Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> Disaster management is a crucial and urgent research issue. Emergency communication networks (ECNs) provide fundamental functions for disaster management, because communication service is generally unavailable due to large-scale damage and restrictions in communication services. Considering the features of a disaster (e.g., limited resources and dynamic changing of environment), it is always a key problem to use limited resources effectively to provide the best communication services. Big data analytics in the disaster area provides possible solutions to understand the situations happening in disaster areas, so that limited resources can be optimally deployed based on the analysis results. In this paper, we survey existing ECNs and big data analytics from both the content and the spatial points of view. From the content point of view, we survey existing data mining and analysis techniques, and further survey and analyze applications and the possibilities to enhance ECNs. From the spatial point of view, we survey and discuss the most popular methods and further discuss the possibility to enhance ECNs. Finally, we highlight the remaining challenging problems after a systematic survey and studies of the possibilities. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> Abstract Reducing the number of road accident victims is a declared purpose of the European Union. Intelligent Transport Systems (ITS) are able to contribute to this by warning and supporting the drivers, therefore improving road safety. The aim of our research was to analyze the safety aspects of ITS systems, structuring the solutions, analyzing and exploring the opportunities for development. Strategic objectives have been evaluated and relevant processes for achieving them have been summarized. The research efficiently contributes to the utilization of development potentials of ITS systems. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> Complementary to the fancy big data applications, networking for big data is an indispensable supporting platform for these applications in practice. This emerging research branch has gained extensive attention from both academia and industry in recent years. In this new territory, researchers are facing many unprecedented theoretical and practical challenges. We are therefore motivated to solicit the latest works in this area, aiming to pave a comprehensive and solid starting ground for interested readers. We first clarify the definition of networking for big data based on the cross disciplinary nature and integrated needs of the domain. Second, we present the current understanding of big data from different levels, including its formation, networking features, mathematical representations, and the networking technologies. Third, we discuss the challenges and opportunities from various perspectives in this hopeful field. We further summarize the lessons we learned based on the survey. We humbly hope this paper will shed light for forthcoming researchers to further explore the uncharted part of this promising land. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> With the rapid growth of digital publishing, harvesting, managing, and analyzing scholarly information have become increasingly challenging. The term Big Scholarly Data is coined for the rapidly growing scholarly data, which contains information including millions of authors, papers, citations, figures, tables, as well as scholarly networks and digital libraries. Nowadays, various scholarly data can be easily accessed and powerful data analysis technologies are being developed, which enable us to look into science itself with a new perspective. In this paper, we examine the background and state of the art of big scholarly data. We first introduce the background of scholarly data management and relevant technologies. Second, we review data analysis methods, such as statistical analysis, social network analysis, and content analysis for dealing with big scholarly data. Finally, we look into representative research issues in this area, including scientific impact evaluation, academic recommendation, and expert finding. For each issue, the background, main challenges, and latest research are covered. These discussions aim to provide a comprehensive review of this emerging area. This survey paper concludes with a discussion of open issues and promising future directions. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> Recent years have seen the emergence of many new and valuable spatial datasets such as trajectories of cell-phones and Global Positioning System (GPS) devices, vehicle engine measurements, global climate models simulation data, volunteered geographic information (VGI), geo-social media, and tweets. The value of these datasets is already evident through many societal applications including disaster management and disease outbreak prediction. However, these location-aware datasets are of a volume, variety, and velocity that exceed the capability of current CyberGIS technologies. We refer to these datasets as Spatial Big Data. In this chapter, we define spatial big data in terms of its value proposition and user experience which depends on the computational platform, use-case, and dataset at hand. We compare spatial big data with traditional spatial data and with other types of big data. We then provide an overview of the current efforts, challenges and opportunities available when spatial big data is enabled via next-generation CyberGIS. Our discussion includes current accomplishments and opportunities from both an analytics and an infrastructure perspective. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> A. RELATED SURVEYS AND CONTRIBUTION OF THIS ARTICLE <s> Knowledge extraction from spatial big data (SBD) with advanced analytics has become a major trend in research and industry. Meanwhile, the increasingly complex SBD and its analytics face proliferat... <s> BIB007
In relation to above, a few survey related efforts have been done in the recent past. For example, the work proposed in BIB006 briefly differentiates between BD and SBD. This paper explains in detail the basic concept of big data and differentiates it with SBD using interesting examples. Several types of big data are also defined such as raster, vector, and graph based datasets. Later, Michael et al. BIB006 shift their entire focus on cyber-based information which includes faulttolerant file systems and BD clouds. Thus, the main idea of interest roams around global information systems (GIS). Our work, on the other hand, not only covers the spatial aspect of big data but also highlights the social media, routing, and learning perspectives. We list down numerous definitions related to SBD, and explain in detail various approaches and algorithms for SBD generation, handling, routing, and analysis. Another short survey is presented in  highlighting important challenges and opportunities in trajectory data analysis. The paper is written on the argument that current research on SBD trajectory analysis focuses on a single factor (such as the movement of people from one place to another) which is quite simple and thus, not enough to make effective and real-time routing decisions. Therefore, the authors emphasize on including multiple factors in trajectory analysis such the types of vehicles used for movement, temperature, and road conditions and they list challenges and issues related to the aforementioned. However, we think that the claim of authors is not totally justified since there are several existing approaches (such as BIB003 and BIB006 ) which consider multiple factors for routing-based decisions. The work proposed in BIB007 highlights the challenges for SBD with uncertainties. The focus of the paper is to identify the unreliable knowledge resulting from different sources and ensure stage-by-stage uncertainty handling for enhancing the reliability of the knowledge. The authors explain the reasons for the uncertain data and provide a framework for uncertainty based SBD analytics. They further discuss the place-based heuristic, analytics, and assessment of user generated images and texts in the context of any existing uncertainty. Quite differently, our work (especially section V) highlights different aspects of big data analysis in moving objects. Although uncertainty can be classified as one of the issues in SBD analysis for moving objects however, we kept our focus on discussing existing techniques used for effective analysis of the available data. Another interesting survey presented in BIB001 lists the important platforms for BD analysis but the importance of spatiality and moving objects is mostly ignored. The focus of BIB002 remains on disaster management aspects related to BD networks. The networking of BD is very comprehensively covered in BIB004 . Xia et al. BIB005 survey all the issues related to the processing and handling of BD which is generated via scholarly information databases. In this paper, we focus on various aspects related to SBD and moving objects. More specifically, the main contributions of this paper are as follows: • We comprehensively define BD. This part includes the viewpoint of several researchers about BD. BD is then differentiated with SBD with the help of promising examples. This section also clarifies the relationship between SBD and moving objects. • We focus on social media and e-applications which are considered to be the highest contributors in generating large volumes of spatial data. This section also includes examples of current market trends related to BD. • Our study highlights the routing perspective for BD and SBD including various interesting strategies to route large traffic volumes generated by the moving objects. This part surveys different strategies designed for routing of vehicles as well as network-generated traffic. • We discuss different techniques for BD analysis within the context of moving objects. • Current and future challenges and issues are also discussed for all of the above mentioned parts. The acronyms used throughout this article are presented in Table 1 .