scholarly journals Caring for (Big) Data: An Introduction to Research Methodologies and Ethical Challenges in Digital Migration Studies

2021 ◽  
pp. 1-21
Author(s):  
Marie Sandberg ◽  
Luca Rossi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Activity ◽  
Data Access ◽  
Digital Data ◽  
Data Sets ◽  
Ethical Challenges ◽  
Migration Studies ◽  
Critical Approach ◽  
Migration Research

AbstractDigital technologies present new methodological and ethical challenges for migration studies: from ensuring data access in ethically viable ways to privacy protection, ensuring autonomy, and security of research participants. This Introductory chapter argues that the growing field of digital migration research requires new modes of caring for (big) data. Besides from methodological and ethical reflexivity such care work implies the establishing of analytically sustainable and viable environments for the respective data sets—from large-scale data sets (“big data”) to ethnographic materials. Further, it is argued that approaching migrants’ digital data “with care” means pursuing a critical approach to the use of big data in migration research where the data is not an unquestionable proxy for social activity but rather a complex construct of which the underlying social practices (and vulnerabilities) need to be fully understood. Finally, it is presented how the contributions of this book offer an in-depth analysis of the most crucial methodological and ethical challenges in digital migration studies and reflect on ways to move this field forward.

Neural Network for Big Data Sets

2022 ◽  
pp. 41-67
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Computer Science ◽  
Large Scale ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Large Scale Data ◽  
Commercial Applications ◽  
Novel Model

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

Research Challenges in Big Data Analytics

2017 ◽  
pp. 83-99
Author(s):  
Sivamathi Chokkalingam ◽  
Vijayarani S.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
New Technologies ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Data Types ◽  
Customer Preferences ◽  
Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

On the Effectiveness of Hybrid Canopy with Hoeffding Adaptive Naive Bayes Trees

2017 ◽  
Vol 8 (2) ◽  
pp. 30-43
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Big Data ◽  
Clustering Analysis ◽  
Large Scale ◽  
Data Sets ◽  
Recent Past ◽  
Large Scale Data ◽  
Huge Data ◽  
With Memory ◽  
Memory Constraints ◽  
Scale Data

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.

scholarly journals Identifying global synchronies in marine zooplankton populations: issues and opportunities

2004 ◽  
Vol 61 (4) ◽  
pp. 445-456 ◽  
Author(s):  
R.Ian Perry ◽  
Harold P Batchelder ◽  
David L Mackas ◽  
Sanae Chiba ◽  
Edward Durbin ◽  
...  
Keyword(s):  
Large Scale ◽  
International Comparisons ◽  
Data Access ◽  
Ecosystem Change ◽  
Data Sets ◽  
Time Lags ◽  
Structural Constraints ◽  
Spatial And Temporal Scales ◽  
Marine Zooplankton

Abstract Analyses of the influences of climate variability on local zooplankton populations and those within ocean basins are relatively recent (past 5–10 years). What is lacking are comparisons of zooplankton population variability among the world's oceans, in contrast to such global comparisons of fish populations. This article examines the key questions, capabilities, and impediments for global comparisons of zooplankton populations using long-term (>10 year) data sets. The key question is whether global synchronies in zooplankton populations exist. If yes, then (i) to what extent are they driven by “bottom-up” (productivity) or “top-down” (predation) forcing; (ii) are they initiated by persistent forcing or by episodic events whose effects propagate through the system with different time-lags; and (iii) what proportion of the biological variance is caused directly by physical forcing and what proportion might be caused by non-linear instabilities in the biological dynamics (e.g. through trophodynamic links)? The capabilities are improving quickly that will enable global comparisons of zooplankton populations. Several long-term sampling programmes and data sets exist in many ocean basins, and the data are becoming more available. In addition, there has been a major philosophical change recently that now recognizes the value of continuing long-term zooplankton observation programmes. Understanding of life-history characteristics and the ecosystem roles of zooplankton are also improving. A first and critical step in exploring possible synchrony among zooplankton from geographically diverse regions is to recognize the limitations of the various data sets. There exist several impediments that must be surmounted before global comparisons of zooplankton populations can be realized. Methodological issues concerned with the diverse spatial and temporal scales of “monitored” planktonic populations are one example. Other problems include data access issues, structural constraints regarding funding of international comparisons, and lack of understanding by decision-makers of the value of zooplankton as indicators of ecosystem change. We provide recommendations for alleviating some of these impediments, and suggest a need for an easily understood example of global synchrony in zooplankton populations and the relation of those signals to large-scale climate drivers.

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

Interoperability in Internet of Media Things and Integration Big Media

2022 ◽  
pp. 59-79
Author(s):  
Dragorad A. Milovanovic ◽  
Vladan Pantovic
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Reference Model ◽  
Big Data Analytics ◽  
Multimedia Data ◽  
Data Sets ◽  
Large Scale Systems ◽  
New Class ◽  
Media Applications

Multimedia-related things is a new class of connected objects that can be searched, discovered, and composited on the internet of media things (IoMT). A huge amount of data sets come from audio-visual sources or have a multimedia nature. However, multimedia data is currently not incorporated in the big data (BD) frameworks. The research projects, standardization initiatives, and industrial activities for integration are outlined in this chapter. MPEG IoMT interoperability and network-based media processing (NBMP) framework as an instance of the big media (BM) reference model are explored. Conceptual model of IoT and big data integration for analytics is proposed. Big data analytics is rapidly evolving both in terms of functionality and the underlying model. The authors pointed out that IoMT analytics is closely related to big data analytics, which facilitates the integration of multimedia objects in big media applications in large-scale systems. These two technologies are mutually dependent and should be researched and developed jointly.

Big Data and Digital Analytics

2022 ◽  
pp. 571-589
Author(s):  
Sumathi Doraikannan ◽  
Prabha Selvaraj
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Digital Data ◽  
Detailed Knowledge ◽  
Data Sets ◽  
Business Values ◽  
Business Operations ◽  
It Systems ◽  
Basic Concepts ◽  
And Storage

Data becomes big data when then the size of data exceeds the ability of our IT systems in terms of 3Vs (volume, velocity, and variety). When the data sets are large and complex, it becomes a great difficult task for handling such voluminous data. This chapter will provide a detailed knowledge of the major concepts and components of big data and also the transformation of big data in to business operations. Collection and storage of big data will not help out in creation of business values. Values and importance are created once when the action starts on data by performing an analysis. Hence, this chapter provides a view on various kinds of analysis that can be done with big data and also the differences between traditional analytics and big data analytics. The transformation of digital data into business values could be in terms of reports, research analyses, recommendations, predictions, and optimizations. In addition to the concept of big data, this chapter discuss about the basic concepts of digital analytics, methods, and techniques for digital analysis.

An Enhancement of Cloud Based Sentiment Analysis and BDAAs Using SVM Based Lexicon Dictionary and Adaptive Resource Scheduling

2018 ◽  
Vol 15 (2) ◽  
pp. 437-445 ◽  
Author(s):  
S. Radha ◽  
C. Nelson Kennedy Babu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Resource Scheduling ◽  
Data Integrity ◽  
Digital Data ◽  
Large Set ◽  
Compression Technique ◽  
Adaptive Resource

At present, the cloud computing is emerging technology to run the large set of data capably, and due to fast data growth, processing of large scale data is becoming a main point of information method and customers can estimate the quality of brands of products employing the information given by new digital marketing channels in social media. Thus, every enterprise requires finding and analyzing a big amount of digital data in order to develop their reputation among the customers. Therefore, in this paper, SLA (Service Level Agreement) based BDAAs (Big Data Analytic Applications) using Adaptive Resource Scheduling and big data with cloud based sentiment analysis is proposed to provide the deep web mining, QoS and to analyze the customer behaviors about the product. In this process, the spatio-temporal compression technique can be applied to data compression for reduction of big data. The data is classified in to positive, negative or neutral by employing the SVM with lexicon dictionary based on the customers' behaviors about brand or products. In cloud computing environment, complex to the reduction of resources cost and fluctuation of resource requirements with BDAAs. As a result, it is needed to have a common Analytics as a Service (AaaS) platform that provides a BDAAs to customers in different fields as unpreserved services in a simple to utilize a way with lower cost. Therefore, SLA based BDAAs is developed to utilize the adaptive resource scheduling depending on the customer behaviors and it can provide visualization and data integrity. Our method can give privacy of cloud owner's information with help of data integrity and authentication process. Experimental results of proposed system shows that the sentiment analysis method for online product using cloud based big data is able to classify the opinions of customers accurately and effective of the algorithm in guarantee of SLA.

scholarly journals Towards Big data Governance in Cybersecurity

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Longzhi Yang ◽  
Jie Li ◽  
Noe Elisa ◽  
Tom Prickett ◽  
Fei Chao
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Access ◽  
Quality Data ◽  
Data Sets ◽  
Data Governance ◽  
Business Decisions ◽  
Regulatory Frameworks ◽  
Big Data Technologies ◽  
Cooperative Decision Making

AbstractBig data refers to large complex structured or unstructured data sets. Big data technologies enable organisations to generate, collect, manage, analyse, and visualise big data sets, and provide insights to inform diagnosis, prediction, or other decision-making tasks. One of the critical concerns in handling big data is the adoption of appropriate big data governance frameworks to (1) curate big data in a required manner to support quality data access for effective machine learning and (2) ensure the framework regulates the storage and processing of the data from providers and users in a trustworthy way within the related regulatory frameworks (both legally and ethically). This paper proposes a framework of big data governance that guides organisations to make better data-informed business decisions within the related regularity framework, with close attention paid to data security, privacy, and accessibility. In order to demonstrate this process, the work also presents an example implementation of the framework based on the case study of big data governance in cybersecurity. This framework has the potential to guide the management of big data in different organisations for information sharing and cooperative decision-making.

scholarly journals The taming of chaos: Optimal cities and the state of the art in urban systems research

Urban Studies ◽  
2021 ◽  
pp. 004209802110128
Author(s):  
Linnet Taylor
Keyword(s):  
Big Data ◽  
Urban Systems ◽  
Data Sets ◽  
Critical Approach ◽  
City Life ◽  
Urban Dynamics ◽  
Systems Research ◽  
New Perspective ◽  
Technology Users ◽  
The City

What can urban big data research tell us about cities? While studying cities as complex systems offers a new perspective on urban dynamics, we should dig deeper into the epistemological claims made by these studies and ask what it means to distance the urban researcher from the city. Big data research has the tendency to flatten our perspective: it shows us technology users and their interactions with digital systems but does so often at the expense of the informal and irregular aspects of city life. It also presents us with the city as optimisable system, offering up the chance to engineer it for particular forms of efficiency or productivity. Both optimisation itself, and the process of ordering of the city for optimisation, confer political and economic power and produce a hierarchy of interests. This commentary advocates that researchers connect systems research to questions of structure and power. To do this requires a critical approach to what is missing, what is implied by the choices about which data to collect and how to make them available, and an understanding of the ontologies that shape both the data sets and the urban spaces they describe.

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