A rewrite/merge approach for supporting real-time data warehousing via lightweight data integration

Cuzzocrea, Alfredo; Ferreira, Nickerson; Furtado, Pedro

doi:10.1007/s11227-018-2707-9

Cited by 8 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These technologies offer organizations effective supply chain collaboration among partners, including transport providers, suppliers, and customers, providing higher efficiency, liquidity, and flexibility with lower costs ( Oztemel and Gursev, 2020 ; Winkelhaus and Grosse, 2020 ). Employment of smart technologies in the supply chain delivers warehousing amenities, joint warehousing, logistic, and inventory management on a real-time basis allowing organizations to deal with real-time paradigms ( Ben-Daya et al., 2019 ; Cuzzocrea et al., 2020 ). Furthermore, smart technologies streamline an organization's efficient ordering and transportation, providing competitive supply chain integration ( Ben-Daya et al., 2019 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

Jayashree

Reza

Malarvizhi

et al. 2021

Heliyon

View full text Add to dashboard Cite

Background The current level of industrialization has generated many challenges worldwide, including ecological hazards, climate change, and the overuse of non-renewable natural resources, thereby creating an increasing demand for achieving the goal of the Triple Bottom Line (TBL). In this regard, Industry 4.0 can be used as a crunch point to contribute to the production process that can help achieve sustainable development. Purpose While the Malaysian government proposed the “Industry4ward” approach to enhance technological adoption, there is scarce empirical evidence in the literature that validates SMEs for Industry 4.0. Using Dynamic Capability View (DCV), this study proposes a framework that includes core determinants like top management commitment, supply chain integration, and IT infrastructure, that can significantly influence Industry 4.0 implementation toward achieving TBL sustainability. Design/methodology/approach Employing simple random sampling, the study adopted a quantitative approach based on 199 useable respondent's feedback collected through a survey questionnaire of 900 employees from Malaysian SMEs. The statistical analysis was performed using Structural Equation Modeling (Partial Least Square, SmartPLS 3.3.2). Findings The results show that top management and IT infrastructure significantly impact Industry 4.0 implementation and sustainability. In contrast, the analysis also demonstrates that supply chain integration is insignificant to Industry 4.0 implementation in SMEs. The findings also indicate that the relationship between the determinants of Industry 4.0 and TBL sustainability can be mediated by the “effective implementation” of Industry 4.0. Recommendations The study highlights the practical consequences of the role and use of the determinants in Industry 4.0 implementation. Its findings help managers and policy-makers to optimize value creation to achieve sustainable development goals. Limitations and future research Focusing only on Malaysian manufacturing SMEs may restrict the generalization of the study; thus, a benchmarking analysis from other industrial settings is encouraged. The questionnaire-based survey is a further limitation of the study.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

Jayashree

Reza

Malarvizhi

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…The tool is designed to provide various interfaces to perform modelling with simplified and adjustable one. Similarly to make the data integration as simpler one, a rewrite/merge scheme is presented in Cuzzocrea and Furtado ( 2018 ), which is a novel data warehousing model. The model works on two modes: in static phase, it performs rewrite, and in dynamic phase, it performs merging to support real-time conditions.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Dynamic multi-variant relational scheme-based intelligent ETL framework for healthcare management

Manickam

Minu

2022

Soft Comput

View full text Add to dashboard Cite

The growth of information technology has opened the gate for the organizations to maintain their data in various forms and at various volumes. This increases the volume and dimension of data being maintained. However, they store their data in their data servers or in cloud environment. Such data have been used to generate various intelligence to support various problems. To support such analysis process, different data have been used and the big data comes to play in this part. Optimizing techniques to help improve the process of ETL could greatly help in real-time analysis of data. ETL optimization could be achieved through several factors simplest being increasing the frequency of the process. Other ways to achieve optimization are through the use of various architectures, programming models, intelligence in transformation and security. To improve the performance of ETL, an efficient dynamic multi-variant relational intelligent ETL framework has been presented in this article. The distributed approach maintains various ontology’s and data dictionaries which have been dynamically updated by different threads of ETL process. Initially, the process is start by applying the extraction process which extracts the data from different sources and finds set of dimensions and their characteristics. Such data extracted have been verified over the data dictionary. Further, the relational score has been measured for each data source with the existing one. Similarly, the method computes the value of multi-variant relational similarity (MVRS) for the data obtained from a single source. This will be performed by different threads of ETL process. According to the value of MVRS, the method performs map reduce and merging of data. According to the value of MVRS the method selects the data node and merges the data to store in the data warehouse. The threads of ETL are capable of reading the changes in data dictionaries and ontology’s to iterate the process of transformation and loading. The method improves the performance of ETL with least time complexity and higher performance.

show abstract

“…They also analyzed memory and time requirements. In addition, a modified well-known Star Schema Benchmark (SSB), called SSB-RT, is used during experimental evaluation of rewrite/merge framework by [44], which embeds real-time features, as well as TPC-H benchmark.…”

Section: Assessment Of Rq4: What Evidence Have Been Reported Whilementioning

confidence: 99%

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

Mehmood

Anees

2020

IEEE Access

View full text Add to dashboard Cite

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

show abstract

A rewrite/merge approach for supporting real-time data warehousing via lightweight data integration

Cited by 8 publications

References 52 publications

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

RETRACTED ARTICLE: Dynamic multi-variant relational scheme-based intelligent ETL framework for healthcare management

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

Contact Info

Product

Resources

About