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Int. J. Big Data Intell. Appl.最新文献

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Role of Candidate Key in Metadata for Data Analysis 候选键在数据分析元数据中的作用
Int. J. Big Data Intell. Appl. Pub Date : 2023-02-16 DOI: 10.4018/ijbdia.318411
P. Pradhan
{"title":"Role of Candidate Key in Metadata for Data Analysis","authors":"P. Pradhan","doi":"10.4018/ijbdia.318411","DOIUrl":"https://doi.org/10.4018/ijbdia.318411","url":null,"abstract":"This proposed research paper focuses on the candidate key to generate a frequent pattern from the large dataset. The functional dependency and candidate key rolling are major activities for creating and collecting the exact pattern to support the decision support system. The functional dependency helps a decision support system to assemble metadata to resolve the uncertainty, unstructured, and unordered data. The large data sets (BigData) can be reassembled through Java programming faster and better by applying combinational algebra. The candidate key is directly proportional to the set of metadata. The large data sets are lively connected to the customer, manufacturer, vendor, order, and product key correctly at the right time. Therefore, the computational mechanism has to develop through the candidate key for better and faster data analysis. The knowledge and decision pattern can be acquired through mapping and integration of candidate key management.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131473558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Strategic Perspective on Challenges and Opportunities in Big Data Management 大数据管理挑战与机遇的战略视角
Int. J. Big Data Intell. Appl. Pub Date : 2022-01-01 DOI: 10.4018/ijbdia.312853
N. Baporikar, Musti K. S. Sastry
{"title":"Strategic Perspective on Challenges and Opportunities in Big Data Management","authors":"N. Baporikar, Musti K. S. Sastry","doi":"10.4018/ijbdia.312853","DOIUrl":"https://doi.org/10.4018/ijbdia.312853","url":null,"abstract":"Big data has come to eminence in all spheres from politics to business and is seen as a resource to enhance business operations and a tool to work efficiently and streamline the collection and distribution of information technology. Virtually everyone, ranging from big web companies to traditional enterprisers, physical science researchers to social scientists, is already experiencing or anticipating unprecedented growth in the amount of data available in their world and seeing opportunities and untapped value. Yet the understanding of big data's role in this interconnected world is in the nascent stage. What has been studied and highlighted appears massive, but what is yet to be realized is the latent potential of big data from an organizational perspective. Hence, adopting a systematic literature review with content analysis, the core aim of this paper is to deliberate on the challenges and opportunities of big data management (BDM) from a strategic perspective. The paper also proposes a mechanism for strategic management of big data and provides case studies to reflect BDM application.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131323099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Semantic Attention Network for Image Captioning and Visual Question Answering Based on Image High-Level Semantic Attributes 基于图像高级语义属性的图像字幕与视觉问答语义关注网络
Int. J. Big Data Intell. Appl. Pub Date : 2022-01-01 DOI: 10.4018/ijbdia.313201
Angelin Gladston, D. Balaji
{"title":"Semantic Attention Network for Image Captioning and Visual Question Answering Based on Image High-Level Semantic Attributes","authors":"Angelin Gladston, D. Balaji","doi":"10.4018/ijbdia.313201","DOIUrl":"https://doi.org/10.4018/ijbdia.313201","url":null,"abstract":"The main challenge in the vision-to-language system is generation of the caption with a proper meaningful answer for a question and extracting even the minute details from the image. The main contributions in this paper are presenting an approach based on image high-level semantic attributes and local image features address the challenges of V2L tasks. Especially, the high-level semantic attributes information is used to reduce the semantic gap between images and text. A novel semantic attention network is designed to explore the mapping relationships between semantic attributes and image regions. The semantic attention network highlights the concept-related regions and selects the region-related concepts. Two special V2L tasks, image captioning and VQA, are addressed by the proposed approach. Improved BLEU score shows the proposed image captioning performs well. The experimental results show that the proposed model is effective for V2L tasks.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126462346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modified GAN for Natural Occlusion Detection and Inpainting of Raw Footage From Video Surveillance 改进的GAN用于视频监控原始素材的自然遮挡检测和图像修复
Int. J. Big Data Intell. Appl. Pub Date : 2022-01-01 DOI: 10.4018/ijbdia.312852
G. PrithamSriram, S. PrasanaVenkatesh, P. DeepakRaj, Angelin Gladston
{"title":"Modified GAN for Natural Occlusion Detection and Inpainting of Raw Footage From Video Surveillance","authors":"G. PrithamSriram, S. PrasanaVenkatesh, P. DeepakRaj, Angelin Gladston","doi":"10.4018/ijbdia.312852","DOIUrl":"https://doi.org/10.4018/ijbdia.312852","url":null,"abstract":"Low resolution and occlusion are mainly prominent in images taken from certain unconstrained environments such as raw footage from video surveillance. In this work, a deep generative adversarial network for joint face completion and face super-resolution is proposed. It will be really useful in the current COVID-19 scenario as people wearing masks are a common sight. Given an input of a low-resolution face image with occlusion, the generator aims to recover a high-resolution face image without occlusion. The discriminator uses a set of carefully designed losses to assure the high quality of the recovered high-resolution face images without occlusion. Experimental results on CelebA database show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution and face completion, and shows good generalization ability in cross-database testing. MSSIM showed an accuracy of around 80% for test cases. The recorded values of generator adversarial loss, generator pixel loss, and discriminator loss are 0.93, 0.10, and 0.003, respectively.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130799692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Big Data Application for Energy Consumption Management of Commercial Buildings 大数据在商业建筑能耗管理中的应用
Int. J. Big Data Intell. Appl. Pub Date : 2021-01-01 DOI: 10.4018/ijbdia.287615
Sarah Abdullah Al Muhaysh, Thanaa E. A. Salem, Fatimah Abdullah Al Jaafari, Al Anoud Saleem Al Shaikh Hussain, A. Khwaja
{"title":"A Big Data Application for Energy Consumption Management of Commercial Buildings","authors":"Sarah Abdullah Al Muhaysh, Thanaa E. A. Salem, Fatimah Abdullah Al Jaafari, Al Anoud Saleem Al Shaikh Hussain, A. Khwaja","doi":"10.4018/ijbdia.287615","DOIUrl":"https://doi.org/10.4018/ijbdia.287615","url":null,"abstract":"The demand for energy is increasing rapidly and, after a few years, it may surpass the available energy, which may lead the energy providers to increase the cost of energy consumption to compensate the cost for the production. This paper provides design and implementation details of a prototype big data application developed to help large buildings to automatically manage their energy consumption by setting energy consumption targets, collecting periodic energy consumption data, storing the data streams, displaying the energy consumption graphically in real-time, analyzing the consumption patterns, and generating energy consumption graphs and reports. The application is connected to Mongo NoSQL backend database to handle the large and continuously changing data. This big data energy consumption management system is expected to help the users in managing energy consumption by analyzing the patterns to see if it is within or above the desired consumption targets and displaying the data graphically.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116478232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Detecting Safe Routes During Floods Using Deep Learning 利用深度学习检测洪水期间的安全路线
Int. J. Big Data Intell. Appl. Pub Date : 2020-01-01 DOI: 10.4018/IJBDIA.2020010102
M. Mathur, Yashi Agarwal, Shubham Pavitra Shah, K. Lavanya
{"title":"Detecting Safe Routes During Floods Using Deep Learning","authors":"M. Mathur, Yashi Agarwal, Shubham Pavitra Shah, K. Lavanya","doi":"10.4018/IJBDIA.2020010102","DOIUrl":"https://doi.org/10.4018/IJBDIA.2020010102","url":null,"abstract":"Floods are one of the most devastating and frequently occurring natural disasters throughout the world. Floods can cause blockage of roads and hence create trouble for civilians and authorities to navigate in the flooded area. This paper proposes an automated system that uses a road extraction algorithm to extract roads from satellite images to create a highlighted map of all the available roads during floods. The road extraction algorithm the authors developed uses U-net model architecture, a fully convolutional neural network, to extract roads from aerial images (satellite images and drone images). Convolutional Neural Network is robust to shadows and water streams, able to obtain the characteristics of roads adequately and most importantly, able to produce output quickly, which is necessary for flood evacuations and relief. The developed system can be deployed as an Application Programming Interface or stand-alone system, loaded on drones, which will provide the users with a map highlighting safe paths to traverse the flooded areas.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133423747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An Application of an Intelligent Data Warehouse for Modelling Spatiotemporal Objects 智能数据仓库在时空对象建模中的应用
Int. J. Big Data Intell. Appl. Pub Date : 2020-01-01 DOI: 10.4018/IJBDIA.2020010103
G. Garani, Nunzio Cassavia, I. Savvas
{"title":"An Application of an Intelligent Data Warehouse for Modelling Spatiotemporal Objects","authors":"G. Garani, Nunzio Cassavia, I. Savvas","doi":"10.4018/IJBDIA.2020010103","DOIUrl":"https://doi.org/10.4018/IJBDIA.2020010103","url":null,"abstract":"Data warehouse (DW) systems provide the best solution for intelligent data analysis and decision-making. Changes applied to data gradually in real life have to be projected to the DW. Slowly changing dimension (SCD) refers to the potential volatility of DW dimension members. The treatment of SCDs has a significant impact over the quality of data analysis. A new SCD type, Type N, is proposed in this research paper, which encapsulates volatile data into historical clusters. Type N preserves complete history of changes, additional tables, columns, and rows are not required, extra join operations are omitted, and surrogate keys are avoided. Type N is implemented and compared to other SCD types. Good candidates for practicing SCDs are spatiotemporal objects (i.e., objects whose shape or geometry evolves slowly over time). The case study used and implemented in this paper concerns shape-shifting constructions (i.e., buildings that respond to changing weather conditions or the way people use them). The results demonstrate the correctness and effectiveness of the proposed SCD Type N.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131212029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data 大数据时代基于深度学习的业务流程再造服务发现
Int. J. Big Data Intell. Appl. Pub Date : 2020-01-01 DOI: 10.4018/IJBDIA.2020010101
Bo Jiang, Chen Junwu, Ye Wang, Liping Zhao, Peng Liu
{"title":"Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data","authors":"Bo Jiang, Chen Junwu, Ye Wang, Liping Zhao, Peng Liu","doi":"10.4018/IJBDIA.2020010101","DOIUrl":"https://doi.org/10.4018/IJBDIA.2020010101","url":null,"abstract":"In recent years, business process re-engineering has played an important role in the development of large-scale web-based applications. To re-engineer business processes, business services are developed and coordinated by reusing a set of open APIs and services on the internet. Yet, the number of services on the internet has grown drastically, making it difficult for them to be discovered to support the changing business goals. One major challenge is therefore to search for a suitable service that matches a specific business goal from a large number of available services in an efficient and effective manner. To address this challenge, this paper proposes a deep learning approach for massive service discovery. The approach, thus called MassRAFF, employs a combination of the recurrent attention and feature fusion methods. This paper first presents the MassRAFF approach and then reports on an experiment for evaluating this approach. The experimental results show that the MassRAFF approach has performed reasonably well and has potential to be improved further in future work.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114158991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network 时间敏感型医疗通信网络中使用软件定义访问的动态路径规划
Int. J. Big Data Intell. Appl. Pub Date : 1900-01-01 DOI: 10.4018/ijbdia.312851
R. Kannamma, K. Umadevi
{"title":"Dynamic Path Planning Using Software-Defined Access in Time-Sensitive Healthcare Communication Network","authors":"R. Kannamma, K. Umadevi","doi":"10.4018/ijbdia.312851","DOIUrl":"https://doi.org/10.4018/ijbdia.312851","url":null,"abstract":"IEEE 802.1 Time-Sensitive Networking (TSN) assures a guaranteed data delivery with limited latency, low jitter, and amazingly low loss of data in handling time-critical traffic. TSN handles different quality of service (QoS) requirements and frame preemption is one of the key features of TSN. In the healthcare sector networking technology preferred by large organizations uses an enormous number of nodes, and thereby, the complexity of the network increases. Since the priority of the medical data varies at times based on the patient's health, dynamic traffic scheduling mechanisms are preferred. To improve the efficiency of the network, the software-defined access mechanism is used to control the network switches and bridges in the time-sensitive network. This work uses reinforcement learning to identify and eliminate the bridges dropping packets, and the alternative path is used to schedule the real-time data traffic. It is perceived that it performs well for the time-critical data in congestion network, increases the throughput, and reduces latency.","PeriodicalId":398232,"journal":{"name":"Int. J. Big Data Intell. Appl.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126314810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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