A GPU-Accelerated Framework for Processing Trajectory Queries

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.

The similarity ainong elemcnts emerges naturally as the most adequate to ask about complex data (such as, multimdia and genomic sequenees among others). There are two basie similarity queries: Range Query and k-Nearest Neighbor Query. The increasing volume of complex data stored in Database Management Systems (DBMS), makes it neeessary to provide support for these data tvpes. One way to support complex data types in current DBMS is to include similarity queries in its query processor, and consequently, in the relational algebra. This fact leacls to produce ways to express such queries in the DBMS language as predicates in select operations. As a consequence, the two basic similarity queries cari be combined in more complex expressions involving boolean conjunctions and disjunctions among them, i.e., complex similarity queries. However, for complex similarity queries to be processed efhciently in a DBMS, it is neeessary to provide support in the optimization and runtime laycrs of the; query proeessing. There are many works involving the development, of algorithms to ariswer specific and simple similarity query whereas there is not a generic algorithm efficiently able to handle complex similarity queries. Furthermore, the similarity query optimization is a topic not frequently explored in the literature. This work establishes a structured rnethod 011 how to analyze complex similarity queries. This method is used to extend the relational algebra through algebraic rules and to determine a small set of algorithms that can be used to answer any complex similarity query. In addition, the proposed method makes it possible to formalize rules for selectivity estimai,ion of these. queries thus assisting cost estimation. To validate the concepts presented, exjjeriments are being performed on real and synthetic data sets that highlight meaningful results. The algebraic rules. algorithms and metrics to est-imate the selectivity can be employed in the optimization process of a DBMS in order to derive efficient complex similarity query execution plans. Therefore, this work deals with essential poiuts that enable the practical use of similarity (jueries in Relational Database Management Systems.

With the development of GPS positioning technology, a large amount of spatiotemporal trajectory data has been generated. Due to the complex structure of trajectory data, which has irregular spatial shapes and continuous temporal sequence attributes, querying massive trajectory data poses certain challenges. The pruning strategy of existing trajectory similarity query methods is not very effective. Even after pruning operations, there are still a large number of trajectories that need to undergo distance calculations to confirm whether they are similar trajectories. This paper proposes multiple local pruning optimization schemes, which maximally reduce the number of trajectories in the candidate set. Specifically, it starts with region pruning in the index space, eliminating index spaces with distances greater than the maximum similarity distance from the query trajectory. Then, it performs distance pruning between trajectories, removing trajectories that do not meet the distance conditions. Finally, it adds a termination condition: when the distance between the current index space and the query trajectory is greater than the maximum similarity distance and the number of trajectories in the result set is K, the loop is exited, and the query process ends. Tests on real datasets demonstrate that the KTSS method outperforms current algorithms of the same type.

In this paper, we propose a new spatio-temporal similarity measure to compute spatio-temporal relevance between two trajectories of moving objects on road networks, which is known as spatio-temporal distance (STDist). In addition, we provide a similar trajectory search algorithm to retrieve similar trajectories based on the proposed measure i.e., a combination of both spatial and temporal properties with respect to the motion of a given query trajectory. The performance evaluation shows that our approach outperforms the existing work in terms of searching similar trajectories of moving objects on road networks.

Metric databases are databases where a metric distance function is defined for pairs of database objects. In such databases, similarity queries in the form of range queries or k-nearest neighbor queries are the most important queries. In traditional query processing, single queries are issued independently by different users. In many data mining applications, however, the database is typically explored by iteratively asking similarity queries for answers of previous similarity queries. In this paper, we introduce a generic scheme for such data mining algorithms and we investigate two orthogonal approaches, reducing I/O cost as well as CPU cost, to speed-up the processing of multiple similarity queries. The proposed techniques apply to any type of similarity query and to an implementation based on an index or using a sequential scan. Parallelization yields an additional impressive speed-up. An extensive performance evaluation confirms the efficiency of our approach.

Many recent database applications need to deal with similarity queries. For such applications, it is important to measure the similarity between two objects using the distance between them. Focusing on this problem, this paper proposes the slim-tree, a new dynamic tree for organizing metric data sets in pages of fixed size. The slim-tree uses the triangle inequality to prune the distance calculations that are needed to answer similarity queries over objects in metric spaces. The proposed insertion algorithm uses new policies to select the nodes where incoming objects are stored. When a node overflows, the slim-tree uses a minimal spanning tree to help with the splitting. The new insertion algorithm leads to a tree with high storage utilization and improved query performance. The slim-tree is a metric access method that tackles the problem of overlaps between nodes in metric spaces and that allows one to minimize the overlap. The proposed is a way to quantify whether a given tree can be improved and also to compare two trees. We show how to use the fat-factor to achieve accurate estimates of the search performance and also how to improve the performance of a metric tree through the proposed algorithm. This paper also presents a new tool in the slim-tree's arsenal of resources, aimed at visualizing it. Visualization is a powerful tool for interactive data mining and for the visual tracking of the behavior of a tree under updates. Finally, we present a formula to estimate the number of disk accesses in range queries. Results from experiments with real and synthetic data sets show that the new slim-tree algorithms lead to performance improvements. These results show that the slim-tree outperforms the M-tree by up to 200% for range queries. For insertion and splitting, the minimal-spanning-tree-based algorithm achieves up to 40 times faster insertions. We observed improvements of up to 40% in range queries after applying the slim-down algorithm.

A GPU's computing power lies in its abundant memory bandwidth and massive parallelism. However, its hardware thread schedulers, despite being able to quickly distribute computation to processors, often fail to capitalize on program characteristics effectively, achieving only a fraction of the GPU's full potential. Moreover, current GPUs do not allow programmers or compilers to control this thread scheduling, forfeiting important optimization opportunities at the program level. This paper presents a transformation centered on Streaming Multiprocessors (SM); this software approach to circumventing the limitations of the hardware scheduler allows flexible program-level control of scheduling. By permitting precise control of job locality on SMs, the transformation overcomes inherent limitations in prior methods. With this technique, flexible control of GPU scheduling at the program level becomes feasible, which opens up new opportunities for GPU program optimizations. The second part of the paper explores how the new opportunities could be leveraged for GPU performance enhancement, what complexities there are, and how to address them. We show that some simple optimization techniques can enhance co-runs of multiple kernels and improve data locality of irregular applications, producing 20-33% average increase in performance, system throughput, and average turnaround time.

This article proposes a robust method for tracking sparse point features that is stable to long video sequences. To improve the stability of feature tracking in a long sequence, we use feature trajectories of tracked points. We predict feature states in the current image frame and compute rough estimates of feature locations. A search window is positioned at each estimated location and a similarity measure is computed within the window. Through the refinement using the similarity measure, an accurate feature position is determined. To reduce false matches, an outlier rejection stage is also introduced. Experimental results from several real video sequences showed that the proposed method stably tracks point features for long frame sequences.KeywordsVideo SequenceCurrent FrameScale Invariant Feature TransformSearch WindowFeature TrackingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The problem of trajectory similarity has been recently attracted research interest considerably, due to its importance in diverse fields. In this work, we study trajectory similarity by attacking the problem taking an information retrieval perspective. Trajectories are first decomposed by using a grid and each trajectory is mapped to a multidimensional space where Latent Semantic Analysis is applied. Distance measures like Euclidean distance or cosine distance are applied to process similarity queries (range queries, k-NN queries). Performance evaluation results, based on real-life data sets, show the simplicity and effectiveness of the proposed scheme.

The information explosion on the Internet has placed high demands on search engines. Despite the improvements in search engine technology, the precision of current search engines is still unsatisfactory. Moreover, the queries submitted by users are short, ambiguous and imprecise. This leads to a number of problems in dealing with similar queries. The problems include lack of common keywords, selection of different documents by the search engine and lack of common clicks etc. These problems render the traditional query clustering methods unsuitable for query recommendations. In this paper, we propose a new query recommendation system. For this, we have identified conceptually related queries by capturing users’ preferences using click-through graphs of web search logs and by extracting the best features, relevant to the queries, from the snippets. The proposed system has an online feature extraction phase and an offline phase in which feature filtering and query clustering are performed. Query clustering is carried out by a new tripartite agglomerative clustering algorithm, Query-Document-Concept Clustering, in which the documents are used innovatively to decouple queries and features/concepts in a tripartite graph structure. This results in clusters of similar queries, associated clusters of documents and clusters of features. We model the query recommendation problem in four different ways. Two models are non-personalized and personalized content-ignorant models. Other two are non-personalized and personalized content-aware models. Three similarity measures are introduced to estimate different kinds of similarities. Experimental results show that the proposed approach has better precision, recall and F-measure than the existing approaches.

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.

Graphics processing units(GPUs) have been increasingly used to accelerate general purpose computations. By exploiting massive thread-level parallelism (TLP), GPUs can achieve high throughput as well as memory latency hiding. As a result, a very large register file (RF) is typically required to enable fast and low-cost context switching between tens of thousands of active threads. However, RF resource is still insufficient to enable all thread level parallelism and the lack of RF resources can hurt performance by limiting the occupancy of GPU threads. Moreover, if the available RF capacity can not fit the requirement of a thread block, GPU needs to fetch some variables from local memory which may lead to long memory access latencies. By observing that a large percentage of computed results actually have fewer significant bits compared to the full width of a 32-bit register for many GPGPU applications, we propose a GPU register packing scheme to dynamically exploit narrowwidth operands and pack multiple operands into a single fullwidth register. By using dynamically register packing, more RF space is available which allows GPU to enable more TLP through assigning additional thread blocks on SMs (Streaming Multiprocessors) and thus improve performance. The experimental results show that our GPU register packing scheme can achieve up to 1.96X speedup and 1.18X on average.

Locality based warp scheduling in GPGPUs

Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model

With the increasing number of vehicles equipped with GPS enabled wireless communication devices, locations and trajectories of vehicles can be collected every time. Finding similar trajectories in massive trajectory data can benefit emerging novel mobile applications, such as carpooling, friend recommendation and traffic analysis. This paper proposes a novel spatio-temporal based trajectory similarity evaluation method. In this method, the significance of each point on the query trajectory can be assigned according to personal preference. Speed factor is also considered in the evaluation approach. Furthermore, most of existing trajectory similarity evaluation methods hold the precondition that the compared trajectories should have the same space and time length. This is not a compulsive condition in our method.