Building a Big Data Analytics Service Framework for Mobile Advertising and Marketing

Deng, Lei; Gao, Jerry; Vuppalapati, Chandrasekar

doi:10.1109/bigdataservice.2015.27

Cited by 27 publications

(11 citation statements)

References 1 publication

(1 reference statement)

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“…With the latest generation of smartphones containing at least seven different unique data measurement sensors, precisely how each mobile app interacts with, collects, and handles metadata from those sensors are technical mysteries to many computer engineering labs (Li et al 2017). As users interact with their devices, virtually endless flows of metadata are instantiated that cannot be easily investigated or tracked, and yet are the basis for data mining and analytics efforts (Deng, Gao, and Vuppalapati 2015) propagating $33 billion USD in Apple app advertising in 2017 (Dogtiev 2018). To keep Apple's hands off of metadata while encouraging it to be cultivated in the name of profit depends upon a set of practices that directly engage smartphone users beyond a privacy policy.…”

Section: Part Two: Apple's Privacy Policymentioning

confidence: 99%

Metadata, Jailbreaking, and the Cybernetic Governmentality of iOS: Or, the Need to Distinguish Digital Privacy from digital privacy

Cooke

2020

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Digital privacy tends to be understood as the "top-down" regulation and control of personal information on the behalf of corporate and governmental institutions, realized through various policies and practices. While smartphone manufacturers increasingly innovate and alter their policies and practices to reflect new and ongoing cyber challenges, they tend to emphasize the protection of personal information in the form of content data. On the other hand, there is metadata: the measurements and math of content data. Abundant and ubiquitous in their discrete movements, they are the most precious commodity in the world of big data mobile analytics. Smartphone metadata are also one of the most pressing privacy concerns precisely because it is exceedingly difficult to see, study, and analyze. However, there is another realm through which digital privacy exists: a realm where the inability to see and study metadata is unacceptable. This entirely differently realm is comprised of jailbreakers-a network of hacktivist programmers injecting software-based "tweaks" into Apple mobile devices in ways that reveal metadata to users and allow users to control them. By doing so, jailbreakers allow users to build previously unrealized relationships with metadata and thereby radically distinguishing "top-down" Digital Privacy from "bottom-up" digital privacy. Although Apple has routinely resisted jailbreaking citing fears over device instability, user security vulnerability, and Terms of Use violations, this article reveals that many key privacy-first jailbreaking tweaks undermine Apple's ability to monopolize metadata flows. Theorised through a cybernetic governmentality, this intervention demonstrates the extents to which Apple goes to reify its profit-first vision of information protection, one which insulates many metadata flows from its users. Through this theoretical approach, we as analysts can critically glean awareness of the politics of the (in)visibility and (il)legibility of metadata and the role they play in the discourse on digital, mobile data protection.

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Section: Part Two: Apple's Privacy Policymentioning

confidence: 99%

Metadata, Jailbreaking, and the Cybernetic Governmentality of iOS: Or, the Need to Distinguish Digital Privacy from digital privacy

Cooke

2020

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“…The key content of portrait technology is to label the target [10][11], so as to describe the real state of the object in the form of label. Labels have three basic characteristics: "artificial definition", "semantic information" and "short text" [12]. Based on the artificial generalization and definition, a label has the explicit and unique semantic meaning, which no longer needs the excessive pre-processing such as text analysis.…”

Section: Label Technologymentioning

confidence: 99%

Research and Realization of a Three-level Label System for Railway locomotive Equipment Portrait

2020

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering

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Locomotives are important transportation production equipment for railway industry. Carrying out equipment portrait analysis and label system research is an important way to promote the development of big data technologies in railway locomotive system. Firstly, by briefly summarizing the theoretical knowledge of portrait technology and label technology, this paper researches and designs a technical framework suitable for locomotive label system and locomotive equipment portrait. Secondly, based on this technical framework, the three-level label system of locomotives is introduced in detail. Finally, an optimized clustering algorithm for centroid selection is proposed to form a complete and reasonable locomotive label system. The related research of locomotive label system can help to realize the applications of locomotive equipment portrait, improve the railway transportation production efficiency, and strengthen the safety management ability.

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“…BDA mampu mengambil peran dalam upaya tersebut. Tantangan tersebut dapat diatasi dengan mengembangkan kerangka kerja BDA mobile marketing dan rekomendasi periklanan [19]. Pendekatan yang ditawarkan mendukung sistem rekomendasi iklan berbasis lokasi menggunakan teknologi terkini dan mampu menyajikan keputusan iklan yang relevan untuk pengguna.…”

Section: Big Data Analytics Dalam Bidang Periklananunclassified

A Survey on Big Data Analytics Model

Kurniawan

2019

ITEJ

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Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.

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Building a Big Data Analytics Service Framework for Mobile Advertising and Marketing

Cited by 27 publications

References 1 publication

Metadata, Jailbreaking, and the Cybernetic Governmentality of iOS: Or, the Need to Distinguish Digital Privacy from digital privacy

Metadata, Jailbreaking, and the Cybernetic Governmentality of iOS: Or, the Need to Distinguish Digital Privacy from digital privacy

Research and Realization of a Three-level Label System for Railway locomotive Equipment Portrait

A Survey on Big Data Analytics Model

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