Predicate Priority Based Event Matching Algorithm in Publish/Subscribe System

Publish/subscribe(pub/sub) systems are widely used in large-scale messaging systems due to their asynchronous and decoupled nature. With the population of pub/sub cloud services, the privacy protection problem of pub/sub systems has started to emerge, and events and subscriptions are exposed when executing event matching on untrustworthy cloud brokers. However, as the number of subscriptions increases, the effectiveness of the previous confidentiality protection approaches declines drastically. In this paper, we propose SBM (scalable blind matching), an effective confidentiality protection scheme for pub/sub systems. To the best of our knowledge, SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its scalability. In this scheme, SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA, whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching performance. The experiment demonstrates that this method has better matching performance compared to others: the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000, and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

Based on analyzing the traditional image matching algorithms, a fast image matching method, which integrates improved sequential similarity detection algorithm (SSDA) and wavelet transformation, is presented in this paper. Firstly, some basic principles of SSDA image matching and wavelet transformation are introduced. Secondly, according to the shortcoming of traditional SSDA algorithm, a new SSDA algorithm is studied for improving the determining velocity of the threshold in SSDA searching match. By integrating wavelet transformation, an integrated matching algorithm is given out. Finally, the new method is compared with the traditional matching algorithms in experiments. The experimental results show that the new method has higher efficiency and accuracy in image matching and is suitable for the realization of real-time underwater navigation.

Current twig pattern matching algorithms are based on the preorder traversal, which leads to numerous redundant nodes. In this paper, a new rule matching algorithm based on the post-order-traversal twig pattern is proposed. In the new algorithm, Post-order traversal to the request tree is used which can promptly suspend the matching of redundant nodes. Analysis and experiments show that the new algorithm effectively reduces the scale of the output, decreases the elapsed time of matching, avoids lots of redundant paths and nodes and increases the efficiency of the rules matching.

Structured publish/subscribe (pub/sub) is a promising technique adopted on kinds of vehicle applications of Internet of industrial vehicles (IoIV), which uses Boolean expressions to capture the items with thousands of different attributes, values and spatial locations, and then processes and analyzes the vast amounts of data collected to obtain users' interests. However, existing pub/sub work with Boolean expressions either ignores spatial requirement or focuses on Euclidean space. This paper aims to fill this gap by addressing the issue of fog-based spatial-textual pub/sub problem with Boolean expressions in IoIV. A novel hybrid index called RnetBE is proposed, which exquisitely organizes traffic network structure, Boolean expressions, and spatial information of subscriptions. And RnetBE can prune huge numbers of unqualified subscriptions based on both spatial constraint and Boolean expressions, thus achieving high efficiency in indexing and matching. Moreover, range-tree deletion and orderly group processing optimization techniques are proposed to save storage space and further improve the subscription pruning efficiency. Simulation results show that RnetBE and the proposed algorithm are efficient in terms of memory consumption and matching time.

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

Contour segment (CS) is the fundamental element of partial boundaries or edges in shapes and images. So far, CS has been widely used in many applications, including object detection/matching and open curve matching. To increase the matching accuracy and efficiency, a variety of CS descriptors have been proposed. A CS descriptor is formed by a chain of boundary or edge points and is able to encode the geometric configuration of a CS. Because many different CS descriptors exist, a structured overview and quantitative evaluation are required in the context of CS matching. This paper assesses 27 CS descriptors in a structured way. Firstly, the analytical invariance properties of CS descriptors are explored with respect to scaling, rotation and transformation. Secondly, their distinctiveness is evaluated experimentally on three datasets. Lastly, their computation complexity is studied. Based on results, we find that both CS lengths and matching algorithms affect the CS matching performance while matching algorithms have higher affection. The results also reveal that, with different combinations of CS descriptors and matching algorithms, several requirements in terms of matching speed and accuracy can be fulfilled. Furthermore, a proper combination of CS descriptors can improve the matching accuracy over the individuals.

This paper designs and implements a multi-dimensional dynamic weight matching algorithm based on Python for intelligent matching of sustainable development education resources. The algorithm constructs a multi-dimensional feature system covering knowledge content, educational goals, and audience characteristics, introduces a dynamic weight adjustment mechanism, and can update weights in real time based on user feedback and resource usage data. The experiment uses 12,000 educational resources and 5,000 user demand data sets. Compared with the content-based matching algorithm and collaborative filtering algorithm, the accuracy of this algorithm reaches 89.2%, the recall rate is 87.6%, and the F1 value is 88.4%, which are 12.3%, 10.5%, 11.4% and 9.8%, 8.7%, 9.2% higher than the comparison algorithm respectively; the weight update response time is 0.3 seconds, which is 62.5% shorter than the comparison algorithm, covering 92.3% of scarce resources, and processing 10,000 data for a single match takes 1.2 seconds. The results show that the algorithm can improve matching accuracy, efficiency and resource coverage, and provide strong support for sustainable development education.

Aiming at the problems of large calculating scale and high complexity in Scale Invariant Feature Transform (SIFT) feature matching algorithm, this paper presents an improved SIFT feature matching algorithm based on quasi Euclidean distance.The traditional Euclidean distance can only calculate out the variance of the two images to the corresponding pixel, so when a slight shift or distortion occurs in the image, it may produce a large deviation.The quasi Euclidean distance instead of Euclidean distance is as the similarity measure of feature descriptors to improve the SIFT feature matching.It reduces the dimensions of SIFT feature vector to improve the efficiency of feature matching.Experimental results show that under the condition of keeping the image matching rate and algorithm robust, the method can not only improve the matching accuracy but also shorten the matching time.The new algorithm has better performance than traditional algorithm, it is possible and valid, which are useful for the fields of image recognition, image reconstruction, etc.

The matching and fusion of local maps generated by multiple robots can greatly enhance the performance of relative localization and collaborative mapping. Currently, existing semantic matching methods are partly based on classical iterative closet point, which typically fails in cases with large initial errors. What’s more, current semantic matching algorithms have high computation complexity in optimizing the transformation matrix. To address the challenge of large initial errors and low matching efficiency, this article proposes a novel large convergence region semantic map matching algorithm. The key novelty of this work is the designing of the initial transformation optimization algorithm and the probabilistic registration model to increase the convergence region. To reduce the initial error before the iteration process, the initial transformation matrix is optimized by estimating the credibility of the data association. At the same time, a factor reflecting the uncertainty of the initial error is calculated and introduced to the formulation of the probabilistic registration model, thereby accelerating the convergence process. The proposed algorithm is performed on public datasets and compared with existing methods, demonstrating the significant improvement in terms of matching accuracy and robustness.

Method for rapidly generating urban damage scenarios under non-uniform ground motion input based on matching algorithms and time history analyses

With the continuous advancement of the Internet and information technologies, the capacity for development and integration of vehicle and cargo resources has been significantly enhanced, driving the rapid emergence of vehicle–cargo matching platforms. Serving as critical intermediaries between vehicle owners and cargo owners, vehicle–cargo matching platforms effectively address key challenges in traditional logistics, such as low matching efficiency and information asymmetry. As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at the current stage, vehicle–cargo matching platforms rarely promote freight pooling services, leading to resource underutilization. Due to the freight pooling matching problem involving the combination and allocation of multiple vehicle owners and cargo owners, which is highly complex, few scholars have conducted research on such issues. The lack of coordinated optimization in matching models may result in inefficiencies, and the limited consideration of individual user preferences can lead to low user satisfaction. Therefore, this paper focuses on the freight pooling matching problem in vehicle–cargo matching platforms. To improve matching efficiency and fully consider user preferences, the theory of stable matching is introduced into the freight pooling matching problem. It defines the concepts of combination preferences and group stability based on combination preferences, establishes a group stable matching model for the freight pooling business of vehicle–cargo matching platforms, and verifies the stability of the model through theoretical proof. Since this model is a mixed-integer linear programming model with relatively few decision variables but a large number of constraints, this paper introduces the cutting-plane algorithm. Based on the characteristics of the problem, the algorithm is improved by removing ineffective constraints and only using key constraints, significantly reducing computational complexity, optimizing the solving process, and greatly improving the model’s solution efficiency. This approach aligns well with the characteristics of the vehicle–cargo freight-pooling matching model. The research results indicate that the group stable matching model significantly improves platform revenue, vehicle owners’ profits, and cargo owners’ satisfaction across various supply and demand scenarios. Additionally, the cutting-plane algorithm reduces computation time by 97% and decreases the number of constraints during the solving process by 99%. The stable matching theory and solution algorithm proposed in this paper can provide users with precise matching schemes, significantly improving matching efficiency, user satisfaction, platform revenue and platform competitiveness. It demonstrates high innovation and practical application value.

QoS is a very important issue in cognitive radio systems. We present and discuss spectrum matching algorithms to support QoS for cognitive radio users. This work is carried out in the context of a dynamic changing spectrum. In this paper, we propose efficient spectrum matching algorithms, which have low-complexity and quite good performance, based on statistical characteristics of spectrum bands. Those are classified random matching algorithm and greedy algorithm. The classified random matching algorithms would match the most suitable spectrum class to a proper user and randomly choose a spectrum within the class. And, using greedy algorithm, the best spectrum out of available spectrum bands will be chosen whenever a new user joins. The proposed algorithms provide QoS for cognitive radio systems through the reduction of spectrum handoff probability and improvement of fairness. Performance analysis and simulation results validate the efficiency of the proposed algorithms.

Performance analysis of PV pumping systems using switched reluctance motor drives

Performance analysis of photovoltaic-powered water-pumping systems using switched reluctance motor drives

The problems of high dimensional feature vectors lead to large data calculating and time consuming in Scale Invariant Feature Transform (SIFT) feature matching. This paper combines the SIFT and Locality Preserving Projection (LPP) as the LPP-SIFT algorithm. First, use the SIFT algorithm to extract feature point vectors, then by the linear LPP dimensionality reduction method, reduce the amount of data. Use the Euclidean distance similarity criteria to achieve the coarse matching feature point vectors. Then re-match each pair of the match points by the way of the Gaussian neighborhood weighted average to get rid of the mismatch points. The experimental results show that the improved LPP-SIFT algorithm can reduce the amount of data, enhance the real-time and has a high matching accuracy and matching efficiency.