Smart Education Using Internet of Things Technology

Smart education is now a typical feature in education emerging from information communications technologies (ICT) and the constant introduction of new technologies into institutional learning. The smart classroom aims users to develop skills, adapt, and use technologies in a learning context that produces elevated learning outcomes which leads to big data. The internet of things (IoT) is a new technology in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. The technologies are rapidly changing, and designing for these situations can be complex. Designing the IoT applications is a challenging issue. The existing standardization activities are often redundant IoT development. The reference architecture provides a solution to smart education for redundant design activities. The purpose of this chapter is to look at the requirements and architectures required for smart education. It is proposed to design a scalable and flexible IoT architecture tor smart education (IoTASE).

New Model for Geospatial Coverages in JSON

Map browsers currently in place present maps and geospatial information using common image formats such as JPEG or PNG, usually created from a service on demand. This is a clear approach for a simple visualization map browser but prevents the browser from modifying the visualization since the content of the image file represents the intensity of colors of each pixel. In a desktop GIS, a coverage dataset is an array of values quantifying a certain property in each pixel of a subdomain of the space. The standard used to describe and distribute coverages is called web coverage service (WCS). Traditionally, encoding of coverages was too complex for map browsers implemented in JavaScript, relegating the WCS to a data download, a process that creates a file that will be later used in a desktop GIS. The combination of a coverage implementation schema in JSON, binary arrays, and HTML5 canvas makes it possible that web map browsers can be directly implemented in JavaScript.

Towards Massive RDF Storage in NoSQL Databases

The resource description framework (RDF) is a model for representing information resources on the web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the web, a huge amount of RDF data is being proliferated and becoming available. So, RDF data management is of increasing importance and has attracted attention in the database community as well as the Semantic Web community. Currently, much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (not only SQL) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

Efficient Storage and Parallel Query of Massive XML Data in Hadoop

In order to solve the problem of storage and query for massive XML data, a method of efficient storage and parallel query for a massive volume of XML data with Hadoop is proposed. This method can store massive XML data in Hadoop and the massive XML data is divided into many XML data blocks and loaded on HDFS. The parallel query method of massive XML data is proposed, which uses parallel XPath queries based on multiple predicate selection, and the results of parallel query can satisfy the requirement of query given by the user. In this chapter, the map logic algorithm and the reduce logic algorithm based on parallel XPath queries based using MapReduce programming model are proposed, and the parallel query processing of massive XML data is realized. In addition, the method of MapReduce query optimization based on multiple predicate selection is proposed to reduce the data transfer volume of the system and improve the performance of the system. Finally, the effectiveness of the proposed method is verified by experiment.

A Survey on JSON Mapping With XML/RDF

As a data format, JSON is able to store and exchange data. It can be mapped with RDF (resource description framework), which is an ontology technology in the direction of web resources. This chapter replies to the question about which techniques or methods to utilize for mapping XML to JSON and RDF. However, a plethora of methods have been explored. Consequently, the goal of this survey is to give the whole presentation of the currents approaches to map JSON with XML and RDF by providing their differences.

Big Data Processing and Big Analytics

Development and wide acceptance of data-driven applications in many aspects of our daily lives is generating waste volume of diverse data, which can be collected and analyzed to support various valuable decisions. Management and processing of this big data is a challenge. The development and extensive use of highly distributed and scalable systems to process big data have been widely considered. New data management architectures (e.g., distributed file systems and NoSQL databases) are used in this context. However, features of big data like their complexity and data analytics demands indicate that these concepts solve big data problems only partially. A development of so called NewSQL databases is highly relevant and even special category of big data management systems is considered. In this chapter, the authors discuss these trends and evaluate some current approaches to big data processing and analytics, identify the current challenges, and suggest possible research directions.

Prediction of Uncertain Spatiotemporal Data Based on XML Integrated With Markov Chain

Since XML could benefit data management greatly and Markov chains have an advantage in data prediction, the authors study the methodology of predicting uncertain spatiotemporal data based on XML integrated with Markov chain. To accomplish this, first, the researchers devise an uncertain spatiotemporal data model based on XML. Then, the researchers put forward the method based on Markov chains to predict spatiotemporal data, which has taken the uncertainty into consideration. Next, the researchers apply the prediction method to meteorological field. Finally, the experimental results demonstrate the advantages the authors approach. Such a method of prediction could broaden the research field of spatiotemporal data, and provide a significant reference in the study of forecasting uncertain spatiotemporal data.

A JSON-Based Fast and Expressive Access Control Policy Framework

Along with the rapid development of ICT technologies, new areas like Industry 4.0, IoT, and 5G have emerged and brought out the need for protecting shared resources and services under time-critical and energy-constrained scenarios with real-time policy-based access control. To achieve this, the policy language needs to be very expressive but lightweight and efficient. These challenges are investigated and a set of key requirements for such a policy language is identified. JACPoL is accordingly introduced as a descriptive, scalable, and expressive policy language in JSON. JACPoL by design provides a flexible and fine-grained ABAC style (attribute-based access control) while it can be easily tailored to express other access control models. The design and implementation of JACPoL are illustrated together with its evaluation in comparison with other existing policy languages. The result shows that JACPoL can be as expressive as existing ones but more simple, scalable, and efficient. The performance evaluation shows that JACPoL requires much less processing time and memory space than XACML.

JSON Data Management in RDBMS

Being a simple semi-structured data model, JSON has been widely accepted as a simple way to store, query, modify, and exchange data among applications. In comparison with schema-oriented relational data model to store, query, and update application data, JSON data model has the advantage of being self-contained, free from schema evolution issues, and flexible enough to enable agile style development paradigm. Therefore, during the last 5 years, SQL/JSON standard has been established as foundation for managing JSON data in SQL standard and there have been JSON functionalities added into RDBMS products to support SQL/JSON standard to various degree. In this chapter, the authors will analyze the strength and weakness of using JSON as the data model to manage data for applications. For use cases where JSON data model is ideal, they present the design approaches to store, index, query, and update JSON in the kernel of RDBMS to support SQL/JSON standard defined operations effectively and efficiently.

Segmented Dynamic Time Warping

Initially used in speech recognition, the dynamic time warping algorithm (DTW) has regained popularity with the widespread use of time series data. While demonstrating good performance, this elastic measure has two significant drawbacks: high computational costs and the possibility of pathological warping paths. Due to the balance between performance and the tightness of restrictions, the effects of many improvement techniques are either limited in effect or use accuracy as a trade-off. In this chapter, the authors discuss segmented-DTW (segDTW) and its applications. The intuition behind significant features is first established. Then considering the variability of different datasets, the relationship between specific global feature selection parameters, feature numbers, and performance are demonstrated. Other than the improvement in computational speed and scalability, another advantage of segDTW is that while it can be a stand-alone heuristic, it can also be easily combined with other DTW improvement methods.