User:Psu431editor/sandbox
Addition of section to "Internet addiction disorder":
Japan
In Japan, internet addiction disorder has manifested into the citizens primarily affecting the youth and adolescent population. In the male youth the internet addiction shows a trend in increased time in gaming on their devices while the female youth shows trends in social media use. The smartphone and internet addiction in Japan has become detrimental to the society by affecting social interactions between people and their communication. They become used to interacting over the internet and their phones that it deteriorates some of their social skills over time. [1]
Many cases of social withdrawal have been occurring in Japan since the late 1990's which inclines people to stay indoors most of the time. The term used for this is hikkomori, and it primarily affects the youth of Japan in that they are less inclined to leave their residences. Internet addiction can contribute to this effect because of how it diminishes social interactions and gives young people another reason to stay at home for longer. Many of the hikkomori people in Japan are reported to have friends in their online games, so they will experience a different kind of social interaction which happens in a virtual space. [2]
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Copy from article "information ethics":
- Bioinformation ethics
- Business information ethics
- Computer ethics
- Cyberethics
- Information ecology
- Library ethics
- Media ethics
1- First two links and Library ethics from the "Branches" section do not work/exist, so I replaced them with working articles as shown below:
2- From "Security and Privacy" section: Added citation to statement where it was missing one.
This concept is a key proponent of ethical consumer marketing and is the basis of United States Privacy Laws, the European Union's privacy directive from 1995, and the Clinton Administration's June 1995 guidelines for personal information use by all National Information Infrastructure participants. An individual being allowed to remove their name from a mailing list is considered a best information collecting practice.[3]
In a few Equifax surveys conducted in the years 1994-1996, it was found that a substantial amount of the American public was concerned about business practices using private consumer information, and that is causes more harm than good.[4]
- ^ Tateno, Masaru; Teo, Alan R.; Ukai, Wataru; Kanazawa, Junichiro; Katsuki, Ryoko; Kubo, Hiroaki; Kato, Takahiro A. (2019-07-10). "Internet Addiction, Smartphone Addiction, and Hikikomori Trait in Japanese Young Adult: Social Isolation and Social Network". Frontiers in Psychiatry. 10. doi:10.3389/fpsyt.2019.00455. ISSN 1664-0640. PMC 6635695. PMID 31354537.
{{cite journal}}
: CS1 maint: unflagged free DOI (link) - ^ Kato, Takahiro A.; Kanba, Shigenobu; Teo, Alan R. (2019). "Hikikomori : Multidimensional understanding, assessment, and future international perspectives". Psychiatry and Clinical Neurosciences. 73 (8): 427–440. doi:10.1111/pcn.12895. ISSN 1440-1819.
- ^ Kalil, Thomas. “Public Policy and the National Information Infrastructure.” Business Economics, vol. 30, no. 4, 1995, pp. 15–20. JSTOR, www.jstor.org/stable/23487729.
- ^ "Equifax :: Consumers :: Privacy Survey". www.frogfire.com. Retrieved 2020-11-23.
Changes to "Data Integration":
1- Expand on lead section to clarify type of data used for integration:(edits are underlined)
Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. Data integration encourages collaboration between internal as well as external users. The data being integrated must be received from a heterogeneous database system and transformed to a single coherent data store that provides synchronous data across a network of files for clients.[1] A common use of data integration is in data mining when analyzing and extracting information from existing databases that can be useful for Business information.[2]
2- Adding citations to "History" section:
As of 2009 the trend in data integration favored the loose coupling of data[3]
Such mappings can be specified in two ways: as a mapping from entities in the mediated schema to entities in the original sources (the "Global-as-View"[4] (GAV) approach), or as a mapping from entities in the original sources to the mediated schema (the "Local-as-View"[5] (LAV) approach).
3- New section for "Business Analytics":
Data integration plays a big role in business regarding data collection used for studying the market. Converting the raw data retrieved from consumers into coherent data is something businesses try to do when considering what steps they should take next.[6] Organizations are more frequently using data mining for collecting information and patterns from their databases, and this process helps them develop new business strategies to increase business performance and perform economic analyses more efficiently. Compiling the large amount of data they collect to be stored in their system is a form of data integration adapted for Business intelligence to improve their chances of success.[7]
- ^ mikben. "Data Coherency - Win32 apps". docs.microsoft.com. Retrieved 2020-11-23.
- ^ Chung, P.; Chung, S. H. (2013-05). "On data integration and data mining for developing business intelligence". 2013 IEEE Long Island Systems, Applications and Technology Conference (LISAT): 1–6. doi:10.1109/LISAT.2013.6578235.
- ^ Pautasso, Cesare; Wilde, Erik (2009-04-20). "Why is the web loosely coupled? a multi-faceted metric for service design". Proceedings of the 18th international conference on World wide web. WWW '09. Madrid, Spain: Association for Computing Machinery: 911–920. doi:10.1145/1526709.1526832. ISBN 978-1-60558-487-4.
- ^ "What is GAV (Global as View)?". GeeksforGeeks. 2020-04-18. Retrieved 2020-11-23.
- ^ "Local-as-View", Wikipedia (in German), 2020-07-24
- ^ "Data Mining in Business Analytics". Western Governors University. Retrieved 2020-11-23.
- ^ Surani, Ibrahim (2020-03-30). "Data Integration for Business Intelligence: Best Practices". DATAVERSITY. Retrieved 2020-11-23.