Inverted index search in data mining

Ilić, Miloš; Spalević, Petar; Veinović, Mladen

doi:10.1109/telfor.2014.7034561

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…erefore, users will initiate the demand for data on the cloud. First, authentication is required between users and cloud servers [10]. Figure 1 shows the process of key distribution to facilitate subsequent data request by users.…”

Section: Key Distributionmentioning

confidence: 99%

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Huang¹,

Wu²

2022

Security and Communication Networks

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Blockchain is a distributed time-series database. Based on the blockchain platform, this paper designs the framework model of cloud storage and designs the cloud storage model based on homomorphic encryption based on the operation process of the model. According to the applicability of underlying blockchain storage, the HElib library is established as the algorithm for data privacy protection. The BGV homomorphic encryption algorithm is used as the bottom layer of the algorithm, and the efficiency of the BGV homomorphic encryption algorithm is compared with that of the Gentry’s bootstrap-based homomorphic encryption algorithm. It is proved that the BGV algorithm is more suitable for big data.

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Section: Key Distributionmentioning

confidence: 99%

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Huang¹,

Wu²

2022

Security and Communication Networks

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“…To utilize texture data in clustering, a technique for efficiently retrieving data required for data analysis is preferentially needed. Generally, an inverted index is used to retrieve from a typical texture data [26]. An inverse index consists of several inverse lists including the IDs of all documents containing a word and its frequency.…”

Section: Keyword-matching Dbscanmentioning

confidence: 99%

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

Kim¹,

Jang²

2023

Computer Modeling in Engineering &Amp; Sciences

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As location information of numerous Internet of Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data. In this paper, we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm. The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm. To solve this problem, we developed the Genetic algorithm-based Keyword Matching DBSCAN (GKM-DBSCAN) algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword. In order to improve the performance of GKM-DBSCAN, we improved the general genetic algorithm by performing a genetic operation in groups. We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method. The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times. GKM-DBSCAN with the index number binarization (INB) is 1.8 times faster than GKM-DBSCAN with the cluster number binarization (CNB).

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“…The indexing method employed involves dividing the whole space into a d-grid (2 d × 2 d grids) and further constructing a (d -1)-Grid, ..., 1-Grid. An inverted index [21] is built from the d layer to the upper layer to record the grid through which the moving object passes. Wang [22] proposed a spatiotemporal index for a road network based on quadtree.…”

Section: Related Workmentioning

confidence: 99%

GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

Qian

Cheng

et al. 2019

IJGI

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With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes.

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Inverted index search in data mining

Cited by 11 publications

References 5 publications

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

GeoSOT-Based Spatiotemporal Index of Massive Trajectory Data

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