Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Traffic information system may produce massive and complex traffic data with the process of collecting real-time original GPS (Global Positioning System) data, matching positions to a map and generating traffic flow information, which brings great markets for even-worse traffic condition in China. Howerver, several issues would occur when we reuse these massive traffic data for history data mining applying on-hand database management tools or traditional data processing apporaches, such as massive storage, high performance processing, open interface. "Big Data" system usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, manage, and process the data within a tolerable elapsed time. With this difficulty and the advantage of "Big Data", we schemed RTIC-C system to handle sensemaking over large quantities of traffic data based on cloud computing technique. RTIC-C designs a distributed data management service to support large scale of data storage, a parallel distributed computing framework for diverse kinds of mining applications based on Map-Reduce mechanism, a restful Web services interface to support third-party mining applications. Experiments on a massive traffic data sets showed that RTIC-C achieves considerable performance comparing with traditional traffic data mining applications. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> In this article we demonstrate that spatial big data can play a key role in many emerging wireless networking applications. We also argue that spatial and spatiotemporal problems have their own very distinct role in the big data context compared to the commonly considered relational problems. We describe three major application scenarios for spatial big data, each imposing specific design and research challenges. We then present our work on developing highly scalable parallel processing frameworks for spatial data in the Hadoop framework using the MapReduce computational model. Our results show that using Hadoop enables highly scalable implementations of algorithms for common spatial data processing problems. However, development of these implementations requires significant specialized knowledge, demonstrating the need for development of more user-friendly alternatives. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Big-Data applications need to shuffle their data over a network in a very large amount. The ability to efficiently allocate network resources to quickly finish the shuffle greatly affects the overall processing speed of Big-Data applications. The recent emergence of software-defined networking (SDN) technology makes it easier for the network to integrate with a Big-Data application. The network can allocate network resources based on an application's run-time requirements to accelerate the speed of Big-Data delivery. In this paper, we used Hadoop as a Big-Data application platform and designed an application-aware SDN routing scheme for Hadoop to accelerate its MapReduce data shuffling over a network. Our results show that our method can outperform the ECMP-RR scheme by up to 20% and outperform the Spanning Tree method and the Floodlight OpenFlow controller by up to 70%. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Social media can be valuable in transportation research, as data collected contain information for mobility patterns and behavioural characteristics that could not be previously obtained. This paper introduces a methodology for the collection and analysis of data from Social Media, for transportation-related uses, first in a generic methodological framework and then particularly for Twitter. The data collection methodology is applied using Twitter and some initial descriptive statistics are presented concerning the characteristics of users and their posts. Results from spatial analysis is presented, conducted using data from the geotagged service of Twitter. The results of the analysis present trends on the use of Twitter for leisure activities and a correlation between high income areas and number of tweets posted. Finally, the potential of using data from Social Media is discussed with the issues that arise. <s> BIB004 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> In D2D communications, random contacts can be utilized to exchange data among nodes without the support from infrastructures or central control units. Because of the huge quantity and high mobility of the nodes, the scarcity of the available spectrum severely limits the data delivery capacity in D2D communications. CR technology gives D2D the ability to use idle licensed radio spectra from licensed networks to improve data delivery capacity. The advantages of opportunistic data delivery and CR technology make D2D communications an alternative that provides a complementary technology for big data applications. However, efficient routing algorithm design in D2D communications with CRD2D is nontrivial due to the spatial, temporal, and spectrum limitations introduced by node mobility and the available spectrum bands. This article investigates and analyzes the latest routing algorithms for D2D communications and CR networks. Taking advantage of the integration of D2D and CR techniques, a routing framework with social awareness for big data applications is proposed, which employs the regularities of nodes' mobility and spectrum mobility to improve the performance of data delivery. Open research issues for big data routing in CRD2D networks are also addressed. <s> BIB005 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community-contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community-contributed photos. We map both user's and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities. <s> BIB006 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. Furthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. Finally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naive Bayes (NB), Random Forest (RF), and C4.5. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.59% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 3.34% and 6.00% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches. <s> BIB007 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Cities are growing rapidly, requiring effective plans for natural disasters and other vulnerabilities. Urban risk analytics can play a significant role in enabling dynamic and timely decision-making for risk management in cities. The authors' cloud-based general framework facilitates effective urban risk analytics over big city data. <s> BIB008 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Car occupancy rates (travelers per vehicle) are currently very low in most developed countries, for example, on average between 1.15 and 1.25 in Australia. Enabling shared rides on short notice can be an effective solution to counter the problem of increasing traffic through the use of the untapped transportation capacity. Common inhibitors for the uptake of ride sharing services are privacy and safety concerns. We present an approach to ride sharing where the pick up/drop off locations for passengers are selected from a fixed set, which has the advantage of increased safety through video surveillance. We present a scheme that chooses optimally fixed locations of Pick up Points (PuPs) and aims to maximize the car occupancy rates while preserving user privacy and safety. Our method enhances privacy as the users do not need to provide their precise home/work locations. We have extended the well studied 1-coverage problem, i.e., to cover an area with the minimum number of circles of a given radius [1] , to road networks. The challenges for road networks are the varying population densities of suburbs which requires circles of different radii. The aim is to ensure that every point of a city's area is covered by at least one PuP while minimizing the total number of PuPs. By ensuring that we have different circle radii for PuPs the anonymity of individuals is the same throughout. Using Voronoi diagrams we present a k -anonymity model that guarantees a minimum number of individuals covered by every PuP. Our problem is a multi objective problem where we aim to maximize coverage, k -anonymity and privacy provided by the system to its users while facilitating ride sharing. Through greedy randomized adaptive search procedure (GRASP) we find out the Pareto front of solutions and evaluate their impact on ride sharing. <s> BIB009 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <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> BIB010 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> I. INTRODUCTION <s> Spectrum handoff plays an important role in spectrum management as it is the process of seamlessly shifting the on-going transmission of a secondary user (SU) to a free channel without degrading the quality of service. In this paper, we develop an adaptive handoff algorithm that allows an SU to detect the arrival of a primary user (via sensing) and adapt to a reactive or a proactive handoff strategy accordingly. The adaptive handoff scheme first allows an SU to decide whether to stay and wait on current channel or to perform handoff. Then, in case of handoff, an SU intelligently shifts between proactive or reactive handoff modes based on primary use (PU) arrival rate. Further, a PU prioritized Markov approach is presented in order to model the interactions between PUs and SUs for smooth channel access. Numerical results show that the proposed handoff scheme minimizes the blocking probability, number of handoffs, handoff delay and data delivery time while maintaining channel utilization and system throughput at maximal level compared to simple reactive and proactive schemes. <s> BIB011
The enormous spread of low cost sensing devices and dramatic drop in storage cost of the data has resulted in extraction of valuable information that is used for the efficient design of any system. The volume, velocity, and variety of such big data (BD) generation and processing is unprecedented. The benefits of BD processing can be seen in a variety of applications such as device to device (D2D) networks BIB005 , urban planning BIB008 , traffic safety BIB009 and Intelligent transport system (ITS) BIB010 . There are various sources that contribute to the generation of BD ranging from social media, global positioning maps, electronic and marketing apps (including e-marketing and e-health), global climate models, and vehicle-related information. The importance and handling of such BD become more significant and complex if the objects (generating data) are highly mobile and spatially located at a distance. This type of data is mostly referred to as spatial big data (SBD) BIB002 , BIB010 . One of the biggest challenges for SBD is the dynamic nature of spatially distributed devices and objects especially with the availability of 4G-LTE (long term evolution) services around the globe BIB011 . The limitation of spectral efficiency for the 4G physical layer results in the utilization of highly dynamic and resource efficient networks such as D2D BIB005 . In addition, the presence of navigation services such as Google Maps has started a new era of GPS (global positioning system)-based communications. Therefore, this highly dynamic nature of data requires us to develop real-time solutions in order to track the moving objects BIB010 . Another vital issue is to efficiently 'route' the SBD BIB006 . This means routing the traffic patterns generated by the movements of objects (such as vehicles and humans) from one location to another. Such kind of information is highly random and quite difficult to manage. Another perspective on routing is to handle the amount of big data generated by modern day networks such as clouds and software defined networks (SDNs) BIB003 . In addition, an important phenomenon for BD and SBD is extracting the appropriate conclusion by properly analyzing the collected data. The exploitation of collected data has always received considerable attention by academia and industry, however, huge volume, high velocity, and large variety of BD push the limits of the existing storage and processing systems. Moreover, it is difficult to apply the existing statistical techniques on this huge data due to several reasons such as the biasness. The collected data consists of both structured and unstructured information that might be useful for a specific scenario only BIB004 . Therefore, it is highly important to develop modern techniques and methods that assist in processing these huge data streams. There are different dimensions of research in the area of BD, but in case of BD analysis for moving objects, it mainly relies on ad-hoc solutions BIB007 , BIB001 . The emphasis stays on achieving certain predefined objectives (such as traffic flow predictions, mining data from sensors, etc.) which may result in very limited applications and range of scenarios. Currently, there are variety of sources for data generation in moving objects including social media, video devices, sensors, and GPS, however, it is not enough to rely only on these sources for extremely sensitive decisions such as real-time traffic flow, routing, and ITS. In addition, the new trend in moving objects is more towards proactive control such as accurate prediction of congestion or traffic accidents. Therefore, it is crucial to understand the inherent mechanism of moving objects using both historical as well as real data.