The vehicle location prediction based on their spatial and temporal information is an important and difficult task in many applications. In the last few years, devices, such as connected vehicles, smart phones, GPS navigation systems, and smart home appliances, have amassed the large stores of geographic data. The task of leveraging this data by employing moving objects database techniques to predict spatio-temporal locations in an accurate and efficient fashion, comprising a complete trajectory remains an actively researched area. Existing methods for frequent sequential pattern mining tend to be limited to predicting short-term partial trajectories, at extremely high computational costs. In order to address these limitations, we designed a prefix-projection-based trajectory prediction algorithm called PrefixTP, which contains three essential phases. First, data collection, connected vehicles equipped with sensors comprise a vehicle grid and generate copious amounts of spatio-temporal data, in order to communicate and share traffic information. Second, model training, examining only the prefix subsequences, and projecting only their corresponding postfix subsequences into projected sets. Finally, trajectory matching, recursively finding postfix sequences meeting the requirement of minimum support count, and outputting the most frequent sequential pattern as the most probable trajectory. Fundamentally, PrefixTP supports three trajectory matching strategies which encompass all possibilities of prediction. Extensive experiments were conducted using real world GPS data sets, and the results show, when comparing predicted complete trajectories against partial short-term trajectories with a guarantee of real-time forecasting, that PrefixTP outperforms first-order, second-order Markov models, and Apriori-based trajectory prediction algorithm.

Community discovery plays an essential role in the analysis of the structural features of complex networks. Since online networks grow increasingly large and complex over time, the methods traditionally used for community discovery cannot efficiently handle large-scale network data. This introduces the important problem of how to effectively and efficiently discover large communities from complex networks. In this study, we propose a fast parallel community discovery model called picaso (a parallel community discovery algorithm based on approximate optimization), which integrates two new techniques: (1) Mountain model, which works by utilizing graph theory to approximate the selection of nodes needed for merging, and (2) Landslide algorithm, which is used to update the modularity increment based on the approximated optimization. In addition, the GraphX distribution computing framework is employed in order to achieve parallel community detection over complex networks. In the proposed model, clustering on modularity is used to initialize the Mountain model as well as to compute the weight of each edge in the networks. The relationships among the communities are then simplified by applying the Landslide algorithm, which allows us to obtain the community structures of the complex networks. Extensive experiments were conducted on real and synthetic complex network datasets, and the results demonstrate that the proposed algorithm can outperform the state of the art methods, in effectiveness and efficiency, when working to solve the problem of community detection. Moreover, we demonstratively prove that overall time performance approximates to four times faster than similar approaches. Effectively our results suggest a new paradigm for large-scale community discovery of complex networks.

The existing approaches for trajectory prediction (TP) are primarily concerned with discovering frequent trajectory patterns (FTPs) from historical movement data. Moreover, most of these approaches work by using a linear TP model to depict the positions of objects, which does not lend itself to the complexities of most real-world applications. In this research, we propose a three-in-one TP model in road-constrained transportation networks called TraPlan. TraPlan contains three essential techniques: 1) constrained network R-tree (CNR-tree), which is a two-tiered dynamic index structure of moving objects based on transportation networks; 2) a region-of-interest (RoI) discovery algorithm is employed to partition a large number of trajectory points into distinct clusters; and 3) a FTP-tree-based TP approach, called FTP-mining, is proposed to discover FTPs to infer future locations of objects moving within RoIs. In order to evaluate the results of the proposed CNR-tree index structure, we conducted experiments on synthetically generated data sets taken from real-world transportation networks. The results show that the CNR-tree can reduce the time cost of index maintenance by an average gap of about 40% when compared with the traditional NDTR-tree, as well as reduce the time cost of trajectory queries. Moreover, compared with fixed network R-Tree (FNR-trees), the accuracy of range queries has shown an on average improvement of about 32%. Furthermore, the experimental results show that the TraPlan demonstrates accurate and efficient prediction of possible motion curves of objects in distinct trajectory data sets by over 80% on average. Finally, we evaluate these results and the performance of the TraPlan model in regard to TP by comparing it with other TP algorithms.