The Effect of ‘Datafication’ on Knowledge Diffusion: A Topological Framework

2021 ◽  
Vol 21 ◽  
pp. 35-42
Author(s):  
Ayse Kok Arslan
Keyword(s):  
Data Processing ◽  
Data Entry ◽  
Knowledge Diffusion ◽  
General Sense ◽  
Data Infrastructure ◽  
Knowledge Codification ◽  
Data Practices ◽  
Data Points ◽  
Topological Framework

This paper begins by distinguishing between data infrastructure, data entry and data points as three distinct, but interrelated situations. Data practices are understood in the general sense of the word here, i.e., such as actions, actions, and consequences, of introducing data-generating technologies for knowledge codification.  This paper will investigate both the generics and specificities of data practices to explore the disentanglement of the liveness of data practices, i.e. how such practices are happening with regard to knowledge codification. Within this regard, this study seeks to account for the ‘fluid and heterogeneous ontology’ of such practices. In other words, the framework conceptualizes data processing as correlational, and aims to provide a technique to explore they disentanglement of these relationships.

Infrastructural approach to processing of spatial data in the problems of management of territorial development

2018 ◽  
Author(s):  
И.В. Бычков ◽  
Г.М. Ружников ◽  
В.В. Парамонов ◽  
А.С. Шумилов ◽  
Р.К. Фёдоров
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Data Exchange ◽  
Complex Analysis ◽  
Data Entry ◽  
Content Management ◽  
Volume Data ◽  
Territorial Development ◽  
Distributed Services ◽  
Spatial Data Processing

Рассмотрен инфраструктурный подход обработки пространственных данных для решения задач управления территориальным развитием, который основан на сервис-ориентированной парадигме, стандартах OGC, web-технологиях, WPS-сервисах и геопортале. The development of territories is a multi-dimensional and multi-aspect process, which can be characterized by large volumes of financial, natural resources, social, ecological and economic data. The data is highly localized and non-coordinated, which limits its complex analysis and usage. One of the methods of large volume data processing is information-analytical environments. The architecture and implementation of the information-analytical environment of the territorial development in the form of Geoportal is presented. Geoportal provides software instruments for spatial and thematic data exchange for its users, as well as OGC-based distributed services that deal with the data processing. Implementation of the processing and storing of the data in the form of services located on distributed servers allows simplifying their updating and maintenance. In addition, it allows publishing and makes processing to be more open and controlled process. Geoportal consists of following modules: content management system Calipso (presentation of user interface, user management, data visualization), RDBMS PostgreSQL with spatial data processing extension, services of relational data entry and editing, subsystem of launching and execution of WPS-services, as well as services of spatial data processing, deployed at the local cloud environment. The presented article states the necessity of using the infrastructural approach when creating the information-analytical environment for the territory management, which is characterized by large volumes of spatial and thematical data that needs to be processed. The data is stored in various formats and applications of service-oriented paradigm, OGC standards, web-technologies, Geoportal and distributed WPS-services. The developed software system was tested on a number of tasks that arise during the territory development.

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category. Results: Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) of missing entries were among categorical data. Omissions in PBDC (n=1002) were twice as frequent as in EDC (n=497, p<0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.

scholarly journals Sistem Informasi Online Pada Direktorat Tahanan Dan Barang Bukti Polda Sumatera Barat Dengan Menggunakan Bahasa Pemrograman PHP MYSQL

2018 ◽  
Vol 1 (2) ◽  
pp. 132-139
Author(s):  
Ahmad Junaidi
Keyword(s):  
Data Processing ◽  
Data Entry ◽  
Processing System ◽  
Data Availability ◽  
Strategic Decision ◽  
Present Moment ◽  
West Sumatra ◽  
Access Rights ◽  
Diverse Data ◽  
And Control

Developments in science and technology in a rapid growth has pushed people to seek and implement ways or new methods of surveillance and control data processing system to run smoothly. Ability and speed in processing the data repeatedly and with a very large number have no doubt to generate the reports required in the process of strategic decision making. So that at the present moment has a lot of companies and government agencies want receipts of computer technology to assist in solving problems of their data processing. In the Directorate of Prisoners and Evidence (DITTAHTI) of West Sumatra Regional Police, the processing of prisoner data and evidence of goods has been done frequently, but has not obtained optimal results. This is due to the use of information technology is still very less and still implemented offline and manual by city and district police. Optimization of data processing is necessary for integrity, access rights and data availability can be maintained properly. Application System to be proposed later is PHP MYSQL. All data entry will be processed in a Database. Diverse data will be more easily and quickly processed in a well-structured system. Keywords : Information System, Directorate of Prisoner and Evidence, PHP, MYSQL

scholarly journals FAKE POPULARITY FOR REAL MONEY: COMMERCIAL ASTROTURFING AND DATA BUBBLE ON CHINESE DIGITAL PLATFORM

2021 ◽  
Author(s):  
Xiaofei Han ◽  
Jiaxi Hou
Keyword(s):  
Real Estate ◽  
Intrinsic Value ◽  
Real Estate Market ◽  
Mixed Data ◽  
Channel Networks ◽  
Data Infrastructure ◽  
Digital Platforms ◽  
Data Practices ◽  
Wide Range ◽  
User Groups

This on-going research delineates the constructing of an interlocking ecosystem around popularity magnification on popular Chinese digital platforms, which we refer as “data bubble”. Similar to the bubble in a stock market or in real estate market in different economies where the price of assets substantially exceeds its intrinsic value, we propose “data bubble” as a neologism to describe the phenomenon and ecosystem of manipulating data to aim for an inflated popularity on Chinese digital platforms, which ultimately pitch to higher commercial and financial values. We argue that data bubble is laced with platform company’s commercial and financial imperatives, logics of datafication and popularity of platform as data infrastructure, and active participation from different user groups and complementors, and a deeply embedded mentality of “traffic is king”. It is achieved through mixed data practices including data optimization, commercial astroturfing, and counterfeit data manufacturing behind which a wide range of actors and entities are involved. They range from platforms, individual end users (fans in particular), influencers, multi-channel networks (MCNs) and incubators, celebrities and their agencies, click farms, and advertisers—all of them have achieved their own ends and thus actively participated in fabricating data bubble in one way or another. The practices of data manipulating and optimization by different participants in constructing data bubble, as a result, have driven the data metrics on Chinese platforms far over—and no longer representative of—the actual popularity.

scholarly journals Playing with data and its consequences

First Monday ◽  
2019 ◽  
Author(s):  
Miren Gutiérrez ◽  
Stefania Milan
Keyword(s):  
Big Data ◽  
Paradigm Shift ◽  
Daily Basis ◽  
Social Contexts ◽  
Communicative Practices ◽  
Data Infrastructure ◽  
Critical Data ◽  
Data Practices ◽  
The Social ◽  
The World

The fundamental paradigm shift brought about by datafication alters how people participate as citizens on a daily basis. “Big data” has come to constitute a new terrain of engagement, which brings organized collective action, communicative practices and data infrastructure into a fruitful dialogue. While scholarship is progressively acknowledging the emergence of bottom-up data practices, to date no research has explored the influence of these practices on the activists themselves. Leveraging the disciplines of critical data and social movement studies, this paper explores “proactive data activism”, using, producing and/or appropriating data for social change, and examines its biographical, political, tactical and epistemological consequences. Approaching engagement with data as practice, this study focuses on the social contexts in which data are produced, consumed and circulated, and analyzes how tactics, skills and emotions of individuals evolve in interplay with data. Through content and co-occurrence analysis of semi-structured practitioner interviews (N=20), the article shows how the employment of data and data infrastructure in activism fundamentally transforms the way activists go about changing the world.

The Information System of Experimental Pathology of the German Cancer Research Center

1980 ◽  
Vol 19 (01) ◽  
pp. 16-22
Author(s):  
D. Komitowski ◽  
C. O. Köhler ◽  
D. Naumann ◽  
B. Lance
Keyword(s):  
Cancer Research ◽  
Information System ◽  
Data Processing ◽  
Animal Experiments ◽  
Data Entry ◽  
Current Data ◽  
Research Center ◽  
German Cancer Research ◽  
Flexible System ◽  
Human Pathology

The information system of experimental oncopathology of the German Cancer Research Center is a computerized data processing program for studying the etiology, pathogenesis and therapy of experimental cancer. This program is adapted to correlate stored data with those from the thesaurus of human pathology. The system is developed and administered by the histodiagnostic facility which serves to collect and register standardized, centralized and current data from all sources. These are: individual investigators, animal laboratory, and central histodiagnostic facility. To record uniform data, a standardized protocol is introduced which entails data sets for information about animals, substances under study, experimental design, and necropsy and histological changes. The data entry takes place semi-automatically by using different codes grouped into three files: for substances, for animals, and for pathological changes. The code for pathological findings is based on SNOP. For data processing the system ALIS is employed which permits input, check and update; reorganisation and confirmation; evaluation.The information system is adapted to the organization and research programs of the German Cancer Research Center. It is a flexible system applicable for different conditions in registering and processing diverse information about animal experiments.

INFORMATION SYSTEM OF ADMINISTRATION ON EKASAKTI UNIVERSITY OF GRADUATE PROGRAM

2016 ◽  
Vol 1 (2) ◽  
pp. 099
Author(s):  
M. Ilham A. Siregar
Keyword(s):  
Information System ◽  
Data Processing ◽  
Administrative Data ◽  
Graduate Program ◽  
Data Entry ◽  
Processing System ◽  
Data Processing System ◽  
Report Generation ◽  
System Administration ◽  
Administrative Data Processing

Data processing system administration at Ekasakti Padang University of Graduate Program still use manual way in data processing, therefore, the author conducted research at University Graduate Program Ekasakti Padang by collecting information and data to be processed to be made an information system administrative data processing student consists of data entry, data processing and report generation. This information system can generate; Student Data Reports, Payment Data Report, and report graduation data. The system is designed with the needs in Padang Ekasakti University Graduate Program in order to be more effective and efficient, and is expected to administrative data processing can be optimized.

Study and Practice of Time-Temperature-Transformation (TTT) Curve Digitization

2013 ◽  
Vol 706-708 ◽  
pp. 1837-1840 ◽  
Author(s):  
Xiao Hong Fan ◽  
Bin Xu ◽  
Jing Li ◽  
Yong Xu ◽  
Shi Lei ◽  
...  
Keyword(s):  
Thermal Treatment ◽  
Data Processing ◽  
Data Base ◽  
Convenient Method ◽  
Industrial Applications ◽  
Data Series ◽  
Data Points ◽  
Temperature Transformation ◽  
The Relationship ◽  
Automatic Level

Several methods were introduced for practicing and studying the method of TTT curve digitization by using Plot Digitizer (version 1.9) and OriginPro (version 8.0724). After digitizing, original TTT images, which can not be inquired in computer procedures, were transformed into the data series and digital patterns, providing a convenient way for inquiring the data points from TTT curves. It offers a handy tool for studying the relationship among the time, temperature and transformation. Furthermore, it provides a convenient method for thermal treatment teaching through courseware. It is believe that, after a systemic data processing, we can obtain a digitizing TTT data base, which can be inquired automatically by PC procedures, promoting the automatic level of the thermal treatment for industrial applications.

scholarly journals Processing Point Clouds Using Simulated Physical Processes as Replacements of Conventional Mathematically Based Procedures: A Theoretical Virtual Measurement for Stem Volume

2021 ◽  
Vol 13 (22) ◽  
pp. 4627
Author(s):  
Zhichao Wang ◽  
Yan-Jun Shen ◽  
Xiaoyuan Zhang ◽  
Yao Zhao ◽  
Christiane Schmullius
Keyword(s):  
Data Processing ◽  
Virtual Water ◽  
Point Clouds ◽  
Stem Volume ◽  
Measurement Instruments ◽  
Physical Processes ◽  
Water Displacement ◽  
Solid Objects ◽  
Mathematical Expressions ◽  
Data Points

Conventional mathematically based procedures in forest data processing have some problems, such as deviations between the natural tree and the tree described using mathematical expressions, and manual selection of equations and parameters. These problems are rooted at the algorithmic level. Our solution for these problems was to process raw data using simulated physical processes as replacements of conventional mathematically based procedures. In this mechanism, we treated the data points as solid objects and formed virtual trees. Afterward, the tree parameters were obtained by the external physical detection, i.e., computational virtual measurement (CVM). CVM simulated the physical behavior of measurement instruments in reality to measure virtual trees. Namely, the CVM process was a pure (simulated) physical process. In order to verify our assumption of CVM, we developed the virtual water displacement (VWD) application. VWD could extract stem volume from an artificial stem (consisted of 2000 points) by simulating the physical scenario of a water displacement method. Compared to conventional mathematically based methods, VWD removed the need to predefine the shape of the stem and minimized human interference. That was because VWD utilized the natural contours of the stem through the interaction between the point cloud and the virtual water molecules. The results showed that the stem volume measured using VWD was 29,636 cm3 (overestimation at 6.0%), where the true volume was 27,946 cm3. The overall feasibility of CVM was proven by the successful development of VWD. Meanwhile, technical experiences, current limitations, and potential solutions were discussed. We considered CVM as a generic method that focuses the objectivity at the algorithmic level, which will become a noteworthy development direction in the field of forest data processing in the future.

scholarly journals Analysis of large volume data processing using clustering algorithms

2018 ◽  
Vol 7 (4.5) ◽  
pp. 685
Author(s):  
Sarada. B ◽  
Vinayaka Murthy. M ◽  
Udaya Rani. V
Keyword(s):  
Data Processing ◽  
Large Volume ◽  
Clustering Algorithms ◽  
Volume Data ◽  
Spatial Complexity ◽  
New Approach ◽  
Low Dimensions ◽  
Internet Age ◽  
Data Points ◽  
Frame Work

The study of large dataset with velocity, variety and volume which is also known as Big data. When the dataset has limited number of clusters, low dimensions and small number of data points the existing traditional clustering algorithms can be used.. As we know this is the internet age, the data is growing very fast and existing clustering algorithms are not giving the acceptable results in terms of time complexity and spatial complexity. So there is a need to develop a new approach of applying clustering of large volume of data processing with low time and spatial complexity through MapReduce and Hadoop frame work applying to different clustering algorithms, k-means, Canopy clustering and proposed algorithm .The analysis shows that the large volume of data processing will take low time and spatial complexity when compared to small volume of data.   

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