A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework

Farhan, Muhammad; Jabbar, Sohail; Aslam, Muhammad; Ahmad, Awais; Iqbal, Muhammad Munwar; Khan, Murad; Maria, Martinez-Enriquez Ana

doi:10.1007/s10766-017-0553-7

Cited by 20 publications

(8 citation statements)

References 47 publications

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“…Furthermore, optimal checks on the strategies that export on-chain data to the off-chain database and where analytics are applied need to be explored and developed. Another challenge is the maintenance of immutability in the exported data and data security in the context of analytics over both on-chain and off-chain data [84]. • Development of smart contract templates-The programmed logic of Blockchain smart contracts is usually manually developed after studying related documents.…”

Section: Research Directionsmentioning

confidence: 99%

Blockchain-enabled supply chain: analysis, challenges, and future directions

et al. 2020

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Managing the integrity of products and processes in a multi-stakeholder supply chain environment is a significant challenge. Many current solutions suffer from data fragmentation, lack of reliable provenance, and diverse protocol regulations across multiple distributions and processes. Amongst other solutions, Blockchain has emerged as a leading technology, since it provides secure traceability and control, immutability, and trust creation among stakeholders in a low cost IT solution. Although Blockchain is making a significant impact in many areas, there are many impediments to its widespread adoption in supply chains. This article is the first survey of its kind, with detailed analysis of the challenges and future directions in Blockchain-enabled supply chains. We review the existing digitalization of the supply chain including the role of GS1 standards and technologies. Current use cases and startups in the field of Blockchain-enabled supply chains are reviewed and presented in tabulated form. Technical and non-technical challenges in the adoption of Blockchain for supply chain applications are critically analyzed, along with the suitability of various consensus algorithms for applications in the supply chain. The tools and technologies in the Blockchain ecosystem are depicted and analyzed. Some key areas as future research directions are also identified which must be addressed to realize mass adoption of Blockchain-based in supply chain traceability. Finally, we propose MOHBSChain, a novel framework for Blockchain-enabled supply chains.

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Section: Research Directionsmentioning

confidence: 99%

Blockchain-enabled supply chain: analysis, challenges, and future directions

et al. 2020

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“…This framework is implemented based on Spark with Hadoop and MapReduce technique that process and handle huge amount of data in real-time. In addition, another IoT based framework developed in a study [60] to analyze students' performance on real-time basis based on sensor and screen activity data. They applied visual attention techniques for their analysis including: Top-down visual attention, Visual saliency/bottom-up attention, Saliency using natural statistics and A boolean map based saliency.…”

Section: B Assessment Of Rq2: Which Challenges Have Been Faced Durinmentioning

confidence: 99%

Challenges and Solutions for Processing Real-Time Big Data Stream: A Systematic Literature Review

Mehmood

Anees

2020

IEEE Access

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Contribution: Recently, real-time data warehousing (DWH) and big data streaming have become ubiquitous due to the fact that a number of business organizations are gearing up to gain competitive advantage. The capability of organizing big data in efficient manner to reach a business decision empowers data warehousing in terms of real-time stream processing. A systematic literature review for real-time stream processing systems is presented in this paper which rigorously look at the recent developments and challenges of real-time stream processing systems and can serve as a guide for the implementation of real-time stream processing framework for all shapes of data streams. Background: Published surveys and reviews either cover papers focusing on stream analysis in applications other than real-time DWH or focusing on extraction, transformation, loading (ETL) challenges for traditional DWH. This systematic review attempts to answer four specific research questions. Research Questions: 1)Which are the relevant publication channels for real-time stream processing research? 2) Which challenges have been faced during implementation of real-time stream processing? 3) Which approaches/tools have been reported to address challenges introduced at ETL stage while processing real-time stream for real-time DWH? 4) What evidence have been reported while addressing different challenges for processing real-time stream? Methodology: A systematic literature was conducted to compile studies related to publication channels targeting real-time stream processing/joins challenges and developments. Following a formal protocol, semi-automatic and manual searches were performed for work from 2011 to 2020 excluding research in traditional data warehousing. Of 679,547 papers selected for data extraction, 74 were retained after quality assessment. Findings: This systematic literature highlights implementation challenges along with developed approaches for real-time DWH and big data stream processing systems and provides their comparisons. This study found that there exists various algorithms for implementing real-time join processing at ETL stage for structured data whereas less work for un-structured data is found in this subject matter. INDEX TERMS Real-time stream processing, big data streaming, structured/un-structured data, ETL, systematic literature review Publication source Channel References No. %age IEEE Transactions on Knowledge and Data Engineering

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“…It can be translated to syntax trees which represents the comprehensive smooth of expressions. The IoT based network is used in students' teacher interaction for online assessment of students 25 …”

Section: Literature Reviewmentioning

confidence: 99%

“…The IoT based network is used in students' teacher interaction for online assessment of students. 25 In the work of Lazar and Banias, 26 the authors described three steps recognition algorithm built on abstract parse tree method to verify clones between C programs. The proposed source code clone extraction algorithm has three phases elementary, classification and generalization.…”

Section: Literature Reviewmentioning

confidence: 99%

Software plagiarism detection in multiprogramming languages using machine learning approach

Ullah

Wang

Farhan

et al. 2018

Concurrency and Computation

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Summary The Software plagiarism, which arises the problem of software piracy is a growing major concern nowadays. It is a serious risk to the software industry that gives huge economic damages every year. The customers may develop a modified version of the original software in other types of programming languages. Furthermore, the plagiarism detection in different types of source codes is a challenging task because each source code may have specific syntax rules. In this paper, we proposed a methodology for software plagiarism detection in multiprogramming languages based on machine learning approaches. The Principal Component Analysis (PCA) is applied for features extraction from source codes without losing the actual information. It extracts features by factor analysis and converts the dataset into normalized linear principal components which are further useful for predictions analysis. Then, the multinomial logistic regression model (MLR) is applied to these components to classify the source codes documents based on predictions. It gives the generalization of logistic regression to handle multiclass problems. Further, the predictors' performance in MLR is evaluated by 2 tailed z test. To apply the experiment, the dataset is collected in five different and popular languages, ie, C, C++, Java, C#, and Python. Each programming language taken in two different case studies, ie, binary search and Stack.

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A Real-Time Data Mining Approach for Interaction Analytics Assessment: IoT Based Student Interaction Framework

Cited by 20 publications

References 47 publications

Blockchain-enabled supply chain: analysis, challenges, and future directions

Blockchain-enabled supply chain: analysis, challenges, and future directions

Challenges and Solutions for Processing Real-Time Big Data Stream: A Systematic Literature Review

Software plagiarism detection in multiprogramming languages using machine learning approach

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