A Review of 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 attentions 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.

Knowledge as a Service Framework for Collaborative Data Management in Cloud Environments - Disaster Domain

Decision-making in disaster management requires information gathering, sharing, and integration by means of collaboration on a global scale and across governments, industries, and communities. Large volume of heterogeneous data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in NoSQL, cloud computing, and Big Data open the door for new solutions in disaster data management. This chapter presents a Knowledge as a Service (KaaS) framework for disaster cloud data management (Disaster-CDM), with the objectives of facilitating information gathering and sharing; storing large amounts of disaster-related data; and facilitating search and supporting interoperability and integration. In the Disaster-CDM approach NoSQL data stores provide storage reliability and scalability while service-oriented architecture achieves flexibility and extensibility. The contribution of Disaster-CDM is demonstrated by integration capabilities, on examples of full-text search and querying services.

Parallel Queries of Cluster-Based k Nearest Neighbor in MapReduce

Parallel queries of k Nearest Neighbor for massive spatial data are an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every point in another dataset R, is a useful tool widely adopted by many applications including knowledge discovery, data mining, and spatial databases. In cloud computing environments, MapReduce programming model is a well-accepted framework for data-intensive application over clusters of computers. This chapter proposes a parallel method of kNN queries based on clusters in MapReduce programming model. Firstly, this chapter proposes a partitioning method of spatial data using Voronoi diagram. Then, this chapter clusters the data point after partition using k-means method. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on k-means clusters using MapReduce programming model. Finally, extensive experiments evaluate the efficiency of the proposed approach.

Modeling and Indexing Spatiotemporal Trajectory Data in Non-Relational Databases

With the ever-growing nature of spatiotemporal data, it is inevitable to use non-relational and distributed database systems for storing massive spatiotemporal datasets. In this chapter, the important aspects of non-relational (NoSQL) databases for storing large-scale spatiotemporal trajectory data are investigated. Mainly, two data storage schemata are proposed for storing trajectories, which are called traditional and partitioned data models. Additionally spatiotemporal and non-spatiotemporal indexing structures are designed for efficiently retrieving data under different usage scenarios. The results of the experiments exhibit the advantages of utilizing data models and indexing structures for various query types.

Security in Cloud of Things (CoT)

With IoT era, development raises several significant research questions in terms of system architecture, design and improvement. For example; the requirement of virtual resource utilization and storage capacity necessitates making IoT applications smarter; therefore, integrate the IoT concept with cloud computing will play an important role. This is crucial because of very large amounts of data that IoT is expected to generate. The Cloud of Things (CoT) is used to connect heterogeneous physical things to the virtual domain of the cloud. Despite its numerous advantages, there are many research challenges with utilization of CoT that needs additional consideration. These include high complexity, efficiency, improving reliability, and security. This chapter introduces CoT, its features, the applications that use CoT. CoT, like all other networked functions, is vulnerable to security attacks. The security risks for CoT are listed and described. The security requirements for CoT are identified and solutions are proposed to address the various attacks on CoT and its components.

A Cloud-Aware Distributed Object Storage System to Retrieve Large Data via HTML5-Enabled Web Browsers

Major Internet services are required to process a tremendous amount of data at real time. As we put these services under the magnifying glass, It's seen that distributed object storage systems play an important role at back-end in achieving this success. In this chapter, overall information of the current state-of –the-art storage systems are given which are used for reliable, high performance and scalable storage needs in data centers and cloud. Then, an experimental distributed object storage system (CADOS) is introduced for retrieving large data, such as hundreds of megabytes, efficiently through HTML5-enabled web browsers over big data – terabytes of data – in cloud infrastructure. The objective of the system is to minimize latency and propose a scalable storage system on the cloud using a thin RESTful web service and modern HTML5 capabilities.

CCCE

Cloud computing technology is a modern emerging trend in the distributed computing technology that is rapidly gaining popularity in network communication field. Despite the advantages that the cloud platforms bolstered, it suffers from many security issues such as secure communication, consumer authentication, and intrusion caused by attacks. These security issues relevant to customer data filtering and lost the connection at any time. In order to address these issues, this chapter, introduces an innovative cloud computing cryptographic environment, that entails both Quantum Cryptography-as-service and Quantum Advanced Encryption Standard. CCCE poses more secure data transmission channels by provisioning secret key among cloud's instances and consumers. In addition, the QCaaS solves the key generation and key distribution problems that emerged through the online negotiation between the communication parties. It is important to note that the CCCE solves the distance limitation coverage problem that is stemmed from the quantum state property.

Green and Energy-Efficient Computing Architecture for E-Learning

Information and Communication Technology (ICT) is one of the fast growing industries that facilitate many latest services to the users and therefore, the number of users is increasing rapidly. The usage of ICT and its life cycle produce hazardous substances that need to be addressed in efficient and green ways. The adoption of green computing involves many improvements and provide energy-efficiency services for data centers, power management and cloud computing. Cloud computing is a highly scalable and cost-effective infrastructure for running Web applications. However, the growing demand of Cloud infrastructure has drastically increased the energy consumption of data centers, which has become a critical issue. Hence, energy-efficient solutions are required to minimize the impact of Cloud environment. E-learning methodology is an example of Green computing. Thus, it is proposed a Green Cloud Computing Architecture for e-Learning Applications that can lower expenses and reduce energy consumption.

Handling Critical Issues of Big Data on Cloud

Big Data is driving radical changes in traditional data analysis platforms. To perform any kind of analysis on such voluminous and complex data, scaling up the hardware platforms becomes impending. With the entire buzz surrounding Big Data; it is being collected at an unprecedented scale. Big Data has potential to revolutionize much more than just research. Loading large data-sets is often a challenge. Another shift of this Big Data processing is the move towards cloud computing. As many communities begin to rely on cloud based data management, large shared data goes up extensively. Analysis of such large data on distributed processing system or cloud is a bit difficult task to handle. The aim of this chapter is to provide a better understanding of the design challenges of cloud computing and analytics of big data on it. The challenge is related to how a large extent of data is being harnessed, and the opportunity is related to how effectively it is used for analyzing the information from it.

The Attitudes of Chinese Organizations Towards Cloud Computing

Cloud Computing become a significant factor in E-commerce and E-business processes and will reduce negative IT impacts on the environment without compromising the needs of future generations. This chapter aim to examine the attitudes of Chinese Organizations towards Cloud Computing adoption. This chapter provides an answer to the question: “What are the advantages and disadvantages of Cloud Computing adoption in Chinese organizations?” The answer was sought by means of an online survey of (N=121) respondents. The survey results revealed the Chinese position regarding the cloud movement, its strengths and weaknesses, the threats posed by Cloud Computing in China, and the specific advantages and disadvantages of this new technology that Chinese organizations and research communities should embrace for the realization of future cloud systems.