JavaScript Object Notation Data Research Articles

PyEGAF: An open-source Python library for the Evaluated Gamma-ray Activation File

The Evaluated Gamma-ray Activation File (EGAF) is one of the most comprehensive resources for thermal neutron-capture data. This database contains data from prompt gamma activation analysis measurements carried out in a consistent manner using the same experimental configuration at the Budapest Research Reactor for 245 isotopes. Although these valuable datasets have been freely available for many years, one of the drawbacks is the outdated and cryptic Evaluated Nuclear Structure Data File (ENSDF) format that is currently adopted for dissemination, making it difficult for users unfamiliar with the format to access and utilize the data contained therein. Furthermore, the ENSDF format does not readily lend itself to modern computational technologies and a parser is required to interpret the complicated mixed-record format. To help overcome these challenges, we have developed a translator to convert the ENSDF-formatted datasets into an open standard JavaScript Object Notation (JSON) format enabling accessibility to applications using different programming languages running in different environments. To compliment this effort, we have also developed an open-source software package implemented in Python, pyEGAF, that is designed to interact with the JSON data structures for general purpose access, manipulation, and analysis of the neutron-capture γ-ray data in EGAF. The new format, together with the pyEGAF library, greatly enhances access to the wider applications community where EGAF data may be useful or is required.

A proposed FAIR approach for disseminating geospatial information system maps

We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets.

$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau \text{JOWL}$</tex> (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.

A Temporal JSON Data Model and Its Query Languages

As a ubiquitous form of data in the natural world, time has been widely used in many domains such as the military, finance, medical treatment, and environment. Temporal data models that are used to model the dynamic development process of data are constantly proposed, such as temporal database and temporal XML. With the rapid development and popularization of the Web, the requirement of dealing with diverse data efficiently is becoming more important. As a new generation of data exchange language, JSON (JavaScript Object Notation) has been widely used because it is lightweight, has fast parsing, and has high transmission efficiency. In this paper, we propose a novel temporal JSON data model. Based on the proposed temporal JSON data model, we further propose two temporal JSON query languages, t-JSONPath, and t-JSONiq, which are the temporal extensions to JSONPath and JSONiq, respectively. With the proposed temporal JSON data model, we demonstrate our temporal JSON query languages with typical temporal query examples and evaluate their query response times.

Sentiment analysis of tweets on prior authorization.

322 Background: Natural language processing (NLP) algorithms can be leveraged to better understand prevailing themes in healthcare conversations. Sentiment analysis, an NLP technique to analyze and interpret sentiments from text, has been validated on Twitter in tracking natural disasters and disease outbreaks. To establish its role in healthcare discourse, we sought to explore the feasibility and accuracy of sentiment analysis on Twitter posts (‘’tweets’’) related to prior authorizations (PAs), a common occurrence in oncology built to curb payer-concerns about costs of cancer care, but which can obstruct timely and appropriate care and increase administrative burden and clinician frustration. Methods: We identified tweets related to PAs between 03/09/2021-04/29/2021 using pre-specified keywords [e.g., #priorauth etc.] and used Twarc, a command-line tool and Python library for archiving Twitter JavaScript Object Notation data. We performed sentiment analysis using two NLP models: (1) TextBlob (trained on movie reviews); and (2) VADER (trained on social media). These models provide results as polarity, a score between 0-1, and a sentiment as ‘’positive’’ (&gt;0), ‘’neutral’’ (exactly 0), or ‘’negative’’ (&lt;0). We (AG, NP) manually reviewed all tweets to give the ground truth (human interpretation of reality) including a notation for sarcasm since models are not trained to detect sarcasm. We calculated the precision (positive predictive value), recall (sensitivity), and the F1-Score (measure of accuracy, range 0-1, 0=failure, 1=perfect) for the models vs. the ground truth. Results: After preprocessing, 964 tweets (mean 137/ week) met our inclusion criteria for sentiment analysis. The two existing NLP models labeled 42.4%- 43.3% tweets as positive, as compared to the ground truth (5.6% tweets positive). F-1 scores of models across labels ranged from 0.18-0.54. We noted sarcasm in 2.8% of tweets. Detailed results in Table. Conclusions: We demonstrate the feasibility of performing sentiment analysis on a topic of high interest within clinical oncology and the deficiency of existing NLP models to capture sentiment within oncologic Twitter discourse. Ongoing iterations of this work further train these models through better identification of the tweeter (patient vs. health care worker) and other analytics from shared content.[Table: see text]

Comparison of JSON and XML Data Formats in Document Stored NoSql Database Replication Processes

The current trend of solutions in storing large amounts of data is using the NoSQL Database. A document stored is one type of NoSQL database that uses the JavaScript Object Notation (JSON) and eXtensible Markup Language (XML) data formats in data storage. High Availability database is significant to support cloud-based applications and services. Replication is one solution to maintain the consistency of source data and target data. This study aims to determine the performance of JSON and XML data formats in the document stored NoSQL Database replication process. In this study, ArangoDB, RethinkDB, and MongoDB were chosen for use in the trial process of replication from master-server to slave-server with two different data formats, JSON and XML. Data transfer, CPU usage, memory usage, and execution time are measured in each trial. Based on research and experiments that have been carried out, the JSON data format consumes bandwidth with an average value smaller than the XML data format; this occurs in MongoDB, CouchDB, and RethinkDB. In CPU usage, JSON data format, on average, consumes less CPU compared to the XML data format. This is the case with MongoDB. While on CouchDB and RethinkDB, the average CPU usage for XML and JSON data formats does not show a significant difference. The average memory usage for the JSON data format is smaller than the XML data format. The average execution time of the XML data format a little faster than the JSON data format.

WEIDJ: Development of a new algorithm for semi-structured web data extraction

In the era of industrial digitalization, people are increasingly investing in solutions that allow their process for data collection, data analysis and performance improvement. In this paper, advancing web scale knowledge extraction and alignment by integrating few sources by exploring different methods of aggregation and attention is considered in order focusing on image information. The main aim of data extraction with regards to semi-structured data is to retrieve beneficial information from the web. The data from web also known as deep web is retrievable but it requires request through form submission because it cannot be performed by any search engines. As the HTML documents start to grow larger, it has been found that the process of data extraction has been plagued with lengthy processing time. In this research work, we propose an improved model namely wrapper extraction of image using document object model (DOM) and JavaScript object notation data (JSON) (WEIDJ) in response to the promising results of mining in a higher volume of image from a various type of format. To observe the efficiency of WEIDJ, we compare the performance of data extraction by different level of page extraction with VIBS, MDR, DEPTA and VIDE. It has yielded the best results in Precision with 100, Recall with 97.93103 and F-measure with 98.9547.

Scalable structural index construction for JSON analytics

JavaScript Object Notation (JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices , can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison - a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine.

On Massive JSON Data Model and Schema

JSON (JavaScript Object Notation) is a lightweight semi-structured data format based on the data types of programming language JavaScript. It is a popular data exchange format over the World Wide Web and becomes a dominant standard format for sending API (Application Programming Interface) requests and responses in the past few years. Furthermore, JSON has also attracted attentions of database community research, especially in data intensive applications. JSON is not only can be integrated in traditional database systems, but also widely used in NoSQL database systems and graph database systems. Compared with XML, JSON document is a set of “key-value” pairs, in which the “value” itself can be a JSON document, which allows arbitrary levels of nesting, so it is more flexible to use and more difficult to process accordingly. JSON data model and schema describe the basic data structures and semantics of the underlying JSON data, so it is the fundamental and key aspects for JSON data format. JSON data model and schema are not only foundations for other data management technologies, such as data indexing, data querying, data searching, data mapping, data integrating, and data mining, but also has important theoretical significance and application prospects to provide theoretical basis and technical means for other related research, such as data integration, data conversion and other semi-structured and unstructured data queries. This paper analyzes the key problems of JSON data model and schema, including what data model should adopted by JSON and the specification and schema outline of JSON model.

Translating JSON Schema logics into OWL axioms for unified data validation on a digital manufacturing platform

Abstract JSON (JavaScript Object Notation) is a prevalent data format used in cloud-based platforms that support composable digital manufacturing workflows. The current work presents a method to translate the logics found in JSON Schema into OWL axioms, in order to facilitate ontology-based unified data validation with JSON data. The specific contributions of this paper include the demonstration of using a formal ontology for the logic translation and data validation, a technique for disambiguating implicit relations found in JSON Schema as explicit OWL properties, and mapping JSON Schema validation keywords to equivalent OWL expressions.

An effective biomedical data migration tool from resource description framework to JSON.

Resource Description Framework (RDF) is widely used for representing biomedical data in practical applications. With the increases of RDF-based applications, there is an emerging requirement of novel architectures to provide effective supports for the future RDF data explosion. Inspired by the success of the new designs in National Center for Biotechnology Information dbSNP (The Single Nucleotide Polymorphism Database) for managing the increasing data volumes using JSON (JavaScript Object Notation), in this paper we present an effective mapping tool that allows data migrations from RDF to JSON for supporting future massive data explosions and releases. We firstly introduce a set of mapping rules, which transform an RDF format into the JSON format, and then present the corresponding transformation algorithm. On this basis, we develop an effective and user-friendly tool called RDF2JSON, which enables automating the process of RDF data extractions and the corresponding JSON data generations.

Survey on JSON Data Modelling

JavaScript Object Notation (JSON) is a popular data format based on the data types of JavaScript programming language. As a lightweight semi-structured data format language, it has become the main data exchange format over the World Wide Web in the past several years, and gained popularity in database community research. JSON data model describes the basic data structures and semantics of the underlying JSON data, so it is the fundamental and key aspects for JSON data format. As JSON data model is also the key and foundation for other data management technologies, such as data indexing, data querying, data searching, data mapping, data integrating, and data mining, how to design an efficient and powerful model for JSON data is necessary and important to other related research topics, such as data integration, data search and query, data quality and evaluation, etc. In this paper, we investigate some approaches to modelling JOSN data and corresponding JSON schemas, which are mainly based on tree models. The advantages and disadvantages of each method are analyzed and compared.

Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration

BackgroundApplication Programming Interfaces (APIs) are now widely used to distribute biological data. And many popular biological APIs developed by many different research teams have adopted Javascript Object Notation (JSON) as their primary data format. While usage of a common data format offers significant advantages, that alone is not sufficient for rich integrative queries across APIs.ResultsHere, we have implemented JSON for Linking Data (JSON-LD) technology on the BioThings APIs that we have developed, MyGene.info, MyVariant.info and MyChem.info. JSON-LD provides a standard way to add semantic context to the existing JSON data structure, for the purpose of enhancing the interoperability between APIs. We demonstrated several use cases that were facilitated by semantic annotations using JSON-LD, including simpler and more precise query capabilities as well as API cross-linking.ConclusionsWe believe that this pattern offers a generalizable solution for interoperability of APIs in the life sciences.

SQL/JSON Standard: Properties and Deficiencies

Recently, a new era of application development is emerging, which is based upon the ease of access to modern compute resources, such as mobile devices. This access can be supported using JSON (Java Script Object Notation). Therefore, the support of storage and query access for JSON documents in the context of relational DBMSs is necessary. For this reason, the SQL standardization committee published a proposal called SQL/JSON. In this paper we discuss the JSON features specified in the proposal and show to what extent different relational database systems have integrated them. At the end of the paper we describe the main drawbacks of the proposal and the ways to solve them. From our point of view, the following should be specified in one of the future proposals of SQL/JSON: JSON documents should be first-class objects in SQL (native storage). Handling JSON documents as first-class objects in SQL would provide the potential for greater capability for users and for better performance. The support of modification of parts of a JSON document using the SQL UPDATE statement is necessary. Direct access of external JSON data should be supported, too.

New algorithms for processing time-series big EEG data within mobile health monitoring systems

Background and objectivesRecent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. MethodsIn this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. ResultsWe tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. ConclusionsOur approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures.

Utilizing Nested Normal Form to Design Redundancy Free JSON Schemas

JSON (JavaScript Object Notation) is a lightweight data-interchange format for the Internet. JSON is built on two structures: (1) a collection of name/value pairs and (2) an ordered list of values (http://www.json.org/). Because of this simple approach, JSON is easy to use and it has the potential to be the data interchange format of choice for the Internet. Similar to XML, JSON schemas allow nested structures to model hierarchical data. As data interchange over the Internet increases exponentially due to cloud computing or otherwise, redundancy free JSON data are an attractive form of communication because they improve the quality of data communication through eliminating update anomaly. Nested Normal Form, a normal form for hierarchical data, is a precise characterization of redundancy. A nested table, or a hierarchical schema, is in Nested Normal Form if and only if it is free of redundancy caused by multivalued and functional dependencies. Using Nested Normal Form as a guide, this paper introduces a JSON schema design methodology that begins with UML use case diagrams, communication diagrams and class diagrams that model a system under study. Based on the use cases’ execution frequencies and the data passed between involved parties in the communication diagrams, the proposed methodology selects classes from the class diagrams to be the roots of JSON scheme trees and repeatedly adds classes from the class diagram to the scheme trees as long as the schemas satisfy Nested Normal Form. This process continues until all of the classes in the class diagram have been added to some JSON scheme trees.

Enhancing the Presence Service Efficiency of Internet Protocol Multimedia Subsystem-Based Web Services

Over the past decade, voice-over-Internet protocol technologies and multimedia services for mobile devices have become a major trend of mobile networks because of the pervasiveness of handheld devices and the increased availability of advanced cellular networks worldwide. Next-generation network solutions, such as Internet protocol multimedia subsystems (IMS), offer considerable potential for providing various multimedia services. However, current IMS users have been developed separately based on various operating systems or devices. This study adopted a web-based IMS user and deployed a gateway between IMS users and an IMS network. Users can access IMS services by employing any device equipped with a web browser which supports a web real-time communication application programming interface. Since more real-time messaging services have exploited presence services (PSs) to issue user status information, a high number of status messages are generated. Instead of using the extensible markup language (XML) format to exchange presence data in PSs, this study adopted JavaScript object notation (JSON), which has a more compact and lightweight format for reducing the message volume and delay. This study evaluated the system performance based on XML and JSON data formats under realistic conditions. The results show that adopting JSON can effectively reduce the message volume and delay by approximately 40 and 54 %, respectively.

JSONiq: The History of a Query Language

NoSQL data stores support hierarchical, heterogeneous data such as XML, JavaScript Object Notation (JSON), and Binary JSON (BSON). A need exists for a tailor-made language for querying such hierarchical, heterogeneous datasets. JSONiq was designed from the ground up to support JSON data and run against all JSONor BSON-based data stores, turning them into full-fledged databases.