Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> With the growing popularity of big-data applications, Data Center Networks increasingly carry larger and longer traffic flows. As a result of this increased flow granularity, static routing cannot efficiently load-balance traffic, resulting in an increased network contention and a reduced throughput. Unfortunately, while adaptive routing can solve this load-balancing problem, network designers refrain from using it, because it also creates out-of-order packet delivery that can significantly degrade the reliable transport performance of the longer flows. In this paper, we show that by throttling each flow bandwidth to half of the network link capacity, a distributed-adaptive-routing algorithm is able to converge to a non-blocking routing assignment within a few iterations, causing minimal out-of-order packet delivery. We present a Markov chain model for distributed-adaptive-routing in the context of Clos networks that provides an approximation for the expected convergence time. This model predicts that for full-link-bandwidth traffic, the convergence time is exponential with the network size, so out-of-order packet delivery is unavoidable for long messages. However, with half-rate traffic, the algorithm converges within a few iterations and exhibits weak dependency on the network size. Therefore, we show that distributed-adaptive-routing may be used to provide a scalable and non-blocking routing even for long flows on a rearrangeably-non-blocking Clos network under half-rate conditions. The proposed model is evaluated and approximately fits the abstract system simulation model. Hardware implementation guidelines are provided and evaluated using a detailed flit-level InfiniBand simulation model. These results directly apply to adaptive-routing systems designed and deployed in various fields. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> The paper presents the methodologies on information fusion for social media.The methodologies, frameworks, and software used to work with big data are given.The state of the art in the data analytic techniques on social big data is provided.Social big data applications for various domains are described and analyzed. Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> Abstract The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches are used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> Big data strongly demands a network infrastructure having the capability to efficiently collect, process, cache, share, and deliver the data, instead of simple transmissions. Such network designs show the requirements of energy efficiency, availability, high performance, and data-aware intelligence. To meet these requirements, we adopt the information-centric networking (ICN) approach, where data are retrieved through names and in-network caching is utilized. However, as the typical existing ICN architectures, content centric network (CCN) cannot efficiently utilize the caches for data sharing because of the on-path caching strategy, and network of information (NetInf) demonstrates the resolution latency for data retrievals. To design an efficient and effective ICN architecture for big data sharing, we combine the strong points of CCN and NetInf, where information islands (IOIs) and management plane are utilized for direct data retrieval and global data discovery, respectively. We provide a reference architecture and propose an aggregatable name-based routing (ANBR), which can naturally enable consumers to retrieve the closest copy of information. In this network, each piece of data can be cached at one IOI at most once, which greatly improves the efficiency of cache usages. The consumers first try to retrieve the data in the local IOI, and then try to globally retrieve it from the closest IOI, holding the copy of the data if necessary. We investigate the impact from the key factor, IOI size, to the energy consumption of ANBR. It shows that energy consumption first decreases and then increases as the IOI size increases, and the optimized IOI size can be found for deployment. Furthermore, we study the relation between the optimized IOI size and the average retrieval times for the data. The result shows that the optimized IOI size increases as the average retrieval times increase. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> There is a growing trend towards attacks on database privacy due to great value of privacy information stored in big data set. Public's privacy are under threats as adversaries are continuously cracking their popular targets such as bank accounts. We find a fact that existing models such as K-anonymity, group records based on quasi-identifiers, which harms the data utility a lot. Motivated by this, we propose a sensitive attribute-based privacy model. Our model is the early work of grouping records based on sensitive attributes instead of quasi-identifiers which is popular in existing models. Random shuffle is used to maximize information entropy inside a group while the marginal distribution maintains the same before and after shuffling, therefore, our method maintains a better data utility than existing models. We have conducted extensive experiments which confirm that our model can achieve a satisfying privacy level without sacrificing data utility while guarantee a higher efficiency. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> VI. ISSUES AND FUTURE DIRECTIONS FOR BIG DATA-DRIVEN MOVING OBJECTS <s> With the development of the latest technologies and changes in market demand, the wireless multi-sensor system is widely used. These multi-sensors are integrated in a way that produces an overwhelming amount of data, termed as big data. The multi-sensor system creates several challenges, which include getting actual information from big data with high accuracy, increasing processing efficiency, reducing power consumption, providing a reliable route toward destination using minimum bandwidth, and so on. Such shortcomings can be overcome by exploiting some novel techniques, such as clustering, data fusion, and coding schemes. Moreover, data fusion and clustering techniques are proven architectures that are used for efficient data processing; resultant data have less uncertainty, providing energy-aware routing protocols. Because of the limited resources of the multi-sensor system, it is a challenging task to reduce the energy consumption to survive a network for a longer period. Keeping challenges above in view, this paper presents a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system. After the selection of cluster heads and member nodes, the proposed data fusion technique is used for partitioning and processing the data. The proposed scheme efficiently reduces the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. Afterward, the routing technique is provided based on the layered architecture. The proposed layered architecture efficiently minimizes the routing paths toward the base station. Comprehensive analysis is performed on the proposed scheme with state-of-the-art centralized clustering and distributed clustering techniques. From the results, it is shown that the proposed scheme outperforms competitive algorithms in terms of energy consumption, packet loss, and cluster formation. <s> BIB007
In the context of V2V communication and BD routing, the use of technology such as GPS, sensors, and other IoT devices provides the important data for analysis. However, using a large number of these devices not only incurs huge cost but also results in redundant data. Another interesting aspect is related to the application of BD analysis on social media. Social media provides diverse information including audio, videos, messages on the fly, and other information generated by sensors attached with the mobile devices. Moreover, social media applications are one of the major sources for BD analysis but there are certain inherited challenges associated with this type of BD. Firstly, it is important to get huge number of users providing reliable data. At the same time, it is required to address the issues related to the security and privacy of users' data BIB002 ,  . The anonymity of the data must be protected in such a way that not only the data makes sense but it also preserves the identity of the subjects BIB006 , BIB005 . At some point of the time, the analysis of data provides useful information such as traffic routes and congestion notification. The model is a dynamic people-centric model. As discussed in previous sections, data is the main element in any analysis framework. The accuracy and efficiency of data collected from different sources are extremely crucial for the analysis phase as well as the design of routing algorithms. While using clustering and data fusion techniques, the energy consumption is an important concern for the sensory devices BIB007 , BIB003 . Apart from energy consumption, latency and delay must be in considerable range for fast processing of data. Thus, the designed algorithms must be scalable and should have low time and space complexity BIB004 . In order to reduce the network contention, dynamic routing algorithms must be deployed instead of static ones BIB001 . In the following subsections, we will discuss some important problems related to the utilization of learning-based systems for moving objects and identify areas for future research as shown in figure 6 . The figure shows the learning and cost issues which are explained in detail in the next sub-sections.