General-Purpose Big Data Processing Systems

Sakr, Sherif

doi:10.1007/978-3-319-38776-5_2

Cited by 7 publications

(7 citation statements)

References 160 publications

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“…Due to variety of BD sources, an integration of that data sources is a core challenge both for data collecting and data access. Other technologies provide inmemory data access that enable run applications many times faster, as well as support real-time processing, on-line machine learning, and continuous calculations [44,45]. E2.6.…”

Section: E24 Data Queryingmentioning

confidence: 99%

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

et al. 2021

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The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

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Section: E24 Data Queryingmentioning

confidence: 99%

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

et al. 2021

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“…In addition, many organizations are using these technologies for more traditional use cases, such as preprocessing and the staging of information to be loaded into a data warehouse.” Such alternatives for parallel database systems for parallel processing and data analysis (e.g. Vertica, Teradata, Netezza, SQL Server, Greenplum, ParAccel) are expensive, difficult for administration, and have deficiency in fault tolerance and processing speed for long-running queries (Pavlo et al , 2009; Sakr, 2016). In practice, Hadoop as an open-source project has achieved great success, with increasing momentum in research and development in educational and business domains.…”

Section: Discussionmentioning

confidence: 99%

“…This technology, with modules of importance and their combination, has enabled even small companies to collect and analyze Big Data in order to gain a competitive advantage. Hadoop as open-source software provides a tool to process vast amounts of data easily and cost-effectively (Sakr, 2016). On the flip side, organizations need a data scientist to establish a workflow and fully utilize the advantages of Big Data analytics.…”

Section: Discussionmentioning

confidence: 99%

A Big Data smart library recommender system for an educational institution

Simović

2018

LHT

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Purpose With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. Design/methodology/approach This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. Findings As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. Social implications The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. Originality/value A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

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“…Pig Latin is a high-level scripting language generated by an open-source framework called Apache Pig [23]. Pig Latin was developed by Yahoo.…”

Section: Data Queryingmentioning

confidence: 99%

A Comprehensive Review of Tools & Techniques for Big Data Analytics

Dhankhar¹

2019

IJETER

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The changing technology prospect is described by the phrase Big Data that resulted in a large amount of data, a greater variety of data sources, a continuous flow of data and multiple data formats. As such data is growing rapidly, there is a need for advanced analytic techniques that operates on such data and extracts effective information, unknown patterns, and relationships that help in making decisions. Big Data Analytics provides such valuable insight. In this paper, we start with a definition of Big Data. Then we provide the systematic structure that divides the Big Data system into five sections namely data generation, acquisition and storage, data processing, data querying, and data analytics. This paper is also providing a brief overview of different techniques and technology used in Big Data Analytics.

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General-Purpose Big Data Processing Systems

Cited by 7 publications

References 160 publications

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

A Big Data smart library recommender system for an educational institution

A Comprehensive Review of Tools & Techniques for Big Data Analytics

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