A Data as a Product Model for Future Consumption of Big Stream Data in Clouds

Huang, Guangyan; He, Jing; Chi, Chi‐Hung; Zhou, Wanlei; Zhang, Yanchun

doi:10.1109/scc.2015.43

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…In software engineering, Meyer (2001) proposed software as a product and a service. Sidi et al (2012) and Huang et al (2015) viewed data as a product, whereas Psomakelis et al (2020) viewed data as a service. Whether they are a product or a service, in this paper, a product is defined as a broader concept, including physical and digital products/services/data regardless of their attributes.…”

Section: Definitions Of Different Development Modelsmentioning

confidence: 99%

Continuous cycles of data-enabled design: reimagining the IoT development process

Lee

Cooper

Hands

et al. 2022

AIEDAM

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With the emergence of Internet of Things (IoT) as a new source of “big” data and value creation, businesses encounter novel opportunities as well as challenges in IoT design. Although recent research argues that digital technology can enable new kinds of development processes that are distinctive from their counterparts in the 20th century, minimal attention has been focused on the IoT design process. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.

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Section: Definitions Of Different Development Modelsmentioning

confidence: 99%

Continuous cycles of data-enabled design: reimagining the IoT development process

Lee

Cooper

Hands

et al. 2022

AIEDAM

View full text Add to dashboard Cite

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Section: Data-as-a-service (Daas) and Data-as-a-product (Daap)mentioning

confidence: 99%

A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

Olasz¹,

Thai²,

Kristóf³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. <br><br> This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (<a href="https://github.com/posseidon/IQLib/"target="_blank">https://github.com/posseidon/IQLib/</a>) developed in the frame of the IQmulus EU FP7 research and development project (<a href="http://www.iqmulus.eu"target="_blank">http://www.iqmulus.eu</a>). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.

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“…Within such centralized solution the data management (frequent update, cleaning, conversions) are served by the provider (Olson, 2009). Data-as-a-Product concept defined by Huang (2015) is a "small sized summary of the original data and can directly answer user's queries". In the next section we are introducing a comparison matrix for current technologies from the Geospatial aspects of the usage.…”

Section: Data-as-a-service (Daas) and Data-as-a-product (Daap)mentioning

confidence: 99%

A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

Olasz¹,

Thai

Kristóf³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (https://github.com/posseidon/IQLib/) developed in the frame of the IQmulus EU FP7 research and development project (http://www.iqmulus.eu). The data distribution framework has no limitations on programming language environment and can execute scripts (and workflows) written in different development frameworks (e.g. Python, R or C#). It is capable of processing raster, vector and point cloud data. The above-mentioned prototype is presented through a case study dealing with country-wide processing of raster imagery. Further investigations on algorithmic and implementation details are in focus for the near future.

show abstract

A Data as a Product Model for Future Consumption of Big Stream Data in Clouds

Cited by 6 publications

References 20 publications

Continuous cycles of data-enabled design: reimagining the IoT development process

Continuous cycles of data-enabled design: reimagining the IoT development process

A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments

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