Modeling Temporal Information With JSON

Modeling Temporal Information With JSON

Zhangbing Hu (Nanjing University of Aeronautics and Astronautics, China) and Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-5225-8446-9.ch007

Abstract

As a ubiquitous form of data in human natural life, time has been widely used in military, finance, medical treatment, environment and other fields. Therefore, temporal data models used to express the dynamic development process of data have been proposed constantly. Currently, the main research achievements focus on temporal database and temporal XML. With the rapid development and popularization of network technology, the requirement of efficiency and security is getting higher and higher. JSON, a new generation of data exchange language, has been widely used because of its lightweight, fast parsing and high transmission efficiency. However, modeling temporal information with JSON has not been studied enough. The chapter proposes a temporal data model based on JSON. What is more, the temporal query language and the JSON Schema is also mentioned.
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Introduction

With the popularity and development of the Internet, amount of data onto all domains is growing exponentially. According to the data volume growth report that released by China Cloud Computing Conference Website in 2018, the amount of Web data in 2020 will be 44 times that of today. Due to the increasing amount of data volume and the improvement in software and hardware computing capability, cloud computing, big data, data mining, machine learning, and other intelligent technologies has developed rapidly, therefore it is very valuable and necessary to use these technologies excavating the hidden trends and laws behind a large number of data (Han, 2005; Mitsa, 2010; Chen & Petrounias, 1998). Time as an omnipresent property of the realistic natural world, it has a great significance when studying the ceaseless application area. Meanwhile, mining the laws behind time dimension from a large number of data have received wide attention in academia and industry (Mitsa, 2010). As early as in 1998, A framework for temporal data mining has raised (Chen & Petrounias, 1998). In the field of medicine,describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services (Chittaro, Combi, & Trapasso 2003; Bellazzi, et al. 2005). What is more, spatio-temporal data mining for typhoon image collection may forecast the typhoon trends (Kitamoto, 2002). Summarizing industry's work, the work in the paper shows how to mine the temporal laws behind temporal information about SOM, SVM, temporal periodic pattern algorithm and so on (Post & Jr, 2008). Furthermore, the intelligent technology of big data, Hadoop, spark, druid, has been widely applied in temporal field.

The JavaScript Object Notation (JSON) has presented by Dougalas Crockford to IETF RFC draft (Bray, 2014).

  • 1.

    It is a lightweight data-interchange format based on the pro-type of the JavaScript programming language;

  • 2.

    It is easy for human to read and write and easy for machine to parse and generate;

  • 3.

    JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages.

These characteristics make JSON an ideal data-interchange language. In addition, JOSN has higher flexibility and scalability when compared with XML (Lin, et al. 2012). Therefore, JSON has been widely used. It is currently the predominant format for sending API requests and responses over the HTTP or HTTPS protocol for its high data transmission efficiency (Sheth, Henson & Sahoo, 2008). JSON data format obtains a wide application. Research on the applications of JSON data interchange format in heterogeneous system integration (Gu & Shen, 2012; Wang & Zhu, 2018). There is a way to realize Android efficiently and safely accessing from a remote database by using JSON format (Soewito, et.al, 2017). JSON was also applied in asynchronous distributed genetic algorithms (Merelo, et.al, 2008). What is more, the implementation of document database in non-relational database classification, MongoDB, CouchDB, are all based on JSON grammar format (Bellazzi, et.al 2016; Boicea, et.al,2012). But the real world is changing over time, non-temporal data model can not well reflect the development and change process of data.

In this paper, we propose a temporal data model based on JSON. First, we introduce a non-temporal data model based on JSON, then we obtain our temporal model by adding validity time to model that represents the history of a fact in the modeled reality. Finally, we make a formal definition of our temporal data model. The main contributions of this paper are summarized as follows:

Key Terms in this Chapter

Schema: Different from the definition in database domain, the schema of JSON or XML represent the constraints and validation rules of the document, which means the structure of the relation in database model.

Temporal Date Model: Add the temporal attribute to traditional data model, XML data model, relation database data model, etc., that can obtain the corresponding temporal data model.

Temporal Attribute: Intuitively, temporal attributes are time data in the real world. But after the temporal attribute has been modeled, it represents the time data in the temporal model.

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