Big Data Value Engineering for Business Model Innovation

Big data value engineering for business model innovation requires a drastically different approach as compared with methods for engineering value under existing business models. Taking a Design Science approach, we conducted an exploratory study to formulate the requirements for a method to aid in engineering value via innovation. We then developed a method, called Eco-ARCH (Eco-ARCHitecture) for value discovery. This method is tightly integrated with the BDD (Big Data Design) method for value realization, to form a big data value engineering methodology for addressing these requirements. The Eco-ARCH approach is most suitable for the big data context where system boundaries are fluid, requirements are ill-defined, many stakeholders are unknown, design goals are not provided, no central architecture pre-exists, system behavior is non-deterministic and continuously evolving, and co-creation with consumers and prosumers is essential to achieving innovation goals. The method was empirically validated in collaboration with an IT service company in the Electric Power industry

Big data and cloud computing: roles and relationships, techniques and tools

In the past years, the increase in data has been accompanied by rapid growth in various fields. It is difficult to analyze large volumes of data using traditional and relational database technology. Therefore, new databases have emerged, and for this reason, big data has become one of the new topics in IT and business today. Also, the cloud environment is increasingly used to store and process big data. Cloud processing refers to processing anything, including Big Data Analytics, on the "cloud". A "cloud" is a collection of high-powered servers from providers that can often view and query large data sets much faster than a regular computer. These two topics differ from each other in various aspects, including definition, collection references, usage method, form and format, and application. In this research, the dimensions and basic concepts, characteristics, tools and techniques, classification, and communication of data are examined. Big has been dealt with cloud computing, and in addition, storage systems, opportunities and challenges, and big data design principles in the cloud environment have been analyzed

PERANCANGAN BIG DATA JALAN DAN JEMBATAN UNTUK MENDUKUNG KONSTRUKSI 4.0

Abstract  The development of Industry 4.0 has now changed the stages of business processes of a job, with each stage of work being made to be faster, simpler, and more efficient. This also applies in the field of construction, which must transform towards digitization and is known as Construction 4.0. The implementation of Construction 4.0 is marked by the development of information and communication technology utilization in order to achieve high efficiency and good quality construction products. For the development of Construction 4.0 can occur maximally, it needs to be supported by 4 functional components of Industry 4.0, namely the internet of things, internet of services, cyber security, and big data. This paper discusses one component of Industry 4.0, namely the design of big data that is appropriate to support Construction 4.0, especially in the field of roads and bridges. The design of big data is very crucial in Construction 4.0, because the large amount of construction data that is stored, processed, and shared requires a high degree of accuracy and security. Therefore the proper big data design must pay attention to 3 factors, namely the size of the data, the speed of transfer, and variation of data. The concept of big data design for road and bridge data reviewed in this study was taken from a case study of the development of the Indonesian Road Data Center Operation conducted by the Institute of Road Engineering and is the result of the adoption of data center technology that has been developed by South Korea. Keywords: Industry 4.0; Construction 4.0; big data; road and bridge data.  Abstrak Perkembangan Industri 4.0 saat ini telah mengubah tahapan proses bisnis suatu pekerjaan, dengan setiap tahapan pekerjaan dibuat menjadi semakin cepat, sederhana, dan efisien. Hal tersebut juga berlaku di bidang konstruksi, yang harus melakukan transformasi ke arah digitalisasi dan dikenal dengan nama Konstruksi 4.0. Penerapan Konstruksi 4.0 ditandai dengan perkembangan pemanfaatan teknologi informasi dan komunikasi guna mencapai efisiensi yang tinggi dan kualitas produk konstruksi yang baik. Agar perkembangan Konstruksi 4.0 dapat terjadi dengan maksimal, perlu didukung oleh 4 komponen fungsional Industri 4.0, yaitu internet of things, internet of services, cyber security, dan big data. Makalah ini membahas salah satu komponen Industri 4.0, yaitu perancangan big data yang tepat untuk mendukung Konstruksi 4.0, khususnya di bidang jalan dan jembatan. Perancangan big data menjadi sangat krusial dalam Konstruksi 4.0, karena besarnya data konstruksi yang disimpan, diolah, dan dibagikan memerlukan tingkat akurasi dan keamanan yang tinggi. Karena itu, perancangan big data yang tepat harus memerhatikan 3 faktor, yaitu besarnya data, kecepatan transfer, dan variasi data. Konsep perancangan big data untuk data jalan dan jembatan yang dikaji pada studi ini diambil dari studi kasus pengembangan Indonesian Road Data Center Operation yang dilakukan oleh Puslitbang Jalan dan Jembatan dan merupakan hasil adopsi teknologi data center yang telah dikembangkan oleh Korea Selatan. Kata-kata kunci: Industri 4.0; Konstruksi 4.0; big data; data jalan dan jembatan

The role of perceived usefulness and annoyance on programmatic advertising: moderating effect of Internet user privacy and cookies

Purpose: The study of the background to programmatic advertising is of great interest in the context of digital marketing. Therefore, the main aim of this research is to define a structural equation modelling (SEM) model, which allows studying the relationship between the usefulness and privacy of online ads to increase the effectiveness and efficiency of campaigns through the use of computation and big data. Design/methodology/approach: A cross-sectional descriptive study based on the Web Browsers Survey was carried out on a sample of 24,062 Internet users by the Association for Media Research. The partial least squares structural equation modelling method (PLS-SEM) was applied to evaluate the model with the study constructs and test the hypotheses. Findings: The result of this research allows us to know how perceived usefulness (U) and perceived annoyance (A) affect users' privacy concerns (P) and concerns about the storage and use of their data through cookies (C). The authors also seek if there is any relationship between privacy concerns (P) and cookies (C) on users' level of Internet usage (IU). Originality/value: One of the novelties of this study is the consideration not only of Internet user perceptions but also their concerns about privacy and the use of cookies, as key variables in the strategic management of the use of programmatic advertising in digital marketing

Diffusion of Big Data in Indian Scientific Literature: Study of Research Productivity and Scientific Collaboration

Purpose: Big data, a buzzword of the present time, is a term used for extremly large data sets generated from the digital process which is not possible to analyze by traditional methods. These data sets are produced by digital devices such as smart phones, remote sensing, camera, microphones, RFID etc. The literature on big data is growing exponentially since 2011. Big data is tending to establish as a very important research field. This paper aims to explore the evolution, growth and scientific collaboration of the Indian publications in the field of big data. Design/methodology/approach: A survey approach is used in the study while data for the study is collected from Scopus database for the year 2001 to 2015. Bibliometric analysis, visualization and mapping software are used to present the current status, growth trends and collaboration in big data research to examine its diffusion in Indian scientific literature. Findings: We found that the big data research in India is gaining momentum and its diffusion and adoption is increasing tremendously. Conference and seminars are used to do social connect and interaction within the research community. The collaboration at institution level is found usual while collaboration at international level is low. Application of big data in health sciences and life sciences is yet to be explored in comparison to the social sciences and physical sciences. Originality/ Value: This paper presents the growth, trends and collaboration in big data literature by the use of sophisticated bibliometric software and visualization software

Neural ‘Freedoms’:Population, Choice, and Machine Learning

This talk interrogates the history of models of decision making and agency in machine learning, neo-liberal economic thought, and finance in order to interrogate how reactionary politics, population and sex, and technology are being reformulated in our present. While the relationship between the Right, post-truth, suggestion algorithms, and social media has long been documented, rarely has there been extensive investigation of how ideas of choice and freedom become recast in a manner amenable to machine automation and to the particular brands of post-1970s alt-Right discourses. An analysis of this history demonstrates a new logic within algorithmic and artificial intelligent rationalities that intersects with, but is also not merely a recursive repetition of, earlier histories of eugenics and racism. This situation provokes serious challenges to political action, but also to our theorization of histories of race and sex capitalism.  Orit Halpern is Full Professor and Chair of Digital Cultures and Societal Change at Technische Universität Dresden. Her work bridges the histories of science, computing, and cybernetics with design. She completed her Ph.D. at Harvard. She has held numerous visiting scholar positions including at the Max Planck Institute for the History of Science in Berlin, IKKM Weimar,  and at Duke University. She is currently working on two projects. The first is a history of automation, intelligence, and freedom;  the second project examines extreme infrastructures and the history of experimentation at planetary scales in design, science, and engineering. She has also published widely in many venues including Critical Inquiry, Grey Room, and Journal of Visual Culture, and E-Flux. Her first book Beautiful Data: A History of Vision and Reason (2015) investigates histories of big data, design, and governmentality. Her second book The Smartness Mandate (with Robert Mitchell, 2023) is a genealogy of the current obsession with smart technologies and artificial intelligence.  Orit Halpern, Neural ‘Freedoms’: Population, Choice, and Machine Learning, lecture, ICI Berlin, 27 March 2023, video recording, mp4, 01:03:05 <https://doi.org/10.25620/e230327

Evaluation of Hadoop/Mapreduce Framework Migration Tools

In distributed systems, database migration is not an easy task. Companies will encounter challenges moving data including legacy data to the big data platform. This paper reviews some tools for migrating from traditional databases to the big data platform and thus suggests a model, based on the review