Periodic pattern mining from spatio-temporal database using novel global pollination artificial fish swarm optimizer-based clustering and modified FP tree

Upadhyay, Pragati; Pandey, Manoj Kumar; Kohli, Narendra

doi:10.1007/s00500-020-05444-z

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Temporal anomaly detection data is the initial step of power consumption of the original stream data preprocessing using dataset from the former to build the virtual machine power model, the model through the acquisition of virtual machine internal events to build a regression model to assess the performance of timestamped virtual machine power consumption data, after transmission through the network transmission to the temporal database [3]. However, whether it is the performance event collector power estimation model failure or the transmission process due to the interruption of network fluctuation, there may be noisy data.…”

Section: Data Preprocessingmentioning

confidence: 99%

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Shang

2022

Security and Communication Networks

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In order to solve the problem of outlier detection of integrated energy security defense monitoring software, an automatic detection algorithm of virtual machine power anomaly in a cloud computing environment is proposed. The method is implemented through three main steps: data preprocessing, pattern recognition, and prediction of virtual machine power anomaly detection model. It is found through experiments that with the increase of node number, the convergent iterations of the model are less and RMSE is lower, but the increase of node number of the hidden layer will lead to a longer model running time. When the number of nodes reaches 100, the test results of the validation set are significantly improved, and the loss function of the validation set is minimal when the number of nodes is less than 30 iterations. Finally, the hidden layer of the model consists of 100 LSTM units, followed by a dense output layer with 1 neuron, and 0.2 loss, retrospection, and foresight equal to 1. Adam optimizer was used to train LSTM and stop it in advance after 50 iteration steps. Its parameters remained default, with a learning rate of 0.001 and attenuation of 0.9. It can be seen that this model can well predict the virtual machine power consumption data and effectively solve the problem of outlier detection of integrated energy security defense monitoring software.

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Section: Data Preprocessingmentioning

confidence: 99%

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Shang

2022

Security and Communication Networks

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“…They employ tree-like structures, such as R-tree [1,2,17,34,35], Quadtree [3,15], etc., or non-tree structures, such as Grid [5,36], space-filling curve [16,37], etc., to maintain spatial location features. Inverted file [1,5,15,16], Fp-tree [25,38], signature [3,17,18], bitmap [19], etc., are also employed to maintain nonspatial textual features. However, considering a large number of the detailed features of textual features and their many object associations, how to efficiently find and index similar spatial objects from spatial textual big data remains a hot research topic.…”

Section: Related Workmentioning

confidence: 99%

Spatial Concept Query Based on Lattice-Tree

Zhang

et al. 2022

IJGI

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As a basic method of spatial data operation, spatial keyword query can provide meaningful information to meet user demands by searching spatial textual datasets. How to accurately understand users’ intentions and efficiently retrieve results from spatial textual big data are always the focus of research. Spatial textual big data and their complex correlation between textual features not only enrich the connotation of spatial objects but also bring difficulties to the efficient recognition and retrieval of similar spatial objects. Because there are a lot of many-to-many relationships between massive spatial objects and textual features, most of the existing research results that employ tree-like and table-like structures to index spatial data and textual data are inefficient in retrieving similar spatial objects. In this paper, firstly, we define spatial textual concept (STC) as a group of spatial objects with the same textual keywords in a limited spatial region in order to present the many-to-many relationships between spatial objects and textual features. Then we attempt to introduce the concept lattice model to maintain a group of related STCs and propose a hybrid tree-like spatial index structure, the lattice-tree, for spatial textual big data. Lattice-tree employs R-tree to index the spatial location of objects, and it embeds a concept lattice structure into specific tree nodes to organize the STC set from a large number of textual keywords of objects and their relationships. Based on this, we also propose a novel spatial keyword query, named Top-k spatial concept query (TkSCQ), to answer STC and retrieve similar spatial objects with multiple textual features. The empirical study is carried out on two spatial textual big data sets from Yelp and Amap. Experiments on the lattice-tree verify its feasibility and demonstrate that it is efficient to embed the concept lattice structure into tree nodes of 3 to 5 levels. Experiments on TkSCQ evaluate lattice from results, keywords, data volume, and so on, and two baseline index structures based on IR-tree and Fp-tree, named the inverted-tree and Fpindex-tree, are developed to compare with the lattice-tree on data sets from Yelp and Amap. Experimental results demonstrate that the Lattice-tree has the better retrieval efficiency in most cases, especially in the case of large amounts of data queries, where the retrieval performance of the lattice-tree is much better than the inverted-tree and Fpindex-tree.

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“…Spatio-temporal clustering and zoning is an important topic in spatio-temporal data mining, which is to zone objects both in temporal dimension and spatial dimension according to the data characteristics, as well as detect abnormal objects for the sake of taking corresponding measures in advance [5][6][7]. In related research works of spatio-temporal clustering and its application, Ansari et al reviewed spatio-temporal clustering and its application in engineering, environmental and seismology studies, transportation, etc.…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

Wang

Chen

et al. 2021

IEEE Access

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Deformation is the most intuitive indicator of the actual working status of a concrete dam. Zoning the variation regulation of dam deformation is one of the key parts of dam safety evaluation and risk assessment. However, the sample points reflecting deformation and variation characteristic information are non-uniformly distributed, thus it is difficult to cluster the data samples by traditional clustering methods. To solve this problem, a spatio-temporal zoning method of dam deformation considering non-uniform distribution of monitoring information is proposed. Firstly, the preprocessed deformation data are utilized to establish the similarity-distance zoning indicators using the absolute deformation, the deformation increase and the relative deformation increase respectively; then the deformation data are transferred into the Cartesian coordinate system, known as sample points. Secondly, utilize the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the points. The clustering parameters M and δ are determined by an optimization algorithm with an evaluation index as the objective function, then the sample points representing time sections or spatial monitoring points are clustered through dynamically updating the neighborhood radius value ε. Moreover, several artificial data sets are selected to demonstrate that the improved DBSCAN algorithm is with more obvious superiority in nonuniform clustering compared to traditional algorithms. Deformation data of an existing concrete dam are presented and discussed to validate the established zoning method.

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Periodic pattern mining from spatio-temporal database using novel global pollination artificial fish swarm optimizer-based clustering and modified FP tree

Cited by 7 publications

References 29 publications

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Integrated Energy Security Defense Monitoring Software Based on Cloud Computing

Spatial Concept Query Based on Lattice-Tree

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

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