Spatial Big Data and Moving Objects: A Comprehensive Survey <s> II. SPATIAL BIG DATA AND MOVING OBJECTS <s> This paper describes the application of data mining methods in the database of the DORIS transportation information system, currently used by the Prague Public Transit Company. The goal is to create knowledge about the behavior of objects within this information system. Data is analyzed partly with the help of descriptive statistical methods, and partly with the help of association rules, which may discover common combinations of attributes that occur most frequently within a given data set. Two types of quantifiers were used when creating the association rules; namely “founded implication” and “above average”. The results of the analysis are presented in the form of graphs and hypotheses. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> II. SPATIAL BIG DATA AND MOVING OBJECTS <s> Research on so-called ‘Big Data’ has received a considerable momentum and is expected to grow in the future. One very interesting stream of research on Big Data analyzes online networks. Many online networks are known to have some typical macro-characteristics, such as ‘small world’ properties. Much less is known about underlying micro-processes leading to these properties. The models used by Big Data researchers usually are inspired by mathematical ease of exposition. We propose to follow in addition a different strategy that leads to knowledge about micro-processes that match with actual online behavior. This knowledge can then be used for the selection of mathematically-tractable models of online network formation and evolution. Insight from social and behavioral research is needed for pursuing this strategy of knowledge generation about micro-processes. Accordingly, our proposal points to a unique role that social scientists could play in Big Data research. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> II. SPATIAL BIG DATA AND MOVING OBJECTS <s> Big data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. Data, information, and knowledge are being created and collected at a rate that is rapidly approaching the exabyte/year range. But, its creation and aggregation are accelerating and will approach the zettabyte/year range within a few years. Volume is only one aspect of big data; other attributes are variety, velocity, value, and complexity. Storage and data transport are technology issues, which seem to be solvable in the near-term, but represent longterm challenges that require research and new paradigms. We analyze the issues and challenges as we begin a collaborative research program into methodologies for big data analysis and design. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> II. SPATIAL BIG DATA AND MOVING OBJECTS <s> The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> II. SPATIAL BIG DATA AND MOVING OBJECTS <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> BIB005
The term big data signifies to huge amount of data that is complex to analyze. There are several examples of BD such as location maps, investigative reports, data gathered from thousands of sensors placed at various locations, climatic changes and detailed weather reports, political surveys about upcoming elections, and so on. According to BIB003 , big data is large and heterogeneous making it difficult to store in regular relational databases. This data can either be structured (stored in relational databases) or unstructured to be used for analysis. Michael et al. BIB005 argue that the size of data varies with the context which means there is a solid correlation between the dataset and users' personal experiences. Snijders et al. BIB002 define BD as a combination of datasets which are humongous in size and cannot be solved using simple statistical techniques within the allocated time frame. These datasets are mostly loosely coupled showing the complexity in handling and processing. Similarly, Laney  defines BD as any information with large size, variety, and high volume that can only be managed through the development of advanced optimization and discovery techniques. Moreover, well-known organizations like IBM, Intel, and Oracle also distinguish BD from conventional data in its size, volume, and unstructured nature BIB004 . Table 2 summarizes  various existing viewpoints about BD. BD is in limelight for years now and a lot of research has been done on defining, processing, and handling these large sets of random information. Due to the current advances in the field of information technology especially with the provision of internet connectivity whenever and wherever needed, SBD has emerged as one of the most important and interesting research areas. The terms BD and SBD are easy to differentiate. For example, normal posts/tweets on Facebook and Twitter can be categorized as BD while geo-located posts/tweets from different parts of the world fall under the window of SBD BIB005 . Another example of SBD is the data gathered from various 'sensor nodes' spatially located in a large geographic region. This data has to be stored, monitored, and processed in real-time to deal with situations like disaster recovery, possibility of fire spreading in a jungle, and temperature control. SBD is largely influenced by the movement of geographically located objects. These 'moving objects' are mostly in the form of humans and vehicles changing their locations on frequent basis. Sometimes, these location changes are so random and fast that it gets difficult to process and control the generated data. Thus, special algorithms and solutions are required to handle the data generated by the moving objects. Recent trends shown by the mobile industry in terms of volume (up to Terabytes) and variety (such as gaming, multimedia, cloud services, navigation, and social posts) are also part of SBD. A huge increase in the usage of location-based services (like Google Earth, Google Maps, Swarm, Waze, etc.) through mobile phones and other handheld devices highlights VOLUME 6, 2018 the importance of SBD in our daily life. Table 3 summarizes the difference between BD and SBD w.r.t several interesting contexts and examples. In continuation to above, figure 1 depicts a case study based on people's daily patterns in life. These statistics were presented in  where the 'activity space' is chosen as a life pattern. Based on a thorough GPS tracking of 30 people's weekday routine, we see that a high percentage spends their time at homes or in offices. However, their online data access could totally be different. For example, from noon to 5pm, the pattern of data would be video conferencing, stock exchange monitoring, fund transferring, tweeting about current achievements in office, and posting pictures of lunch menus and restaurants. On the other hand, the hours from midnight to 7am would result in rather different traffic patterns like normal web-browsing and social media activities. Therefore, a person having a seamless internet connectivity will be experiencing a wide variety of activity during a single day and by multiplying it with billions, we can think of how huge, dynamic, and geographically distant this data can become. Furthermore, in this context, in Table 4 , we name certain examples of data, which are specific to SBD and moving objects, and should be processed in real-time. The recent trends show that the total amount of data (generated from the types specified in Table 4 and similar other cases) is increasing at a fast pace and expected to reach 7.9 Zettabytes this year compared to 1.8 Zettabytes in 2011, respectively BIB001 . Therefore, efficient techniques are desirable to compensate this immense growth in SBD considering the distributed and dynamic nature of objects.