Efficient Incremental Algorithm Research Articles

Double-kernel based Bayesian approximation broad learning system with dropout

Broad learning system (BLS) is an efficient incremental learning machine algorithm. However, there are some disadvantages in such an algorithm. For example, the number of hidden layer nodes needs to be manually adjusted during the training process, meanwhile the large uncertainty will be caused by two random mappings. To solve these problems, based on the optimization ability of the kernel function, a double-kernel broad learning system (DKBLS) is proposed to eliminate the uncertainty of random mapping by using additive kernel strategy. Meanwhile, to reduce the computing costs and training time of DKBLS, a double-kernel based bayesian approximation broad learning system with dropout (Dropout-DKBLS) is further proposed. Ablation experiments show that the output accuracy of Dropout-DKBLS does not decrease even if the node is dropped. In addition, function approximation experiments show that DKBLS and Dropout-DKBLS have good robustness and can accurately predict noise data. The regression and classification experiments on multiple datasets are compared with the latest kernel-based learning methods. The comparison results show that both DKBLS and Dropout-DKBLS have good regression and classification performance. By further comparing the training time of these kernel-based learning methods, we prove that the Dropout-DKBLS can reduce the computational cost while ensuring the output accuracy.

TC-Match: Fast Time-Constrained Continuous Subgraph Matching

Continuously monitoring structural patterns in streaming graphs is a critical task in many real-time graph-based applications. In this paper, we study the problem of time-constrained continuous subgraph matching (shorted as TCSM) over streaming graphs. Given a query graph Q with timing order constraint and a data graph stream G , TCSM aims to report all incremental matches of Q in G for each update of G , where a match should obey both structure constraint (i.e., isomorphism) and timing order constraint of Q. Although TCSM has a wide range of applications, such as cyber-attack detection and credit card fraud detection, we note that this problem has not been well addressed. The state-of-the-art bears the limitations of high index space cost and intermediate result maintenance cost. In this paper, we propose TC-Match, an effective approach to TCSM. First, we design a space and time cost-effective index CSS, which is essentially a k -partite graph structure where a node corresponds to an edge in G. By carefully creating links between nodes, we can encapsulate into CSS the partial embedding and timing order information between edges in G. We theoretically show that CSS has polynomial space and construction time complexities. Second, based on the property of CSS, we develop an efficient incremental matching algorithm with an effective node merging optimization. Extensive experiments show that TC-Match can achieve up to 3 orders of magnitude query performance improvement over the baseline methods, and meanwhile the memory consumption is reduced by 48.7%-86.7%.

Efficient and Effective Cardinality Estimation for Skyline Family

Cardinality estimation, predicting the query result size, is a fundamental problem in databases. Existing skyline cardinality estimation methods are computationally infeasible for massive skyline queries over the large-scale database. In this paper, we introduce a unified skyline family w.r.t. various skyline variants. We propose an efficient and effective skyline family cardinality estimation model, named EECE, in an end-to-end manner. EECE consists of two modules, unsupervised data distribution learning (DDL) and supervised monotonic cardinality estimation (MCE). DDL leverages the mixture data guided transformer to learn the distribution of database and query parameters for model pre-training. MCE further incorporates supervised learning and parameter clamping to enhance the estimation under monotonicity guarantees. We develop an efficient incremental learning algorithm for EECE to adapt the database and query logs update. Extensive experiments on several real-world and synthetic datasets demonstrate that, EECE speeds up the cardinality estimation by six orders of magnitude, with more than 39% accuracy gain, compared to the state-of-the-art approaches.

IDLIQ: An Incremental Deterministic Finite Automaton Learning Algorithm Through Inverse Queries for Regular Grammar Inference.

We present an efficient incremental learning algorithm for Deterministic Finite Automaton (DFA) with the help of inverse query (IQ) and membership query (MQ). This algorithm is an extension of the Identification of Regular Languages (ID) algorithm from a complete to an incremental learning setup. The learning algorithm learns by making use of a set of labeled examples and by posing queries to a knowledgeable teacher, which is equipped to answer IQs along with MQs and equivalence query. Based on the examples (elements of the live complete set) and responses against IQs from the minimally adequate teacher (MAT), the learning algorithm constructs the hypothesis automaton, consistent with all observed examples. The Incremental DFA Learning algorithm through Inverse Queries (IDLIQ) takes time complexity in the presence of a MAT and ensures convergence to a minimal representation of the target DFA with finite number of labeled examples. Existing incremental learning algorithms; the Incremental ID, the Incremental Distinguishing Strings have polynomial (cubic) time complexity in the presence of a MAT. Therefore, sometimes, these algorithms even fail to learn large complex software systems. In this research work, we have reduced the complexity (from cubic to square form) of the DFA learning in an incremental setup. Finally, we prove the correctness and termination of the IDLIQ algorithm.

Generation of efficient and interference-free scanning path for inspecting impeller on a cylindrical CMM

Traditional methods for inspecting impellers on a three-axis Coordinate Measuring Machine (CMM) lack efficiency, since they need to adjust the clamping posture of the impeller or the probe orientation many times to avoid interference. In this paper, we propose an algorithm to generate efficient and interference-free scanning path for inspecting impeller on a cylindrical CMM. Firstly, the minimal number of probe tilt angles for inspecting the blade section is formulated as an optimization problem. The blade section is divided into several paths, each of which corresponds to one of the optimized probe tilt angles. Then, the feasible turntable rotation angles (FTRAs) in each path are calculated by an efficient incremental algorithm. Finally, the B-spline fitting and bisection method are used to generate a smooth turntable trajectory under the constraints of FTRAs. Inspection experiments on two impellers verify the efficiency of the proposed algorithm and the smoothness of the turntable rotation.

An efficient planar incremental convex hull algorithm to find the edges of the boundary polygon of the convex hull of a set of points

The Ceylon Journal of Science is published by the University of Peradeniya, Sri Lanka. Full text available. The journal also has its own website. The Ceylon Journal of Science is a continuation of the Ceylon Journal of Science (Biological Sciences) which is no longer being published as a separate journal. The history of the journal can be found here.From May 2020, Ceylon Journal of Science is indexed in DOAJ.

Dynamic network embedding via incremental skip-gram with negative sampling

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of $$\mathbb{O}(\frac{1}{n^{2}})$$ , and the second order moment of the objective difference can be stabilized in order of $$\mathbb{O}(1)$$ . Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to 22 times speedup has been achieved.

Solar Based High Gain Power Converter with Efficient Controller for LED Application

The representation of this paper is a different approach to get efficient high brightness of LED lighting operating with high gain buck boost converter controlled by efficient incremental conductance MPPT controller algorithm of solar PV module. The mathematical modeling associated with process of simulation for solar panel, high gain buck boost topology and efficient MPPT controller is discussed clearly in this paper. Conventional converters are applied and analyzed for the LED lighting system in literature, but these are not preferred due to lack of efficient performance, high harmonic distortion and low power factor. The projected buck-boost structure is modified structure of high gain buck-boost structure. The comparison between projected converter and existing power converter is clearly summarized in this paper. The switching as well as conduction losses are very low due to single switch operating without transformer. The cost of the solar based LED lighting system with high conversion ratio buck-coost converter is very cheap because the number of semiconductor devices and passive components are using low. In this paper 120W and 30W of LED lighting system is planned and executed in MATLAB/SIMULINK. The clear summary of outcomes will prove the system is efficient

SCI-Tree: An Incremental Algorithm for Computing Support Counts of all Closed Intervals from an Interval Dataset

Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from the given interval data. Also, all the frequent closed intervals can be computed for any user defined minimum support with a single scan of SCI-Tree without revisiting the dataset. The proposed method has been tested with real life and synthetic datasets and results have been reported.

An efficient incremental strongly connected components algorithm for evolving directed graphs

The strongly connected components (SCC) algorithm can contract a directed graph into a directed acyclic graph and is widely used in directed graph analysis applications, such as reachability queries. A variety of SCC algorithms for static directed graphs have been proposed but such algorithms require non-negligible runtime overheads to repeatedly perform computations on an entire graph in response to the frequent changes in the evolving directed graphs that are ubiquitous in the real world. In general, evolving directed graphs are often evolving with minor changes (less than 5%).It allows us to compute SCC in an evolving directed graph on the basis of incremental computations in order to reduce the response time. This paper proposes Inc-SCC, an efficient incremental SCC algorithm for evolving directed graphs, reducing the data access and computation overhead of the algorithm by eliminating unnecessary computations, and using the disjoint feature of SCC for parallel processing to improve the performance of the SCC algorithm. We propose a heuristic optimization method to further speed up the convergence of Inc-SCC. Experiments show that Inc-SCC can be used to enhance the timeliness for evolving directed graphs. When the number of the changed edges of the entire directed graph is 5%, the speedup of Inc-SCC over the existing algorithm is from 2.8 to 12 times. When the number of thechanged edges of an entire directed graph is 0.5%, the speedup of Inc-SCC over the existing algorithm is from 2.9 to 12 times.

A New Rapid Incremental Algorithm for Constructing Concept Lattices

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.

A lightweight and scalable visual-inertial motion capture system using fiducial markers

Accurate localization of a moving object is important in many robotic tasks. Often an elaborate motion capture system is used to realize it. While high precision is guaranteed, such a complicated system is costly and limited to specified small size workspace. This paper describes a lightweight and scalable visual-inertial approach, which leverages paper printable, known size and unknown pose, artificial landmarks, as called fiducials, to obtain motion state estimates, including pose and velocity. Visual-inertial joint optimization using incremental smoother over factor graph and the IMU preintegration technique make our method efficient and accurate. No special hardware is required except a monocular camera and an IMU, making our system lightweight and easy to deploy. Using paper printable landmarks, as well as the efficient incremental inference algorithm, renders it nearly constant-time complexity and scalable to large-scale environment. We perform an extensive evaluation of our method on public datasets and real-world experiments. Results show our method achieves accurate state estimates and is scalable to large-scale environment and robust to fast motion and changing light condition. Besides, our method has the ability to recover from intermediate failure.

An Efficient Incremental Mining Algorithm for Discovering Sequential Pattern in Wireless Sensor Network Environments.

Wireless sensor networks (WSNs) are an important type of network for sensing the environment and collecting information. It can be deployed in almost every type of environment in the real world, providing a reliable and low-cost solution for management. Huge amounts of data are produced from WSNs all the time, and it is significant to process and analyze data effectively to support intelligent decision and management. However, the new characteristics of sensor data, such as rapid growth and frequent updates, bring new challenges to the mining algorithms, especially given the time constraints for intelligent decision-making. In this work, an efficient incremental mining algorithm for discovering sequential pattern (novel incremental algorithm, NIA) is proposed, in order to enhance the efficiency of the whole mining process. First, a reasoned proof is given to demonstrate how to update the frequent sequences incrementally, and the mining space is greatly narrowed based on the proof. Second, an improvement is made on PrefixSpan, which is a classic sequential pattern mining algorithm with a high-complexity recursive process. The improved algorithm, named PrefixSpan+, utilizes a mapping structure to extend the prefixes to sequential patterns, making the mining step more efficient. Third, a fast support number-counting algorithm is presented to choose frequent sequences from the potential frequent sequences. A reticular tree is constructed to store all the potential frequent sequences according to subordinate relations between them, and then the support degree can be efficiently calculated without scanning the original database repeatedly. NIA is compared with various kinds of mining algorithms via intensive experiments on the real monitoring datasets, benchmarking datasets and synthetic datasets from aspects including time cost, sensitivity of factors, and space cost. The results show that NIA performs better than the existed methods.

Dynamical SimRank search on time-varying networks

SimRank is an appealing pair-wise similarity measure based on graph structure. It iteratively follows the intuition that two nodes are assessed as similar if they are pointed to by similar nodes. Many real graphs are large, and links are constantly subject to minor changes. In this article, we study the efficient dynamical computation of all-pairs SimRanks on time-varying graphs. Existing methods for the dynamical SimRank computation [e.g., LTSF (Shao et al. in PVLDB 8(8):838–849, 2015) and READS (Zhang et al. in PVLDB 10(5):601–612, 2017)] mainly focus on top-k search with respect to a given query. For all-pairs dynamical SimRank search, Li et al.’s approach (Li et al. in EDBT, 2010) was proposed for this problem. It first factorizes the graph via a singular value decomposition (SVD) and then incrementally maintains such a factorization in response to link updates at the expense of exactness. As a result, all pairs of SimRanks are updated approximately, yielding O({r}^{4}n^2) time and O({r}^{2}n^2) memory in a graph with n nodes, where r is the target rank of the low-rank SVD. Our solution to the dynamical computation of SimRank comprises of five ingredients: (1) We first consider edge update that does not accompany new node insertions. We show that the SimRank update {varvec{Delta }}{} mathbf{S} in response to every link update is expressible as a rank-one Sylvester matrix equation. This provides an incremental method requiring O(Kn^2) time and O(n^2) memory in the worst case to update n^2 pairs of similarities for K iterations. (2) To speed up the computation further, we propose a lossless pruning strategy that captures the “affected areas” of {varvec{Delta }}{} mathbf{S} to eliminate unnecessary retrieval. This reduces the time of the incremental SimRank to O(K(m+|{textsf {AFF}}|)), where m is the number of edges in the old graph, and |{textsf {AFF}}| (le n^2) is the size of “affected areas” in {varvec{Delta }}{} mathbf{S}, and in practice, |{textsf {AFF}}| ll n^2. (3) We also consider edge updates that accompany node insertions, and categorize them into three cases, according to which end of the inserted edge is a new node. For each case, we devise an efficient incremental algorithm that can support new node insertions and accurately update the affected SimRanks. (4) We next study batch updates for dynamical SimRank computation, and design an efficient batch incremental method that handles “similar sink edges” simultaneously and eliminates redundant edge updates. (5) To achieve linear memory, we devise a memory-efficient strategy that dynamically updates all pairs of SimRanks column by column in just O(Kn+m) memory, without the need to store all (n^2) pairs of old SimRank scores. Experimental studies on various datasets demonstrate that our solution substantially outperforms the existing incremental SimRank methods and is faster and more memory-efficient than its competitors on million-scale graphs.

Incremental sparse GP regression for continuous-time trajectory estimation and mapping

Recent work on simultaneous trajectory estimation and mapping (STEAM) for mobile robots has used Gaussian processes (GPs) to efficiently represent the robot’s trajectory through its environment. GPs have several advantages over discrete-time trajectory representations: they can represent a continuous-time trajectory, elegantly handle asynchronous and sparse measurements, and allow the robot to query the trajectory to recover its estimated position at any time of interest. A major drawback of the GP approach to STEAM is that it is formulated as a batch trajectory estimation problem. In this paper we provide the critical extensions necessary to transform the existing GP-based batch algorithm for STEAM into an extremely efficient incremental algorithm. In particular, we are able to vastly speed up the solution time through efficient variable reordering and incremental sparse updates, which we believe will greatly increase the practicality of Gaussian process methods for robot mapping and localization. Finally, we demonstrate the approach and its advantages on both synthetic and real datasets.

Ranking weighted clustering coefficient in large dynamic graphs

Efficiently searching top-k representative vertices is crucial for understanding the structure of large dynamic graphs. Recent studies show that communities formed by a vertex with high local clustering coefficient and its neighbours can achieve enhanced information propagation speed as well as disease transmission speed. However, local clustering coefficient, which measures the cliquishness of a vertex in its local neighbourhood, prefers vertices with small degrees. To remedy this issue, in this paper we propose a new ranking measure, weighted clustering coefficient (WCC) of vertices, by integrating both local clustering coefficient and degree. WCC not only inherits the properties of local clustering coefficient but also approximately measures the density (i.e., average degree) of its neighbourhood subgraph. Thus, vertices with higher WCC are more likely to be representative. We study efficiently computing and monitoring top-k representative vertices based on WCC over large dynamic graphs. To reduce the search space, we propose a series of heuristic upper bounds for WCC to prune a large portion of disqualifying vertices from the search space. We also develop an approximation algorithm by utilizing Flajolet-Martin sketch to trade acceptable accuracy for enhanced efficiency. An efficient incremental algorithm dealing with frequent updates in dynamic graphs is explored as well. Extensive experimental results on a variety of real-life graph datasets demonstrate the efficiency and effectiveness of our approaches.

An efficient incremental algorithm for generating the characteristic shape of a dynamic set of points in the plane

ABSTRACTSeveral 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 χ-shape. Based on the Delaunay triangulation (DT), χ-shapes guaranteed to be simple (Jordan) polygons. This paper presents for the first time an incremental χ-shape algorithm, capable of processing point data streams. Our incremental χ-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 χ-shape algorithm for processing a wide variety of point data streams.

Web mining based on one-dimensional Kohonen’s algorithm: analysis of social media websites

One-dimensional Kohonen’s algorithm is a process of mining knowledge which finds the characteristics of social media websites as a mode from the sequence database. Social media web log records generated constantly, and user access patterns will change accordingly. This study focused on taking advantage of the dynamic characteristics of the Kohonen algorithm, delivering a fast and efficient incremental mining algorithm and testing the new developed model.

Recommender System for Academic Literature with Incremental Dataset

On account of the colossal expansion in the size of research paper repository, the stature of Recommender System has increased, as it can guide the researchers to find papers akin to them from this vast collection. Furthermore, the recommendation methods like collaborative-filtering or content-based do not allow the user's to provide their personalized requirements explicitly; hence the focus is shifted towards the customized Recommender Systems that can scrutinize user's preferences by contemplating their inputs. But the state-of-art recommendation techniques satisfying user's personalized requirements make a strong assumption of static dataset. So, in this work we are going to present a customized Recommender System that can acknowledge the ever growing nature of research paper repository. To accomplish this, the Efficient Incremental High-Utility Itemset Mining algorithm (EIHI), which has been recently introduced in the literature, is used which is specialized to work with dynamic datasets. Experimental results prove that the proposed system satisfies the researcher's personalized requirements and at the same time handles the incremental nature of the research paper repository efficiently.

An Incremental Decision Tree for Mining Multilabel Data

Mining with multilabel data is a popular topic in data mining. When performing classification on multilabel data, existing methods using traditional classifiers, such as support vector machines (SVMs), k-nearest neighbor (k-NN), and decision trees, have relatively poor accuracy and efficiency. Motivated by this, we present a new algorithm adaptation method, namely, a decision tree–based method for multilabel classification in domains with large-scale data sets called decision tree for multi-label classification (DTML). We build an incremental decision tree to reduce the learning time and divide the training data and adopt the k-NN classifier at leaves to improve the classification accuracy. Extensive studies show that our algorithm can efficiently learn from multilabel data while maintaining good performance on example-based evaluation metrics compared to nine state-of-the-art multilabel classification methods. Thus, we draw a conclusion that we provide an efficient and effective incremental algorithm adaptation method for multilabel classification especially in domains with large-scale multilabel data.