An efficient integration and indexing method based on feature patterns and semantic analysis for big data

Nashipudimath, Madhu; Shinde, Subhash K.; Jain, Jayshree

doi:10.1016/j.array.2020.100033

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…When it comes to Apache Spark, the primary use is to index real-time geographic data in R-tree and its variations which makes the spatial range queries get real-time results. Employing feature transformation and selection techniques and (Nashipudimath et al [8]) applied a probabilistic feature patterns (PFP) strategy to efficiently integrate data and feature latent semantic analysis (F-LSA) method to index the various heterogeneous cluster data sources.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

Rong

2022

Mobile Information Systems

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More and more medical images are being produced quickly by modern imaging devices that are connected to the Internet of Things (IoT). For the retrieval of necessary images from a conventional big volume database, the continuous scanning of all image data in an IoT network appears to be ineffective and computationally expensive. Effective image retrieval from a big image database necessitates the use of an effective and scalable image indexing approach. Existing image indexing methods have significant drawbacks, including lower efficiency, constrained scalability, increased computing demands, and longer processing times. To index the medical images obtained by IoT sensors, we suggested a novel, effective Content-Based Cascaded Gabor Wavelet Algorithm (CBCGWA) in this study. Further in this study, the medical images from computed tomography (CT) are employed for medical indexing Totally, the dataset included 168 individually annotated square patches in a subset of 115 high-resolution CT (High-Resolution CT) slices used in this study. An adaptive median filter is used for preprocessing the CT medical images once they are acquired from IoT sensor nodes connected to medical imaging systems. The Gaussian Adaptive Attention Network is then used to cluster together images with comparable attributes (GAAN). The suggested method is used to index the images after image clustering. The cloud database is where the indexed images are eventually maintained. The comparison of the recommended indexing approach to the existing indexing strategies revealed that it is better in terms of processing time, power, and indexing efficiency.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

Rong

2022

Mobile Information Systems

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“…It comprised more than 4000 files with more than 6 million article records. These data are obtained from the diverse technical article publishers' data files in Information Extraction and Synthesis Laboratory (IESL) 17 . The article's keywords are used as the parameters for data retrieval for integration.…”

Section: Simulation Analysis and Resultsmentioning

confidence: 99%

“…These data are obtained from the diverse technical article publishers' data files in Information Extraction and Synthesis Laboratory (IESL). 17 The article's keywords are used as the parameters for data retrieval for integration. Moreover, the available BibText dataset is taken from "https://sites.google.com/a/iesl.cs.umass.edu/home/data/bibtex" website.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Web information mining and semantic analysis in heterogeneous unstructured text data using enhanced latent Dirichlet allocation

Venugopal

Sharma

2022

Concurrency and Computation

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Summary Information mining and semantic analysis have gained significant attention over recent years to obtain appropriate information from unstructured data. Several approaches have been introduced for web information mining. However, the expected accuracy is not reached by these approaches. Therefore, hybrid fuzzy clustering and enhanced latent Dirichlet allocation (ELDA) are proposed for the accuracy increment in this work. The information clustering process is performed using the hybrid fuzzy clustering algorithm called fuzzy‐C‐medoids optimized using improved whale algorithm. The clustering procedure entails grouping data points into more similar clusters than data points from other clusters. Finally, the context of the text is recognized by analyzing the semantic information with ELDA, which offers a suitable index for accurate and fast data extraction. PYTHON tool is used to develop the proposed web mining model, and the simulation analysis of the proposed model is carried out using the BibTex dataset and compared with baseline models. The performance of the proposed method is evaluated using purity, normalized mutual information, accuracy, and precision metrics. Also, it is compared with different existing algorithms. Thus, the analysis of the results proved that the proposed approach achieves better outcomes than the existing approaches.

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“…Selection of attributes and value of attributes were considered as a cause of low performance [33]. A method for indexing of heterogeneous cluster of data resources is proposed [34]. e authors proposed for the future that dynamic construction of indexing can be performed to reduce the complications in storing index during querying.…”

Section: Artificial Intelligence and Big Datamentioning

confidence: 99%

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

Chang

Nazir

2022

Scientific Programming

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Big Data is data whose shape and volume are rising with the passage of time and innovations in technology. This increase will give birth to more uncertain and complex situations, which will then be difficult to properly analyze and manage. Various devices are interconnected with each other, which communicate different types of information. This information is used for different purposes. A huge volume of data is produced, and the storage becomes larger. Computational modeling is the tool that helps analyze, process, and manage the data to extract useful information. The modern industry's challenge is to incorporate knowledge into Big Data applications to deal with distinguishing difficulties in computational models. The techniques and models are delivered with guides to help analysts quickly fit models to information insights. The decision support system is a strong system that plays a significant role in shaping Big Data for sustaining efficiency and performance. Decision-making through computational modeling is also a powerful mechanism for supporting efficient tools for managing Big Data for influential use. Keeping in view the issues of modern-day industry, the proposed study has been considered to present decision-making and computational modeling of Big Data for sustaining influential usage. The existing state-of-the-art literature is presented in an organized way to analyze the currently available research.

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An efficient integration and indexing method based on feature patterns and semantic analysis for big data

Cited by 9 publications

References 11 publications

Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

Web information mining and semantic analysis in heterogeneous unstructured text data using enhanced latent Dirichlet allocation

Decision-Making and Computational Modeling of Big Data for Sustaining Influential Usage

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