IoT streaming data integration from multiple sources

Tu, Doan Quang; Kayes, A. S. M.; Rahayu, Wenny; Nguyen, Kim Phi Phung

doi:10.1007/s00607-020-00830-9

Cited by 21 publications

(21 citation statements)

References 33 publications

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“…According to the collection of enterprise tax risk information, determine the circular simulation of enterprise tax risk and enterprise operation, and arrange the information fields of the evaluation correlation characteristics of enterprise tax risk. Adjust the process of information de duplication by using the information confusion of repeated risk information [26,27]. Make enterprise tax risk assessment for complex enterprise operation, and make risk correlation expression according to the collected enterprise tax risk information.…”

Section: Extract the Related Characteristics Of Enterprise Tax Riskmentioning

confidence: 99%

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Guo¹

2022

Wireless Communications and Mobile Computing

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Enterprise tax risk involves many aspects. In order to accurately realize enterprise tax risk assessment, an intelligent assessment method of enterprise tax risk based on deep learning is proposed. Collect enterprise tax risk information; reasonably distinguish the same type of risk information according to category, type, and appearance; extract the correlation characteristics of enterprise tax risk assessment; use the correlation characteristics to build a deep learning enterprise tax risk assessment model; search the best parameters of the automatic encoder; build a stacked automatic encoder based on the automatic encoder; combine it with the classifier; and complete the design of enterprise tax risk intelligent evaluation model. The experimental results show that the deviation ratio of Receiver Operating Characteristic (ROC), the risk assessment index of the proposed method, is less than 0.38 as a whole. Therefore, the enterprise tax risk intelligent assessment method based on deep learning is better.

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Section: Extract the Related Characteristics Of Enterprise Tax Riskmentioning

confidence: 99%

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Guo¹

2022

Wireless Communications and Mobile Computing

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“…Further, in order to fetch meaningful information from the collected data, the data stream collector collects and processes the data and finds the relevant information 6 . The generated data stream from IoT devices traverses on the network on varied communication speed.…”

Section: Introductionmentioning

confidence: 99%

“…5 Further, in order to fetch meaningful information from the collected data, the data stream collector collects and processes the data and finds the relevant information. 6 The generated data stream from IoT devices traverses on the network on varied communication speed. When data streams are transmitted from the IoT devices to a data stream collector, it travels over the heterogeneous IoT network and the Internet.…”

mentioning

confidence: 99%

IoT streamed data handling model using delta encoding

Jindal

Kumar

Patidar

2022

Int J Communication

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Summary Internet of Thing (IoT) is a trending internet connectivity extension to connect physical devices on the heterogeneous networks to meet application requirements on various grounds, that is, health, agriculture, industry, and so forth. The amount of streamed data is generated by the IoT devices and processed via IoT device itself or external resources such as Cloud/Fog by considering real‐time constraints. The problem arises in real‐time data transmission when a huge amount of stream data is generated from IoT devices, and it travels through the network and needs to be processed on low‐speed IoT infrastructure. Considering this scenario, there is an extremely requirement for designing a model relying on real‐time stream data handling for speedy transmission considering IoT device real‐time communication constraint. On the other hand, IoT devices are also very sensitive towards energy consumption during the streamed data transmission, and low battery devices become a barrier in data handling in real‐time. To overcome these limitations, a model is proposed to compress and encrypt the data stream during the IoT network transmission. In this model, delta encoding facilitates the compression in form of deltas. To ensure security requirements, a light weighted stream cipher encryption technique is proposed. For the experimentation, a test‐bed setup is prepared to utilize Raspberry Pi (IoT device), CPU, and collectors to collect data stream. The proposed approach is compared with the baseline model, that is, LDPC encoding on temperature sensor datasets. As an outcome, the data are compressed up to 37.59%, and the average transmission time and the average power consumption are reduced by 72.57% and 68.86%, respectively.

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“…Based on data of such different origins, an evident need is to integrate these sources [Asano et al 2019, Tatbul 2010]. Several works have been developed in order to promote query mechanisms capable of integrating streaming data, addressing aspects of semantic optimization [Cappuzzo et al 2020, Alkhamisi andSaleh 2020], continuous queries with sliding windows [Shein and Chrysanthis 2020], time alignment of queries [Tu et al 2020] and aspects related to scalability [Stonebraker and Ilyas 2018].…”

Section: Introductionmentioning

confidence: 99%

Stream and Historical Data Integration using SQL as Standard Language

Amará

Ströele

Braga

et al. 2021

Anais Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD 2021)

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The complexity imposed by data heterogeneity makes it difficult to integrate 'streaming x streaming' and 'streaming x historical' data types. For practical analysis, the enrichment and contextualization process based on historical and streaming data would benefit from approaches that facilitate data integration, abstracting details and formats of the primary sources. This work presents a framework that allows the integration of streaming data and historical data in real-time, abstracting syntactic aspects of queries through the use of SQL as a standard language for querying heterogeneous sources. The framework was evaluated through an experiment using a relational database and real data produced by sensors. The results point to the feasibility of the approach.

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IoT streaming data integration from multiple sources

Cited by 21 publications

References 33 publications

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

Intelligent Assessment Method of Enterprise Tax Risk Based on Deep Learning

IoT streamed data handling model using delta encoding

Stream and Historical Data Integration using SQL as Standard Language

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