, such as table and object, as JSON objects. National and foreign scientists conducted studies on the comparison of performance of relational database management systems and NoSQL database management system, but there were no studies that compared the performance in data insertion and storage using ordinary records and JSON objects within the relational database management system. An experiment was carried out in terms of the real examples of corporate use of the database management system; the results show the relevance of the use of JSON objects in storage of large volumes of data.

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time-Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity.

The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors' enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration.

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Обнаружение аномалий в составных объектах в формате JSON

In this paper, we address the problem of intrusion detection for modern web applications and mobile applications with the cloud-based server side, using malicious content detection in JSON data, which is currently one of the most popular data serialization and exchange formats between client and server parts of an application. We propose a method for building a JSON model for the given set of JSON objects capable of detection of structure and type anomalies. The model is based on the models for basic data types inside JSON collection objects and schema model that generalizes objects’ structure in the collection. We performed experiments using modifications of objects’ structures and insertions of code injection attack vectors such as SQL injections, OS command injections, and JavaScript/HTML injections. The analysis showed statistical significance between the model’s predictions and the presence of anomalies in the data gathered from the real web applications’ traffic. The quality of the model’s predictions was measured using the Matthews correlation coefficient (MCC). The MCC values computed on the data were close to one which indicates the model’s high efficiency in solving the problem of anomaly detection in JSON objects.

An Improved Blockchain Technique for Secure Land Registration Data Records

The land is a very valuable asset for any government. It’s government job to ensure that the land registration and transfer are done without any fraud, good speed and transparency. The current land registration method employed by the governments are not open to frauds, hacks, and corruption of land records. Fraud is one of the major problems in land registration methods. In this study, the goal is to develop the framework by incorporating the blockchain technique that secures the land data during the land registration and transfer phases by preventing the fraud. The use of blockchain gives us the transparent, decentralized and robust infrastructure to build our framework upon. The blockchain technology is implemented with the asymmetric keys encryption/decryption that securely stores the land registration/transfer data. The data is held using encrypting with the public key of the landowner and storing a hash of the data. The use of the cryptographic function of hashing using SHA. The comparison of using SHA 256 and SHA 512 is given and discussed. The dataset used to compare results is created using 200 records of JSON objects with each object being identical for both SHA256 and SHA512 to remove data bias. The proposed framework with the SHA 512 performed 29% faster than the SHA 256. The results indicate our proposed framework performing better than the works proposed in current research land registration techniques.

XERIS/APEX

Modern day cloud native applications have become broadly representative of distributed systems in the wild. However, unlike traditional distributed system models with conceptually static designs, cloud-native systems emphasize dynamic scaling and on-line iteration (CI/CD). Cloud-native systems tend to be architected around a networked collection of distinct programs ("microservices") that can be added, removed, and updated in real-time. Typically, distinct containerized programs constitute individual microservices that then communicate among the larger distributed application through heavy-weight protocols. Common communication stacks exchange JSON or XML objects over HTTP, via TCP/TLS, and incur significant overhead, particularly when using small size message sizes. Additionally, interpreted/JIT/VM-based languages such as Javascript (NodeJS/Deno), Java, and Python are dominant in modern microservice programs. These language technologies, along with the high-overhead messaging, can impose superlinear cost increases (hardware demands) on scale-out, particularly towards hyperscale and/or with latency-sensitive workloads.

Recent Trends in JSON Filters

JavaScript Object Notation is a text-based data exchange format for structuring data between a server and web application on the client-side. It is basically a data format, so it is not limited to Ajax-style web applications and can be used with API’s to exchange or store information. However, the whole data never to be used by the system or application, It needs some extract of a piece of requirement that may vary person to person and with the changing of time. The searching and filtration from the JSON string are very typical so most of the studies give only basics operation to query the data from the JSON object. The aim of this paper to find out all the methods with different technology to search and filter with JSON data. It explains the extensive results of previous research on the JSONiq Flwor expression and compares it with the json-query module of npm to extract information from JSON. This research has the intention of achieving the data from JSON with some advanced operators with the help of a prototype in json-query package of NodeJS. Thus, the data can be filtered out more efficiently and accurately without the need for any other programming language dependency. The main objective is to filter the JSON data the same as the SQL language query.

A Flexible IoT Stream Processing Architecture Based on Microservices

The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework.

Rumble

This paper introduces Rumble, a query execution engine for large, heterogeneous, and nested collections of JSON objects built on top of Apache Spark. While data sets of this type are more and more wide-spread, most existing tools are built around a tabular data model, creating an impedance mismatch for both the engine and the query interface. In contrast, Rumble uses JSONiq, a standardized language specifically designed for querying JSON documents. The key challenge in the design and implementation of Rumble is mapping the recursive structure of JSON documents and JSONiq queries onto Spark's execution primitives based on tabular data frames. Our solution is to translate a JSONiq expression into a tree of iterators that dynamically switch between local and distributed execution modes depending on the nesting level. By overcoming the impedance mismatch in the engine , Rumble frees the user from solving the same problem for every single query, thus increasing their productivity considerably. As we show in extensive experiments, Rumble is able to scale to large and complex data sets in the terabyte range with a similar or better performance than other engines. The results also illustrate that Codd's concept of data independence makes as much sense for heterogeneous, nested data sets as it does on highly structured tables.

NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries.

Natural language interfaces (NLls) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design. We present NL4DV, a toolkit for natural language-driven data visualization. NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset. In response, the toolkit returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks, and a list of Vega-Lite specifications relevant to the input query. In doing so, NL4DV aids visualization developers who may not have a background in NLP, enabling them to create new visualization NLIs or incorporate natural language input within their existing systems. We demonstrate NL4DV's usage and capabilities through four examples: 1) rendering visualizations using natural language in a Jupyter notebook, 2) developing a NLI to specify and edit Vega-Lite charts, 3) recreating data ambiguity widgets from the DataTone system, and 4) incorporating speech input to create a multimodal visualization system.

Data Structure Comparison Between MySql Relational Database and Firebase Database NoSql on Mobile Based Tourist Tracking Application

Tracking application is widely used to help outdoor activities. One of them is used to help tourists who do tourist destinations in group activities. Tourist tracking application requires data on the position of tourists on earth. This coordinate data can be obtained using latitude and longitude data from GPS (Global Positioning System). Coordinate latitude and longitude position of tourists from time to time are stored in a database. Database Selection for media storage will determines the level of speed and ease in accessing data. There are some differences in designing the structure of the MySql and Firebase databases. Relational Database begins with identification of entities involved in the system. One entity with another entity is related and has a cardinality level of the relationship. Relational database implementation in the form of tables in normal forms that are interconnected. Data will be stored in columns and rows. Normalization of tables causes high query computation when calling data that involves many tables. Firebase, which is one of the NoSql databases, in building data structures is stored in JSON objects. JSON tree structures usually consider the display of information that will be presented to users. So that the generated JSON tree has high efficiency and speed in processing data. In this study a comparison between the design of the relational database structure, namely MySql and NoSql Firebase database structure was developed. Firebase is more power full for the development of applications which update data in realtime and and handle a lot of coordinate data such as tourist tracking applications. Applications that do not have complex data relations such as tourist tracking applications are more suitable to use firebase than using MySQL.

Energy-Efficient Transaction Serialization for IoT Devices

This article presents two designs, the Transaction Serial Format (TSF) and the Transaction Array Model (TAM). Together, they provide full, efficient, transaction serialization facilities for devices with limited onboard energy, such as those in an Internet of Things (IoT) network. TSF provides a compact, non-parsed, format that requires minimal processing for transaction deserialization. TAM provides an internal data structure that needs minimal dynamic storage and directly uses the elements of TSF. The simple lexical units of TSF do not require parsing. The lexical units contain enough information to allocate the internal TAM data structure efficiently. TSF generality is equivalent to XML and JSON. TSF represents any XML document or JSON object without loss of information, including whitespace. The XML equivalence provides a foundation for the performance comparisons. A performance comparison of a C reference implementation of TSF and TAM to the popular Expat XML library, also written in C, shows that TSF reduces deserialization processor time by more than 80%.

Використання чат-бота @es_economy_karkas_bot для онлайн консультації з експертною системою

Чат-бот (співрозмовник) – це програма, яка імітує людське спілкування на основі елементів штучного інтелекту. У статті представлені результати інтегрування чат-бота @es_economy_karkas_bot з експертною системою для організації консультування в режимі онлайн. Дано опис архітектури і реалізація імплементації чат-бота месенджера телеграм в експертну систему на базі системи “КАРКАС” – інструментальний засіб для побудови моделей баз знань. Розглянута структура і алгоритм взаємодії чат-бота і агентів експертної системи в онлайн режимі. Виконано аналіз можливостей створення чат-ботів в месенджері ТЕЛЕГРАМ, їх інтеграцію з експертами системи в сфері економіки.

Effective and efficient retrieval of structured entities

Structured entities are commonly abstracted, such as from XML, RDF or hidden-web databases. Direct retrieval of various structured entities is highly demanded in data lakes, e.g., given a JSON object, to find the XML entities that denote the same real-world object. Existing approaches on evaluating structured entity similarity emphasize too much the structural inconsistency. Indeed, entities from heterogeneous sources could have very distinct structures, owing to various information representation conventions. We argue that the retrieval could be more tolerant to structural differences and focus more on the contents of the entities. In this paper, we first identify the unique challenge of parent-child (containment) relationships among structured entities, which unfortunately prevent the retrieval of proper entities (returning parents or children). To solve the problem, a novel hierarchy smooth function is proposed to combine the term scores in different nodes of a structured entity. Entities sharing the same structure, namely an entity family, are employed to learn the coefficient in aggregating the scores, and thus distinguish/prune the parent or child entities. Remarkably, the proposed method could cooperate with both the bag-of-words (BOW) and word embedding models, successful in retrieving unstructured documents, for querying structured entities. Extensive experiments on real datasets demonstrate that our proposal is effective and efficient.

Scaling entity resolution: A loosely schema-aware approach

In big data sources, real-world entities are typically represented with a variety of schemata and formats (e.g., relational records, JSON objects, etc.). Different profiles (i.e., representations) of an entity often contain redundant and/or inconsistent information. Thus identifying which profiles refer to the same entity is a fundamental task (called Entity Resolution) to unleash the value of big data. The naïve all-pairs comparison solution is impractical on large data, hence blocking methods are employed to partition a profile collection into (possibly overlapping) blocks and limit the comparisons to profiles that appear in the same block together. Meta-blocking is the task of restructuring a block collection, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features, under the assumption that handling the schema variety of big data does not pay-off for such a task. In this paper, we demonstrate how “loose” schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract the loose schema information by adopting an LSH-based step for efficiently handling volume and schema heterogeneity of the data. Furthermore, we introduce a novel meta-blocking algorithm that can be employed to efficiently execute Blast on MapReduce-like systems (such as Apache Spark). Finally, we experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art (meta-)blocking approaches.

Web-GIS and livestock trace tools for epidemiological surveillance, control and management

Web-GIS and livestock trace tools for epidemiological surveillance, control and management

A mobile game-based learning system for diacritic insertion

This work reports the development of a mobile game-based learning system for Yoruba diacritics with a motive to provide an interesting means of learning its diacritics insertion procedure for effective reading and writing in the language. Data were gathered from Yoruba language texts, interview and internet sources and grouped into levels of difficulty based on the number of syllables contained in each word. Two and three syllable words, four syllable words, five and six syllable words were categorised as easy, intermediate and hard levels respectively. The corpus was formed into a JSON object. The system was designed and implemented using the unified modelling language and JavaScript framework, ReactNative applications for iOS and Android. A randomly selected sample of 85 students was used for the evaluation of the system. Questionnaire containing twenty items, which was drawn based on the heuristics evaluation components, was used to measure its effectiveness as a tool for learning and skill acquisition. All the students who were given questionnaires indicated that they strongly agreed to keep using the game for play and learning purposes. The research concludes that the resource makes learning the language practical, fun, interesting and can be used anywhere in relation to the facilities owned by the students.