We propose a method for conducting algebraic program analysis (APA) incrementally in response to changes of the program under analysis. APA is a program analysis paradigm that consists of two distinct steps: computing...
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We propose a method for conducting algebraic program analysis (APA) incrementally in response to changes of the program under analysis. APA is a program analysis paradigm that consists of two distinct steps: computing a path expression that succinctly summarizes the set of program paths of interest, and interpreting the path expression using a properly-defined semantic algebra to obtain program properties of interest. In this context, the goal of an incremental algorithm is to reduce the analysis time by leveraging the intermediate results computed before the program changes. We have made two main contributions. First, we propose a data structure for efficiently representing path expression as a tree together with a tree-based interpreting method. Second, we propose techniques for efficiently updating the program properties in response to changes of the path expression. We have implemented our method and evaluated it on thirteen Java applications from the DaCapo benchmark suite. The experimental results show that both our method for incrementally computing path expression and our method for incrementally interpreting path expression are effective in speeding up the analysis. Compared to the baseline APA and two state-of-the-art APA methods, the speedup of our method ranges from 160x to 4761x depending on the types of program analyses performed.
The role mining process, i.e., computing minimal set of roles from a user-permission assignment relation, is rather incremental task. In a usual situation the set of roles is already established, and the existing perm...
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The role mining process, i.e., computing minimal set of roles from a user-permission assignment relation, is rather incremental task. In a usual situation the set of roles is already established, and the existing permissions are evolved according to current needs. Computation of roles from scratch after each small change in the data is inefficient. Surprisingly, no incremental algorithm for role mining problem exists. The paper proposes a first attempt to an incremental algorithm for role mining problem. The algorithm is tested with several real-world datasets. The results show that the proposed algorithm is significantly faster than non-incremental algorithms, and in many cases, produces a smaller number of roles than the non-incremental version of the algorithm. (C) 2020 Elsevier Ltd. All rights reserved.
Recently, Graph based recommendation algorithms have gained more and more attention due to their flexible and unified embedding representation of both users and items as well as the effective modeling of context infor...
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ISBN:
(纸本)9783031441943;9783031441950
Recently, Graph based recommendation algorithms have gained more and more attention due to their flexible and unified embedding representation of both users and items as well as the effective modeling of context information for efficient recommendation. However, most of the existing graph recommendation methods are designed upon the static interaction graph, while neglecting the dynamic evolution of the graph. This paper proposes a lightweight Influence Propagation Model, namely IPM, for efficient recommendation on the dynamic evolving graphs. Specifically, IPM models the accumulating and propagating procedure of influence on the user-item interaction graph to obtain the characteristics of users and items. For dynamic changed edges of the graph, the amount of information about the relevant users and items can be quickly updated by propagating the impact associated with the added interactions. Our model exhibits very efficient performance and comparable recommendation results with the same experimental setup compared to advanced graph recommendation algorithms and dynamic graph embedding algorithms.
In the near future, the internet-of-things (IoT) technology will improve dramatically our daily life as a new pervasive computing paradigm. For the IoT computing, various devices and wireless networks are the hardware...
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In the near future, the internet-of-things (IoT) technology will improve dramatically our daily life as a new pervasive computing paradigm. For the IoT computing, various devices and wireless networks are the hardware infrastructure, and service-oriented architecture (SOA) is a valuable software system that allows heterogeneous devices to interoperate each other. Even though IoT researchers have tackled a number of challenges for service composition, the orchestration techniques on IoT are rarely studied yet. Given a set of IoT services and a goal, the QoS-aware IoT service composition problem constructs a composite IoT service with the optimal accumulated QoS value, which satisfies the given goal specification. However, in the IoT environment, frequent changes happen inherently - for instance, temporary machine down, heavy system workload, and network failure. If the solution we have constructed is not valid anymore due to the changes, we should solve a new problem again. In this paper, we propose a novel incremental recomposition algorithm, which does not solve the new composition problem from scratch but explores only the changed space. In the experiment, our incremental recomposition algorithm can deal with the composition problem much faster than the original algorithm to solve from scratch.
In this paper, the problem of distributed Nash equilibrium computation in two-network zero-sum games is studied. Based on a sequential communication strategy, a novel incremental algorithm is developed to compute a Na...
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In this paper, the problem of distributed Nash equilibrium computation in two-network zero-sum games is studied. Based on a sequential communication strategy, a novel incremental algorithm is developed to compute a Nash equilibrium. Different from the existing algorithms, the agents in two different subnet-works perform their updates in an asynchronous way, and the square-summable assumption of step sizes adopted in the existing methods is removed in our algorithm. In the convergence analysis of the proposed algorithm, two important relations of the agents' equilibrium estimates are firstly provided based on the properties of projection operator. Then by combining the methods of contradiction and mathematical induction, it is proven that the agents' estimates achieve a Nash equilibrium even without the square-summable requirement of step sizes. Finally, simulations are provided to verify the validity of our method. (C) 2019 Elsevier B.V. All rights reserved.
Formal concept analysis has proven to be a very effective method for data analysis and rule extraction, but how to build formal concept lattices is a difficult and hot topic. In this paper, an efficient and rapid incr...
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Formal concept analysis has proven to be a very effective method for data analysis and rule extraction, but how to build formal concept lattices is a difficult and hot topic. In this paper, an efficient and rapid incremental concept lattice construction algorithm is proposed. The algorithm, named FastAddExtent, is seen as a modification of AddIntent in which we improve two fundamental procedures, including fixing the covering relation and searching the canonical generator. The proposed algorithm can locate the desired concept quickly by adding data fields to every concept. The algorithm is depicted in detail, using a formal context to show how the new algorithm works and discussing time and space complexity issues. We also present an experimental evaluation of its performance and comparison with AddExtent. Experimental results show that the FastAddExtent algorithm can improve efficiency compared with the primitive AddExtent algorithm.
Association rule mining plays an important role in many areas, including market basket analysis, intrusion detection, bioinformatics and so on. As an efficient approach of finding frequent itemset among large datasets...
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ISBN:
(纸本)9781450365192
Association rule mining plays an important role in many areas, including market basket analysis, intrusion detection, bioinformatics and so on. As an efficient approach of finding frequent itemset among large datasets, several parallel Apriori-based algorithms are widely used in association rule mining Moreover, datasets are always changed in many real-world applications. For example, datasets of the purchased products from an e-commercial website is growing all the time. However, the existing parallel Apriori-based algorithms cannot update the frequent itemset efficiently for these large and evolving datasets. So we propose an incremental parallel Apriori-based algorithm in this paper. As the datasets increase, our algorithm updates the frequent itemset based on frequent itemset in previous, instead of re-computing the whole datasets from scratch. We implement the proposed algorithm on Spark and evaluate its performance via groups of experiments on some real-world datasets. It is demonstrated by the experimental results that the proposed algorithm improves the performance of mining frequent itemset on the large and evolving data sets significantly.
The incremental acoustoelastic equations for fluid-saturated porous media (FSPM) under the large static pre-deformation are derived in this paper by incremental loading method based on classic acoustoelastic theory of...
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The incremental acoustoelastic equations for fluid-saturated porous media (FSPM) under the large static pre-deformation are derived in this paper by incremental loading method based on classic acoustoelastic theory of FSPM, which provides quantitative acoustoelastic relation of FSPM with arbitrary constitutive equation. Isotropic FSPM with third-order constitutive equation are taken as an example to give the relation between wave velocity and confining pressure and discuss the effect of loading step on acoustoelastic relations of isotropic FSPM under closed-pore jacketed condition and opened-pore jacketed condition.
Clustering coefficient is widely used in many real world applications, such as social network analysis and community mining. However, it is expensive to compute clustering coefficient for the large and dynamic network...
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ISBN:
(纸本)9781538604977
Clustering coefficient is widely used in many real world applications, such as social network analysis and community mining. However, it is expensive to compute clustering coefficient for the large and dynamic networks. To improve the performance of clustering coefficient computing for these dynamic graphs, we propose an incremental algorithm based on random wedge sampling and implement the proposed algorithm upon MapReduce. The proposed algorithm reuses previous result and updates the estimate incrementally, instead of computing the whole dynamic graph from scratch. Experiments on real-world graphs demonstrate that the proposed algorithm is accurate and efficient. Compared with a state-of-the-art MapReduce algorithm, the proposed algorithm runs faster without scarifying accuracy of estimate.
Several algorithms have been proposed to generate a polygonal footprint' to characterize the shape of a set of points in the plane. One widely used type of footprint is the -shape. Based on the Delaunay triangulat...
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Several algorithms have been proposed to generate a polygonal footprint' to characterize the shape of a set of points in the plane. One widely used type of footprint is the -shape. Based on the Delaunay triangulation (DT), -shapes guaranteed to be simple (Jordan) polygons. This paper presents for the first time an incremental -shape algorithm, capable of processing point data streams. Our incremental -shape algorithm allows both insertion and deletion operations, and can handle streaming individual points and multiple point sets. The experimental results demonstrated that the incremental algorithm is significantly more efficient than the existing, batch -shape algorithm for processing a wide variety of point data streams.
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