A Taxonomy on Big Data: Survey

Patgiri, Ripon

doi:10.48550/arxiv.1808.08474

Cited by 4 publications

(8 citation statements)

References 41 publications

(55 reference statements)

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“…In fundamental, big data refers to massive datasets with heterogeneous formats: structured, unstructured, and semi-structured data, which has three key characteristics: large volume with the incessant generation from millions of devices and applications, high velocity with the real-time data acquisition and processing, and high variety with various source and multiple formats. Taxonomy of big data Here we present a taxonomy of big data in various perspectives, including data domain, storage infrastructure, compute infrastructure, and AI-based data processing [30]. This taxonomy is illustrated in Fig.…”

Section: B Big Datamentioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

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Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

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Section: B Big Datamentioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

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“…The architecture of storage system is classified into three types, namely, Direct-Attached Storage (DAS), Network Attached Storage (NAS), and Storage Area Network (SAN) [77].…”

Section: Classification Of Storage System Architecturementioning

confidence: 99%

“…(2) Block Storage: In case of block storage system [57,77], the data are stored in blocks of storage media. This storage type depends on the network of storage area.…”

Section: Types Of Big Data Storagementioning

confidence: 99%

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A Survey on Large Scale Metadata Server for Big Data Storage

Patgiri¹,

Nayak²

2020

Preprint

Self Cite

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Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are growing at an exponential pace. The access mechanism of these data silos are defined by metadata. The metadata are decoupled from data server for various beneficial reasons. For instance, ease of maintenance. The metadata are stored in metadata server (MDS). Therefore, the study on the MDS is mandatory in designing of a large scale storage system. The MDS requires many parameters to augment with its architecture. The architecture of MDS depends on the demand of the storage systemâĂŹs requirements. Thus, MDS is categorized in various ways depending on the underlying architecture and design methodology. The article surveys on the various kinds of MDS architecture, designs, and methodologies. This article emphasizes on clustered MDS (cMDS) and the reports are prepared based on a) Bloom filter-based MDS, b) Client-funded MDS, c) Geo-aware MDS, d) Cache-aware MDS, e) Load-aware MDS, f) Hash-based MDS, and g) Tree-based MDS. Additionally, the article presents the issues and challenges of MDS for mammoth sized data.

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“…Nowadays, machine learning (ML) libraries have been frequently used in a wide variety of domains including but not limited to image classification [4,22], big data analysis [27], pattern recognition [20], self-driving [17,30,33] and Natural Language Processing [6,25,31]. These ML libraries can be vulnerable to many attacks [21], and failures to detect the vulnerabilities in these libraries could cause catastrophic outcomes, such as car accidents [11].…”

Section: Introductionmentioning

confidence: 99%

Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries

Harzevili¹,

Shin²,

Wang³

et al. 2022

Preprint

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The application of machine learning (ML) libraries has been tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerabilities of such libraries could result in irreparable consequences. However, the characteristics of software security vulnerabilities have not been well studied. In this paper, to bridge this gap, we take the first step towards characterizing and understanding the security vulnerabilities of five well-known ML libraries, including TensorFlow, PyTorch, Scikit-learn, Pandas, and Numpy. To do so, we collected 596 security vulnerabilities to explore five major factors: 1) vulnerability types, 2) root causes, 3) symptoms, 4) fixing patterns, and 5) fixing efforts of security vulnerabilities in ML libraries. The findings of this study can help developers understand the characteristics of security vulnerabilities across different ML libraries. To make our finding actionable, we further developed DeepMut, an automated mutation testing tool, as a proof-of-concept application of our findings. DeepMut is designed to assess the adequacy of existing test suites of ML libraries against security-aware mutation operators extracted from the vulnerabilities studied in this work. We applied Deep-Mut on the TensorFlow kernel module and found more than 1k alive mutants not covered by the existing test suits, which have been confirmed by the development team of TensorFlow. The results demonstrate the usefulness of our findings.

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A Taxonomy on Big Data: Survey

Cited by 4 publications

References 41 publications

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

A Survey on Large Scale Metadata Server for Big Data Storage

Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries

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