New Methods of Urban Research in the Information Age—Based on the Combination of Big Data and Traditional Data

2022 ◽  
pp. 301-309
Author(s):  
Wenhao Wu
Keyword(s):  
Big Data ◽  
Information Age ◽  
Urban Research ◽  
New Methods

A study of big data evolution and research challenges

2018 ◽  
Vol 45 (3) ◽  
pp. 322-340 ◽  
Author(s):  
Deepak Gupta ◽  
Rinkle Rani
Keyword(s):  
Big Data ◽  
Data Stream ◽  
Information Age ◽  
Tidal Wave ◽  
Digital Data ◽  
Data Driven ◽  
Research Challenges ◽  
Wide Gap ◽  
Almost All ◽  
Data Evolution

The world is already into the information age. The huge growth of digital data has overwhelmed the traditional systems and approaches. Big data is touching almost all aspects of our life and the data-driven discovery approach is an emerging paradigm for computing. The ever-growing data provides a tidal wave of opportunities and challenges in terms of data capture, storage, manipulation, management, analysis, knowledge extraction, security, privacy and visualisation. Though the promise of big data seems to be genuine, still a wide gap exists between its potential and realisation. In last few years, there is a huge surge in research efforts in academia as well as industry to have a better understanding of big data. This article discusses the following: (1) big data evolution including a bibliometric study of academic and industry publications pertaining to big data during the period 2000–2017, (2) popular open-source big data stream processing frameworks and (3) prevalent research challenges which must be addressed to realise the true potential of big data.

scholarly journals The Personality Panorama: Conceptualizing Personality through Big Behavioural Data

2020 ◽  
Vol 34 (5) ◽  
pp. 599-612 ◽  
Author(s):  
Ryan L. Boyd ◽  
Paola Pasca ◽  
Kevin Lanning
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Historical Thinking ◽  
Self Report ◽  
Ethical Considerations ◽  
Personality Psychology ◽  
The Public ◽  
New Methods ◽  
Behavioural Assessment ◽  
The Face

Personality psychology has long been grounded in data typologies, particularly in the delineation of behavioural, life outcome, informant–report, and self–report sources of data from one another. Such data typologies are becoming obsolete in the face of new methods, technologies, and data philosophies. In this article, we discuss personality psychology's historical thinking about data, modern data theory's place in personality psychology, and several qualities of big data that urge a rethinking of personality itself. We call for a move away from self–report questionnaires and a reprioritization of the study of behaviour within personality science. With big data and behavioural assessment, we have the potential to witness the confluence of situated, seamlessly interacting psychological processes, forming an inclusive, dynamic, multiangle view of personality. However, big behavioural data come hand in hand with important ethical considerations, and our emerging ability to create a ‘personality panopticon’ requires careful and thoughtful navigation. For our research to improve and thrive in partnership with new technologies, we must not only wield our new tools thoughtfully, but humanely. Through discourse and collaboration with other disciplines and the general public, we can foster mutual growth and ensure that humanity's burgeoning technological capabilities serve, rather than control, the public interest. © 2020 European Association of Personality Psychology

Data “big” and “small” – Examples from the Australian lexical database

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Claire Bowern
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Qualitative Data ◽  
Language Change ◽  
First Century ◽  
Lexical Database ◽  
Traditional Methods ◽  
New Methods ◽  
Statistical Approaches ◽  
Insight Into

AbstractThe twenty-first Century has been billed the era of “big data”, and linguists are participating in this trend. We are seeing an increased reliance on statistical and quantitative arguments in most fields of linguistics, including the oldest parts of the field, such as the study of language change. The increased use of statistical methods changes the types of questions we can ask of our data, as well as how we evaluate the answers. But this all has the prerequisite of certain types of data, coded in certain ways. We cannot make powerful statistical arguments from the qualitative data that historical linguists are used to working with. In this paper I survey a few types of work based on a lexical database of Pama-Nyungan languages, the largest family in Aboriginal Australia. I highlight the flexibility with which large-scale databases can be deployed, especially when combined with traditional methods. “Big” data may require new methods, but the combination of statistical approaches and traditional methods is necessary for us to gain new insight into old problems.

scholarly journals Big Data analysis: Intelligent Transportation Development Engine

2014 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Qin Xiao
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Information Age ◽  
Large Data ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Transportation Demand ◽  
Advantages And Disadvantages ◽  
Transportation Development

<p>With the development of the times, people have unwittingly entered the information age. The information age has become a large amount of data bursting characteristics of the new era. In this feature people still seek to improve the production and quality of life. For the development of intelligent transportation needs of people's lives and make the real world, but in the construction of intelligent transportation among a large number of information data also adds to its change and difficulty, how to build an intelligent era of big data, security, low-cost, efficient and convenient of intelligent transportation systems become today people study. From the era of big data to intelligent traffic changes brought advantages and disadvantages, the era of big data to bring intelligent traffic problems and challenges, as well as the integration platform for massive data intelligent transportation intelligent transportation demand and large data structures has done a simple elaborate, it can provide some suggestions for areas of research that scientists.</p>

scholarly journals Ethical Arguments and Prospects of Utilizing Big Data in Information Age

2016 ◽  
Vol 1 (108) ◽  
pp. 227-248
Author(s):  
송선영 ◽  
Kim Hang-In
Keyword(s):  
Big Data ◽  
Information Age

scholarly journals DETECTION OF BEHAVIOR PATTERNS OF INTEREST USING BIG DATA WHICH HAVE SPATIAL AND TEMPORAL ATTRIBUTES

2017 ◽  
Vol IV-4/W2 ◽  
pp. 31-35 ◽  
Author(s):  
R. W. La Valley ◽  
A. Usher ◽  
A. Cook
Keyword(s):  
Big Data ◽  
Analytical Techniques ◽  
Multidimensional Data ◽  
Behavior Patterns ◽  
One Dimensional ◽  
New Methods ◽  
Temporal Aspects ◽  
String Representation ◽  
Computationally Expensive ◽  
Vector Approach

New innovative analytical techniques are emerging to extract patterns in Big Data which have temporal and geospatial attributes. These techniques are required to find patterns of interest in challenging circumstances when geospatial datasets have millions or billions of records and imprecision exists around the exact latitude and longitude of the data. Furthermore, the usual temporal vector approach of years, months, days, hours, minutes and seconds often are computationally expensive and in many cases do not allow the user control of precision necessary to find patterns of interest.<br><br> Geohashing is a single variable ASCII string representation of two-dimensional geometric coordinates. Time hashing is a similar ASCII representation which combines the temporal aspects of date and time of the data into a one dimensional set of data attributes. Both methods utilize Z-order curves which map multidimensional data into single dimensions while preserving locality of the data records. This paper explores the use of a combination of both geohashing and time hashing that is known as “geo-temporal” hashing or “space-time” boxes. This technique provides a foundation for reducing the data into bins that can yield new methods for pattern discovery and detection in Big Data.

Thick Big Data

2020 ◽  
Author(s):  
Dariusz Jemielniak
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Access ◽  
Cultural Analysis ◽  
Additional Advantage ◽  
Corporate World ◽  
New Methods ◽  
Novel Approach ◽  
The Social ◽  
History Of

The social sciences are becoming datafied. The questions that have been considered the domain of sociologists, now are answered by data scientists, operating on large datasets, and breaking with the methodological tradition for better or worse. The traditional social sciences, such as sociology or anthropology, are thus under the double threat of becoming marginalized or even irrelevant; both because of the new methods of research, which require more computational skills, and because of the increasing competition from the corporate world, which gains an additional advantage based on data access. However, sociologists and anthropologists still have some important assets, too. Unlike data scientists, they have a long history of doing qualitative research. The more quantified datasets we have, the more difficult it is to interpret them without adding layers of qualitative interpretation. Big Data needs Thick Data. This book presents the available arsenal of new tools for studying the society quantitatively, but also show the new methods of analysis from the qualitative side and encourages their combination. In shows that Big Data can and should be supplemented and interpreted through thick data, as well as cultural analysis, in a novel approach of Thick Big Data.The book is critically important for students and researchers in the social sciences to understand the possibilities of digital analysis, both in the quantitative and qualitative area, and successfully build mixed-methods approaches.

scholarly journals From big data mining to technical sport reports: the case of inertial measurement units

2019 ◽  
Vol 5 (1) ◽  
pp. e000565 ◽  
Author(s):  
Daniel Rojas-Valverde ◽  
Carlos D Gómez-Carmona ◽  
Randall Gutiérrez-Vargas ◽  
Jose Pino-Ortega
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Inertial Measurement Units ◽  
New Approach ◽  
Inertial Measurement ◽  
Big Data Mining ◽  
New Methods ◽  
Approach Method ◽  
Measurement Units ◽  
Individual Sports

The inertial measurement units (IMU) are instruments used to quantify the external load of athletes; they are increasingly common in assessing team and individual sports. This type of instruments has several sensors, such as accelerometers, gyroscopes and magnetometers; this allows access to a large amount of information and analysis possibilities. Due to the complexity of synthesising this data, it is necessary to create a flow for collecting, analysing and presenting the collected data in a simple way and present it as quickly as possible to the technical staff. This report aims to present new methods of reduction of the data and propose a new approach method for the analysis of the IMU’s outcomes.

Role of Big Data in Continuous Improvement Environments

2019 ◽  
Vol 9 (1) ◽  
pp. 53-72 ◽  
Author(s):  
Brian J. Galli
Keyword(s):  
Big Data ◽  
Continuous Improvement ◽  
Measurement System ◽  
Business Processes ◽  
Information Age ◽  
Performance Measurement System ◽  
Organizational Changes ◽  
Business Associations ◽  
Data Points

Innovation technologies are used for consistent and continuous improvement, as well as for examining past executions in business. Furthermore, obtaining numerous bits of knowledge about a business can help to influence planning and future choices. A way to create connections between various data points is through big data. Currently, business processes face many challenges because of technological headway and information age. Since big data has grown and become so popular, it is possible to apply it to unique and conventional business associations. Additionally, if big data is used to meet the business's needs, then it can yield organizational changes in infrastructure and real-world improvement. Through big data, analysts can reveal continuous improvement methods and a performance measurement system in data administration, as well as management, transactions, and convey central leadership.

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