Visually exploring movement data via similarity-based analysis

In a cloud environment, resources should be acquired quickly and automatically released at runtime. Traditional trajectory data partitions, indexing and query processing techniques are extended, so that they can take advantage of the cloud of large clusters of highly parallel processing capabilities. There are ways with trajectory data in the cloud database query processing. The advanced sensing techniques available today caused the existence of many types of trajectory datasets whose study can form a strong basis for decision making regarding a data context. Trajectory data generally includes trajectory classification, trajectory clustering, and trajectory associations and so on. All these tools need trajectory similarity measures to get comparisons in trajectory data. Some of the popular existing methods of trajectory similarity include Euclidean distance, semantic distance and there near variants. Each variant of these techniques exhibits notable differences in measurement of similarity and computational difficulty. This paper targeted to deal with this situation with the goal of lower computational efforts in trajectory similarity computations. A two‐phase trajectory similarity measure is defined. The first phase does point level analysis of trajectory points representing all trajectories and the following phase use the analysis to find the similarities. Using a hybrid similarity framework, new means for trajectory clustering and trajectory query processing are developed. From trajectory analysis followed by trajectory clustering, evaluation of cluster quality and processing of trajectory query are undertaken in this paper in different ways from the existing methods. All the proposed developments are tested with trajectory datasets with multiple attributes and the results are more favour to proposed framework than the existing ones.

With the Widespread use of Internet of Thing (IoT) technology and the extensive application of wireless communication technologies, innovational changes have been made in all walks of life. In the field of navigation, Automatic Identification System (AIS) is widely equipped in vessels, which can obtain continuous position data from vessels and assemble them to vessel trajectories. While the massive vessel trajectory data obtained through AIS make great difficulty in maritime data analysis, however, they also make the vessel trajectory similarity measure become a hot topic in spatial database research. In recent years, there have been many studies about trajectory similarity measure of maritime, but these methods only consider the calculation of relative position between trajectory points, and these methods are inefficient with a low accuracy of trajectory similarity measure. In this study, we propose a novel approach called Convolutional Autoencoder (CAE), for measuring vessel trajectory similarity based on Convolutional Neural Network (CNN) and Autoencoder (AE). In this model, the vessel trajectory was gridded, and the grid-based Convolutional Autoencoder was proposed to extract the trajectory data as feature vectors to represent the original trajectories. Then the low-dimensional feature vectors were used for estimating the original trajectory similaiity. In addition, an experiment was conducted to prove the effectiveness of our model. Compared with the Frechet distance and Dynamic Time Warping (DTW)distance with the CAE, the results prove that CAE is capable of more efficient trajectory similarity computation and search.

Trajectory clustering has played an essential role in trajectory mining tasks. It serves in a wide range of real-life applications, including transportation, location-based services, behavioral study, and so on. To support trajectory clustering analytics, a plethora of trajectory clustering methods have been proposed, which mainly extend traditional clustering algorithms by using spatio-temporal characteristics of trajectories. However, existing traditional trajectory clustering approaches based on raw trajectory representation highly rely on hand-craft similarity metrics, and can not capture hidden spatial dependencies in trajectory data, which is inefficient and inflexible for clustering analysis. To this end, we propose an end-to-end deep trajectory clustering framework via self-training, termed as E2DTC, inspired by the data-driven capabilities of deep neural networks. E2DTC does not require any additional manual feature extraction operations, and can be easily adapted for trajectory clustering analytics on any trajectory dataset. Extensive experimental evaluations on three real-life datasets show that our framework E2DTC achieves superior accuracy and efficiency, compared with classical clustering methods (i.e., K-Medoids) and state-of-the-art neural-network based approaches (i.e., t2vec).

Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node‐Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time‐Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T‐Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%.

Tremendous growth of Location-based technologies resulted in the generation of a huge volume of spatial data, which needs to be analyzed to get potentially important patterns. The spatial patterns extracted can be used to design a better infrastructure ensuring reliable service coverage. Trajectory data is one variant of spatial data that are generated by moving objects travelling across. It is represented as a sequence of spatial coordinates (latitude, longitude) of a location. Trajectory clustering tries to group similar spatial data points to extract the most common movement behaviors. Trajectory data poses major challenges including uncertainty, sampling rate, representation, relationships, spatial autocorrelation, serialization, redundancy, and triviality, which makes it hard to apply traditional clustering algorithms over trajectory data. In this paper, K-Means and DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithms are analyzed using different similarity measures like Euclidean, Hausdorff and Haversine distances with the help of index measures say Adjusted Rand Index (ARI) and Fowlkes-Mallows scores (FMS). Experiment is carried out over two different trajectory datasets and it is proved that usage of Haversine distance for clustering is efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data

The popularization of mobile devices and satellite navigation systems has brought new opportunities and challenges to many location-based services (LBSs), intelligent transport systems (ITSs) and fleet management systems. Those systems store trajectory data in moving object databases and trajectory data warehouses. Trajectory datasets can be used in many data mining tasks, e.g., for mining traffic patterns, frequently visited places, and so on. However, such data mining tasks introduce security and location privacy threats to companies (data owners) and mobile objects that generate trajectories. In this paper, in order to overcome the location privacy threats, we present a technique that blurs trajectories according to user-defined privacy profiles and a data mining algorithm called MCSPP that is capable of mining continuous sequences from blurred trajectories. To test that our approach works correctly, we have implemented an experimental environment. Moreover, the conducted experiments have shown a good performance and scalability of the MCSPP algorithm.

Similarity search finds similar objects for a given query object based on a certain similarity metric. Similarity search in metric spaces has attracted increasing attention, as the metric space can accommodate any type of data and support flexible distance metrics. However, a metric space only models a single data type with a specific similarity metric. In contrast, a multi-metric space combines multiple metric spaces to simultaneously model a variety of data types and a collection of associated similarity metrics. Thus, a multi-metric space is capable of performing similarity search over any combination of metric spaces. Many studies focus on indexing a single metric space, while only a few aims at indexing multi-metric space to accelerate similarity search. In this paper, we propose DESIRE, an efficient dynamic cluster-based forest index for similarity search in multi-metric spaces. DESIRE first selects high-quality centers to cluster objects into compact regions, and then employs B+-trees to effectively index distances between centers and corresponding objects. To support dynamic scenarios, efficient update strategies are developed. Further, we provide filtering techniques to accelerate similarity queries in multi-metric spaces. Extensive experiments on four real datasets demonstrate the superior efficiency and scalability of our proposed DESIRE compared with the state-of-the-art multi-metric space indexes.

A major challenge of the contemporary information age is the overwhelming and increasing data amount, especially when looking for specific information. Searching for relevant information is no longer manually possible, but has to rely on automatic methods, specifically, similarity search. From a formal perspective, similarity search can be seen as the problem of finding entities, which are considered to be similar to a query with respect to certain describing features. The question which features or which weighted combination of features to use for a given query creates a need for semi-automatic methods to address the needs of diverse users. Furthermore, the quality of the results of a similarity search is more than effectiveness, measured by precision and recall. The user ideally needs to trust the results and understand how they were computed. We propose to apply Visual Analytics methodologies, for synergistic cooperation of user and algorithms, to integrate three key dimensions of similarity search: users, tasks, and data for effective search. However, there exists a gap in knowledge how user, task as well as the available data influence each other and the similarity search. In this concept paper, we envision how Visual Analytics can be used to tackle current challenges of similarity search.

A Self-structuring Data Learning Algorithm was introduced and has been implemented in our prior work. While the algorithm and the software package are advancing, it has been tested with both synthetic data and real-world data. After encouraging synthetic data test results, real-world data testing also shows promising outcomes while posing some challenges such as object occlusion, objects merging, and going into and emerging from under bridge. To resolve such problems, a multi-int solution is proposed. One of the key features in this solution is similarity measure. There are different types of similarity measures. In this paper, we primarily focus on aerial images similarity measure. The images we worked on presents unique challenge in similarity measure because of small object in distance and large area image, which consequently provides limited information. To deal with this difficulty, we have developed 14 different similarity metrics by employing Normalized Cross Correlation method, Sum of Squared Differences, and overlapping and colors of pixels. We used object tracking ability to evaluate the metrics. The simulation results show each metric has some advantages and disadvantages. In attempt to improve tracking capability, we imposed some metrics thresholds in addition to the image similarity metrics. Such metrics thresholds were learned from labeled data with valuation of tracking correctness. To further enhance tracking ability, speed similarity was incorporated on top of two features mentioned above. More improvement can be done by studying robustness of images similarity metrics and using tracks fusion.

Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

Similarity is important in knowledge discovery. Cluster analysis, classification, and granulation each involve some notion or definition of similarity. The measurement of similarity is selected based on the domain and distribution of the data. Even within a specific domain, some similarity metrics may be considered more useful than others. There is an amount of uncertainty in quantitatively measuring the similarity between records of mixed data. The uncertainty develops from the lack of scale that both nominal and ordinal data have. Rough set theory is one tool developed for handling uncertainty. Rough sets can be used in dissimilarity analysis of qualitative data. It would seem that rough sets could be applied in measuring similarity between records containing both quantitative and qualitative data for the purpose of clustering the records.

Facilitated by ubiquitous computing techniques and the wide development of location-based service (LBS) and smart card systems, users' trajectory data, which is usually attached with user activities, are recorded more and more easily. Inappropriate publishing these trajectory data may jeopardize users' privacy. However, existing works on privacy-preserving trajectory data publishing fail to consider the activity information. In this paper, we propose a scheme under an existing privacy framework for solving the problem that how to preserve users' privacy when publishing an activity trajectory dataset. In this scheme, it should be ensured that the posterior probability about users' privacy inferred by adversaries is not too much larger than the prior one. We assume that user behaviors could be described by a Markov model and that the adversary has a knowledge of both the user model and our privacy preserving mechanism. The adversary could infer users' predefined sensitive information by Bayesian inference. Then we propose a dataset publishing algorithm, PPPAT, which preserves user privacy. It iterates on all the events of a given user to decide whether they could be published or not. Finally, we evaluate our algorithm by conducting experiments on two datasets, compared with several baselines. The results verify the effectiveness of our algorithm and the utility of outputted activity trajectory dataset.

BackgroundInteraction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied.ResultsThe performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests.ConclusionA general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit.