scholarly journals Archiving Historical Data : Three Criticisms for the Reliability of Digital Sources

2020 ◽  
Vol 8 (2) ◽  
pp. 242-250
Author(s):  
Raistiwar Pratama ◽  
Keyword(s):  
Big Data ◽  
Historical Data ◽  
Historical Thinking ◽  
Source Criticism ◽  
Online Access ◽  
Data Medium

For data, medium is the information itself, as stated by Marshal McLuhan in 1960’s. What Harold K. Innis would have to assume as “the bias of media”. In the midst of “total history” reawakening and the use of “big data” and “one data,” issues on source criticism towards data are deliberately ignored as soon as online access is open for public. Similar to Heather Sutherland’s “historicizing history” and Samuel Wineberg’s “historical thinking”, principles on records processes are among others efforts to protect historical data. This writing describes source criticisms on digital archival records.

Big Data Analytics in Aquaculture Using Hive and Hadoop Platform

2018 ◽  
pp. 29-35
Author(s):  
P. Venkateswara Rao ◽  
A. Ramamohan Reddy ◽  
V. Sucharita
Keyword(s):  
Big Data ◽  
Data Management ◽  
Single Machine ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Huge Amount ◽  
Hadoop Platform ◽  
Different Sources

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.

Volunteered Surveillance

2019 ◽  
pp. 2053-2071
Author(s):  
Subhi Can Sarıgöllü ◽  
Erdem Aksakal ◽  
Mine Galip Koca ◽  
Ece Akten ◽  
Yonca Aslanbay
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Historical Data ◽  
Front End ◽  
Commercial World ◽  
Asymmetric Power ◽  
Ownership Control

As the front end of the digitized commercial world, corporations, marketers, and advertisers are under the spotlight for taking advantage of some part of the big data provided by consumers via their digital presence and digital advertising. Now, collectors and users of that data have escalated the level of their asymmetric power with scope and depth of the instant and historical data on consumers. Since consumers have lost the ownership (control) over their own data, their reaction ranges from complete opposition to voluntary submission. This chapter investigates psychological and societal reasons for this variety in consumer behavior and proposes that a contractual solution could promote a beneficial end to all parties through transparency and mutual power.

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

Big data analytics for proactive industrial decision support

atp magazin ◽  
2016 ◽  
Vol 58 (09) ◽  
pp. 62 ◽  
Author(s):  
Martin Atzmueller ◽  
Benjamin Klöpper ◽  
Hassan Al Mawla ◽  
Benjamin Jäschke ◽  
Martin Hollender ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Historical Data ◽  
Big Data Analytics ◽  
Lessons Learned ◽  
Support Functions ◽  
Big Data Applications ◽  
Big Data Technologies ◽  
Process Plants ◽  
Plant Data

Big data technologies offer new opportunities for analyzing historical data generated by process plants. The development of new types of operator support systems (OSS) which help the plant operators during operations and in dealing with critical situations is one of these possibilities. The project FEE has the objective to develop such support functions based on big data analytics of historical plant data. In this contribution, we share our first insights and lessons learned in the development of big data applications and outline the approaches and tools that we developed in the course of the project.

African Testimony Reported in European Travel Literature: What Did Paul Soleillet and Camille Piétri Hear and Why Does No One Recount It Now?

1991 ◽  
Vol 18 ◽  
pp. 143-158
Author(s):  
John H. Hanson
Keyword(s):  
Travel Literature ◽  
Historical Data ◽  
Textual Criticism ◽  
Oral Traditions ◽  
Oral Narratives ◽  
Source Criticism ◽  
The Past ◽  
Principal Value ◽  
Special Category ◽  
Written Sources

European visitors to Africa frequently report versions of oral narratives in their travel accounts from the precolonial era. Beatrix Heintze cautions against the uncritical use of these narratives, arguing that they are a “special category of source to which one must apply not only all the criteria for the analysis of oral traditions, but also the sort of source criticism specific to written sources.” Her call for textual criticism is appropriate, but her recommendations regarding the oral aspects of the information raise several issues: what criteria should be adopted for the analysis of oral narratives and what insights into the past do these materials provide? Heintze assumes that oral narratives present “concrete historical data” with “literal” meanings which become “more abstract over the course of time.” She sees the principal value of European-mediated accounts as providing access to the factual statements and initial metaphors from which emerged the more abstract historical clichés expressed by informants in contemporary Africa.

scholarly journals Data Management at the French National Audiovisual Institute (Ina)

Atlanti ◽  
2016 ◽  
Vol 26 (1) ◽  
pp. 101-108
Author(s):  
Eleonore Alquier
Keyword(s):  
Data Mining ◽  
Big Data ◽  
General Public ◽  
Open Data ◽  
Digital Technologies ◽  
Data Access ◽  
Technical Data ◽  
Production Processes ◽  
Data Policy ◽  
Online Access

The French National Audiovisual Institute has been responsible since 1974 for the preservation of the audiovisual heritage produced by national broadcasting corporation (or “Office de radio et television française”: ORTF, for French radio and television corporation). The massive digitalization of these collections in the 1990s, the native digital capture of 120 channels since 2001, the opening of a “general public” website in 2006, are some of the steps taken by the Institute to progressively take into account the digital technologies to benefit the audiovisual preservation. This proposal of presentation would provide an update on the evolution of our processing, concerning most specifically a multi-year project which aims, linked to a new big data policy, to harmonize descriptive metadata according to common thesaurus and to streamline production processes as well as to promote new uses of these contents within the Institute (partial automation of documentary processing by automatic detecting of quoted or represented entities (faces, names, …), automatic articulation of documentary and legal metadata, …), but also outside of the Institute (online access to open data, access to media by technical data mining, …).

Volunteered Surveillance

2018 ◽  
pp. 133-150
Author(s):  
Subhi Can Sarıgöllü ◽  
Erdem Aksakal ◽  
Mine Galip Koca ◽  
Ece Akten ◽  
Yonca Aslanbay
Keyword(s):  
Big Data ◽  
Consumer Behavior ◽  
Historical Data ◽  
Front End ◽  
Commercial World ◽  
Asymmetric Power ◽  
Ownership Control

As the front end of the digitized commercial world, corporations, marketers, and advertisers are under the spotlight for taking advantage of some part of the big data provided by consumers via their digital presence and digital advertising. Now, collectors and users of that data have escalated the level of their asymmetric power with scope and depth of the instant and historical data on consumers. Since consumers have lost the ownership (control) over their own data, their reaction ranges from complete opposition to voluntary submission. This chapter investigates psychological and societal reasons for this variety in consumer behavior and proposes that a contractual solution could promote a beneficial end to all parties through transparency and mutual power.

scholarly journals Coping with big: Does big data lead to ‘bigger’ innovation?

2016 ◽  
Vol 4 (1) ◽  
pp. 1-3
Author(s):  
Marko Torkkeli ◽  
Anne-Laure Mention ◽  
João José Pinto Ferreira
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Historical Data ◽  
Critical Issue ◽  
Time Data ◽  
The Future ◽  
Long Time ◽  
Spring Issue ◽  
First Time ◽  
Minority Report

This Spring Issue will discuss about big data and multiple aspects of its usability and applicability. Many of us have seen blockbuster movies Back to the future (premiere in 1985), The Terminator (1984) or Minority report (2002). The unifying element of the above mentioned movies is that manuscripts are introducing a superior competitive advantage factor. The protagonists create an advantage by having either real-time data (sometimes from the future) or all relevant (big and historical) data with enormous computing capacity over competitors. A bit after first two of those movies premiered, NASA scientists Cox and Ellsworth (1997) published an article where term ‘big data’ appeared first time (Press, 2014). Intelligence needs to be topped up in a way to create advantage. Data has been there for a long time, in all forms and sizes. It is applied in almost single every business sector and it is getting faster in sense of usability. The data storage capacity has been exponentially increasing over time, but the usability of this wealth of data remains a critical issue.(...)

scholarly journals Machine Learning Based Method for Prediction of Heart Disease in Big Data Environment

2020 ◽  
Vol 9 (4) ◽  
pp. 1917-1921
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Historical Data ◽  
Heart Diseases ◽  
Medical Professional ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Huge Data ◽  
Learning Techniques ◽  
Non Linear

Prediction of diseases is one of the challenging tasks in healthcare domain. Conventionally the heart diseases were diagnosed by experienced medical professional and cardiologist with the help of medical and clinical tests. With conventional method even experienced medical professional struggled to predict the disease with sufficient accuracy. In addition, manually analysing and extracting useful knowledge from the archived disease data becomes time consuming as well as infeasible. The advent of machine learning techniques enables the prediction of various diseases in healthcare domain. Machine learning algorithms are trained to learn from the existing historical data and prediction models are being created to predict the unknown raw data. For the past two decades, machine learning techniques are extensively employed for disease prediction. Despite the capability of machine algorithm on learning from huge historical data which is stored in data mart and data warehouses using traditional database technologies such as Oracle OnLine Analytical Processing (OLAP). The conventional database technologies suffer from the limitation that they cannot handle huge data or unstructured data or data that comes with speed. In this context, big data tools and technologies plays a major role in storing and facilitating the processing of huge data. In this paper, an approach is proposed for prediction of heart diseases using Support Vector Algorithm in Spark environment. Support Vector Machine algorithm is basically a binary classifier which classifies both linear and non-linear input data. It transforms the non-linear data into hyper plan with the help of different kernel functions. Spark is a distributed big data processing platform which has a unique feature of keeping and processing a huge data in memory. The proposed approach is tested with a benchmark dataset from UCI repository and results are discussed.

scholarly journals Integrating CyberGIS and Urban Sensing for Reproducible Streaming Analytics

2021 ◽  
pp. 663-681
Author(s):  
Shaowen Wang ◽  
Fangzheng Lyu ◽  
Shaohua Wang ◽  
Charles E. Catlett ◽  
Anand Padmanabhan ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Network Services ◽  
Location Aware ◽  
Scientific Innovation ◽  
Challenges And Opportunities ◽  
Research Questions ◽  
Urban Sensing ◽  
Online Access ◽  
New Research

AbstractIncreasingly pervasive location-aware sensors interconnected with rapidly advancing wireless network services are motivating the development of near-real-time urban analytics. This development has revealed both tremendous challenges and opportunities for scientific innovation and discovery. However, state-of-the-art urban discovery and innovation are not well equipped to resolve the challenges of such analytics, which in turn limits new research questions from being asked and answered. Specifically, commonly used urban analytics capabilities are typically designed to handle, process, and analyze static datasets that can be treated as map layers and are consequently ill-equipped in (a) resolving the volume and velocity of urban big data; (b) meeting the computing requirements for processing, analyzing, and visualizing these datasets; and (c) providing concurrent online access to such analytics. To tackle these challenges, we have developed a novel cyberGIS framework that includes computationally reproducible approaches to streaming urban analytics. This framework is based on CyberGIS-Jupyter, through integration of cyberGIS and real-time urban sensing, for achieving capabilities that have previously been unavailable toward helping cities solve challenging urban informatics problems.

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