Big Data 2.0 Processing Systems: Taxonomy and Open Challenges

Bajaber, Fuad; Elshawi, Radwa; Batarfi, Omar; Altalhi, Abdulrahman H.; Barnawi, Ahmed; Sakr, Sherif

doi:10.1007/s10723-016-9371-1

Cited by 51 publications

(29 citation statements)

References 47 publications

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“…Following the taxonomies found in [6], [126], we distinguish four types of big data processing models: (i) general purpose -platforms to process big data that make little assumptions about the data characteristics and the executed algorithms, (ii) SQL-like -platforms focusing on scalable processing of structured and tabular data, (iii) graph processing -platforms focusing on the processing of large graphs, and (iv) stream processing -platforms dealing large-scale data that continuously arrive to the system in a streaming fashion. Hive, HAWQ, Apache Drill, and Tajo belong to the SQL-like type.…”

Section: Programming Models and Platformsmentioning

confidence: 99%

A Survey on Big Data for Network Traffic Monitoring and Analysis

D’Alconzo

Drago

Morichetta

et al. 2019

IEEE Trans. Netw. Serv. Manage.

127

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Network Traffic Monitoring and Analysis (NTMA) represents a key component for network management, especially to guarantee the correct operation of large-scale networks such as the Internet. As the complexity of Internet services and the volume of traffic continue to increase, it becomes difficult to design scalable NTMA applications. Applications such as traffic classification and policing require real-time and scalable approaches. Anomaly detection and security mechanisms require to quickly identify and react to unpredictable events while processing millions of heterogeneous events. At last, the system has to collect, store, and process massive sets of historical data for post-mortem analysis. Those are precisely the challenges faced by general big data approaches: Volume, Velocity, Variety, and Veracity. This survey brings together NTMA and big data. We catalog previous work on NTMA that adopt big data approaches to understand to what extent the potential of big data is being explored in NTMA. This survey mainly focuses on approaches and technologies to manage the big NTMA data, additionally briefly discussing big data analytics (e.g., machine learning) for the sake of NTMA. Finally, we provide guidelines for future work, discussing lessons learned, and research directions.

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Section: Programming Models and Platformsmentioning

confidence: 99%

A Survey on Big Data for Network Traffic Monitoring and Analysis

D’Alconzo

Drago

Morichetta

et al. 2019

IEEE Trans. Netw. Serv. Manage.

127

View full text Add to dashboard Cite

show abstract

“…However, the performance evaluation was discussed only on theoretical grounds. A taxonomy and detailed analysis of the state of the art in big data 2.0 processing systems were presented in [12]. The focus of the study was to identify current research challenges and highlight opportunities for new innovations and optimization for future research and development.…”

Section: Related Workmentioning

confidence: 99%

Big Data in Cloud Computing: A Resource Management Perspective

Ullah

Awan

Khiyal

2018

Scientific Programming

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The modern day advancement is increasingly digitizing our lives which has led to a rapid growth of data. Such multidimensional datasets are precious due to the potential of unearthing new knowledge and developing decision-making insights from them. Analyzing this huge amount of data from multiple sources can help organizations to plan for the future and anticipate changing market trends and customer requirements. While the Hadoop framework is a popular platform for processing larger datasets, there are a number of other computing infrastructures, available to use in various application domains. The primary focus of the study is how to classify major big data resource management systems in the context of cloud computing environment. We identify some key features which characterize big data frameworks as well as their associated challenges and issues. We use various evaluation metrics from different aspects to identify usage scenarios of these platforms. The study came up with some interesting findings which are in contradiction with the available literature on the Internet.

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“…Even though automobiles today contain an impressive amount of processing power, the amount of information flowing back and forth inside them requires technology with considerable storage capabilities that can handle sophisticated processing and analytical functions [1] [2]. An ideal task for cloud computing [3] and big data [4] processing, used by cars on the road. In our paper we detail the evolution of a cloud based framework and services built on top for handling and processing vehicular data.…”

Section: Introductionmentioning

confidence: 99%

A novel IoT platform for the era of connected cars

Marosi

Lovas

Kisari

et al. 2018

2018 IEEE International Conference on Future IoT Technologies (Future IoT)

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Abstract-The race among manufacturers to build convenient, safe, and autonomous Connected Cars by applying the latest digital technologies, and ultimately a completely self-driving vehicle, is already underway. One of the cornerstones of such vehicles is the continuous ingestion of massive amount of data from wide variety of hardware components, including sensors, onboard cameras, and further external sources. Cloud computing and big data processing are ideal candidates and already proven technologies in order to store and process the heterogeneous, rapidly growing, and large-scale data sets. The cloud may act as a kind of central hub or as an Internet of Things (IoT) back-end where the sensor and the other available data can be gathered while also offering an elastic platform where the vast amount of data can be processed, analyzed and distributed real-time. In our paper we detail the evolution of a cloud-based, scalable IoT back-end framework and services built on top for handling and processing vehicular data in various use case scenarios: CAN data collection, remote device flashing, Eco-driving, weather report and forecast. The first version is an Infrastructure-as-a-Service (IaaS) solution with a reference implementation deployed on an OpenNebula based cloud. The second iteration runs on a private Platform-as-a-Service (PaaS) cloud built on the Cloud Foundry platform within the premises of an automotive supplier company. Both variants have been successfully evaluated and validated with benchmarks.

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Big Data 2.0 Processing Systems: Taxonomy and Open Challenges

Cited by 51 publications

References 47 publications

A Survey on Big Data for Network Traffic Monitoring and Analysis

A Survey on Big Data for Network Traffic Monitoring and Analysis

Big Data in Cloud Computing: A Resource Management Perspective

A novel IoT platform for the era of connected cars

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