Bibliography Data Science - Big Data DevOps Software Engineering

B06XPJML5D ISBN-13: 978-1449373320

See: Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, 1st Edition, Publisher ‏ : ‎ O’Reilly Media; 1st edition (April 18, 2017)

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B089N35YXY ISBN-13: 978-0135404676

See: Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud (POD File) 1st Edition

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B084FLBDV7 ISBN-13: 978-1718500686

See: Dive Into Algorithms: A Pythonic Adventure for the Intrepid Beginner

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B0852P29NJ ISBN-13: ‎ 978-1718500624

See: Real-World Python: A Hacker’s Guide to Solving Problems with Code, by

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See: SQLAlchemy: Database Access Using Python (Developer’s Library) 1st Edition

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SQLAlchemy is an open-sourceSQL toolkit and object-relational mapper (ORM) for the Python programming language released under the MIT License.[5]

Original author(s)Michael Bayer[1][2]
Initial releaseFebruary 14, 2006; 15 years ago[3]
Stable release1.4.15 / May 11, 2021; 2 months ago[4]
Written inPython
Operating systemCross-platform
TypeObject-relational mapping
LicenseMIT License[5] 


SQLAlchemy’s philosophy is that relational databases behave less like object collections as the scale gets larger and performance starts being a concern, while object collections behave less like tables and rows as more abstraction is designed into them. For this reason it has adopted the data mapper pattern (similar to Hibernate for Java) rather than the active record pattern used by a number of other object-relational mappers.[6] However, optional plugins allow users to develop using declarative syntax.[7]


SQLAlchemy was first released in February 2006[8][3] and has quickly become one of the most widely used object-relational mapping tools in the Python community, alongside Django‘s ORM.


This section possibly contains original research. Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed. (November 2019) (Learn how and when to remove this template message)

The following example represents an n-to-1 relationship between movies and their directors. It is shown how user-defined Python classes create corresponding database tables, how instances with relationships are created from either side of the relationship, and finally how the data can be queried—illustrating automatically generated SQL queries for both lazy and eager loading.

Schema definition

Creating two Python classes and according database tables in the DBMS:

from sqlalchemy import *
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relation, sessionmaker

Base = declarative_base()

class Movie(Base):
    __tablename__ = "movies"

    id = Column(Integer, primary_key=True)
    title = Column(String(255), nullable=False)
    year = Column(Integer)
    directed_by = Column(Integer, ForeignKey(""))

    director = relation("Director", backref="movies", lazy=False)

    def __init__(self, title=None, year=None):
        self.title = title
        self.year = year

    def __repr__(self):
        return "Movie(%r, %r, %r)" % (self.title, self.year, self.director)

class Director(Base):
    __tablename__ = "directors"

    id = Column(Integer, primary_key=True)
    name = Column(String(50), nullable=False, unique=True)

    def __init__(self, name=None): = name

    def __repr__(self):
        return "Director(%r)" % (

engine = create_engine("dbms://user:pwd@host/dbname")

Data insertion

One can insert a director-movie relationship via either entity:

Session = sessionmaker(bind=engine)
session = Session()

m1 = Movie("Robocop", 1987)
m1.director = Director("Paul Verhoeven")

d2 = Director("George Lucas")
d2.movies = [Movie("Star Wars", 1977), Movie("THX 1138", 1971)]



alldata = session.query(Movie).all()
for somedata in alldata:

SQLAlchemy issues the following query to the DBMS (omitting aliases):

SELECT, movies.title, movies.year, movies.directed_by,,
FROM movies LEFT OUTER JOIN directors ON = movies.directed_by

The output:

Movie('Robocop', 1987L, Director('Paul Verhoeven'))
Movie('Star Wars', 1977L, Director('George Lucas'))
Movie('THX 1138', 1971L, Director('George Lucas'))

Setting lazy=True (default) instead, SQLAlchemy would first issue a query to get the list of movies and only when needed (lazy) for each director a query to get the name of the according director:

SELECT, movies.title, movies.year, movies.directed_by
FROM movies

FROM directors
WHERE = %s

See also


  1. ^ Mike Bayer is the creator of SQLAlchemy and Mako Templates for Python.
  2. ^ Interview Mike Bayer SQLAlchemy #pydata #python
  3. a b “Download – SQLAlchemy”. SQLAlchemy. Retrieved 21 February 2015.
  4. ^ “Releases – sqlalchemy/sqlalchemy”. Retrieved 17 May 2021 – via GitHub.
  5. a b “zzzeek / sqlalchemy / source / LICENSE”. BitBucket. Retrieved 21 February 2015.
  6. ^ in The architecture of open source applications
  7. ^ Declarative
  8. ^


External links


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See: Machine Learning Pocket Reference: Working with Structured Data in Python 1st Edition

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See: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

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Spark: The Definitive Guide: Big Data Processing Made Simple – B079P71JHY

See: Spark: The Definitive Guide: Big Data Processing Made Simple – March 20, 2018

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Artificial Intelligence Cloud Data Science - Big Data History

Limited hangout

“A limited hangout or partial hangout is, according to former special assistant to the Deputy Director of the Central Intelligence Agency Victor Marchetti, “spy jargon for a favorite and frequently used gimmick of the clandestine professionals. When their veil of secrecy is shredded and they can no longer rely on a phony cover story to misinform the public, they resort to admitting—sometimes even volunteering—some of the truth while still managing to withhold the key and damaging facts in the case. The public, however, is usually so intrigued by the new information that it never thinks to pursue the matter further.”[1][2] (WP)

Modified limited hangout

“In a March 22, 1973, meeting between president Richard NixonJohn DeanJohn EhrlichmanJohn Mitchell, and H. R. Haldeman, Ehrlichman incorporated the term into a new and related one, “modified limited hangout“.[3][4]

The phrase was coined in the following exchange:[5]” (WP)

PRESIDENT: You think, you think we want to, want to go this route now? And the — let it hang out, so to speak?

DEAN: Well, it’s, it isn’t really that —
HALDEMAN: It’s a limited hang out.
DEAN: It’s a limited hang out.
EHRLICHMAN: It’s a modified limited hang out.

PRESIDENT: Well, it’s only the questions of the thing hanging out publicly or privately.

“Before this exchange, the discussion captures Nixon outlining to Dean the content of a report that Dean would create, laying out a misleading view of the role of the White House staff in events surrounding the Watergate burglary. In Ehrlichman’s words: “And the report says, ‘Nobody was involved,'”. The document would then be shared with the United States Senate Watergate Committee investigating the affair. The report would serve the administration’s goals by protecting the President, providing documentary support for his false statements should information come to light that contradicted his stated position. Further, the group discusses having information on the report leaked by those on the Committee sympathetic to the President, to put exculpatory information into the public sphere.[5]” (WP)

“The phrase has been cited as a summation of the strategy of mixing partial admissions with misinformation and resistance to further investigation, and is used in political commentary to accuse people or groups of following a Nixon-like strategy.[6]” (WP) However, this “strategy” has been used since time immemorial.

“Writing in The Washington PostMary McGrory described a statement by Pope John Paul II regarding sexual abuse by priests as a “modified, limited hangout”.[7] (WP)

See also


  1. ^ Victor Marchetti (August 14, 1978) The Spotlight
  2. ^ “720 F2d 631 Hunt v. Liberty Lobby Dc”. OpenJurist. 1983-11-28. Retrieved 2016-07-13.
  3. ^ Frost/Nixon: The Complete Interviews. David Frost, Richard Nixon. Paradine Television, 1977.
  4. ^ Safire, William (26 March 1989). “On Language; In Nine Little Words”New York Times. Retrieved 23 June 2013.
  5. a b “Transcript of a recording of a meeting among the president, John Dean, John Erlichman, H. R. Haldeman, and John Mitchell on March 22, 1973 from 1:57 to 3:43 p.m.” History and Politics Out Loud. Retrieved 2006-08-27.
  6. ^ Carrol, Jon (2002-05-01). “The Richard Nixon playbook”San Francisco Chronicle. Retrieved 2006-08-27.
  7. ^ McGrory, Mary (2002-04-25). “From Rome, A ‘Limited Hangout'”The Washington Post. Washington, D.C. p. A29. Retrieved 2010-04-30.


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Fair Credit Reporting Act – 1970 AD

Return to Timeline of the History of Computers


Fair Credit Reporting Act

Alan Westin (1929–2013)

“In March 1970, a (“limited hangout“) professor from Columbia University testified before the US Congress about shadowy American businesses that were maintaining secret databases on American citizens. These files, said Alan Westin, “may include ‘facts, statistics, inaccuracies and rumors’ . . . about virtually every phase of a person’s life: his marital troubles, jobs, school history, childhood, sex life, and political activities.”

The files were used by American banks, department stores, and other firms to determine who should be given credit to buy a house, a car, or even a furniture set. The databanks, Westin explained, were also used by companies evaluating job applicants and underwriting insurance. And they couldn’t be outlawed: without credit and the ability to pay for major purchases with installments, many people couldn’t otherwise afford such things.

Westin was well known to the US Congress: he had testified on multiple occasions before congressional committees investigating the credit-reporting industry, and he had published a book, Privacy and Freedom (1967), in which he argued that freedom in the information age required that individuals have control over how their data are used by governments and businesses. Westin defined privacy as “the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others.” And he coined the phrase data shadow to describe the trail of information that people leave behind in the modern world.

On October 26, 1970, Congress enacted the Fair Credit Reporting Act (FCRA), which gave Americans, for the first time, the right to see the consumer files that businesses used to decide who should get credit and insurance. The FCRA also gave consumers the right to force the credit bureaus to investigate a claim that the consumer felt was inaccurate, and the ability to insert a statement in the file, telling his or her side of the story.

The FCRA was one of the first laws in the world regulating what private businesses could do with data that they collect—the beginning of what is now called data protection, an idea that eventually spread worldwide.

Today there are privacy commissioners in almost every developed country. The passage of the European Union’s General Data Protection Regulation (GDPR) marked the most far-reaching privacy law on the planet.”

SEE ALSO Relational Database (1970)

Columbia professor Alan Westin was concerned about American businesses keeping secret databases on American citizens.

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AIoT – Artificial intelligence of Things

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The Artificial Intelligence of Things (AIoT) is the combination of Artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics[1] [2] [3]

See also


  1. ^ Ghosh, Iman (12 August 2020). “AIoT: When Artificial Intelligence Meets the Internet of Things”Visual Capitalist. Retrieved 22 September 2020.
  2. ^ Lin, Yu-Jin; Chuang, Chen-Wei; Yen, Chun-Yueh; Huang, Sheng-Hsin; Huang, Peng-Wei; Chen, Ju-Yi; Lee, Shuenn-Yuh (March 2019). “Artificial Intelligence of Things Wearable System for Cardiac Disease Detection”2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS): 67–70. doi:10.1109/AICAS.2019.8771630. Retrieved 22 September 2020.
  3. ^ Chu, William Cheng-Chung; Shih, Chihhsiong; Chou, Wen-Yi; Ahamed, Sheikh Iqbal; Hsiung, Pao-Ann (November 2019). “Artificial Intelligence of Things in Sports Science: Weight Training as an Example”Computer52 (11): 52–61. doi:10.1109/MC.2019.2933772ISSN 1558-0814. Retrieved 22 September 2020.


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Ubiquitous computing – Ubicomp

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Ubiquitous computing (or “ubicomp“) is a concept in software engineering and computer science where computing is made to appear anytime and everywhere. In contrast to desktop computingubiquitous computing can occur using any device, in any location, and in any format. A user interacts with the computer, which can exist in many different forms, including laptop computerstablets and terminals in everyday objects such as a refrigerator or a pair of glasses. The underlying technologies to support ubiquitous computing include Internet, advanced middlewareoperating systemmobile codesensorsmicroprocessors, new I/O and user interfacescomputer networks, mobile protocols, location and positioning, and new materials.

This paradigm is also described as pervasive computing,[1] ambient intelligence,[2] or “everyware”.[3] Each term emphasizes slightly different aspects. When primarily concerning the objects involved, it is also known as physical computing, the Internet of Things (IoT)haptic computing,[4] and “things that think”. Rather than propose a single definition for ubiquitous computing and for these related terms, a taxonomy of properties for ubiquitous computing has been proposed, from which different kinds or flavors of ubiquitous systems and applications can be described.[5]

Ubiquitous computing touches on distributed computingmobile computing, location computing, mobile networking, sensor networkshuman–computer interaction, context-aware smart home technologies, and artificial intelligence.

Core concepts

Ubiquitous computing is the concept of using small internet connected and inexpensive computers to help with everyday functions in an automated fashion. For example, a domestic ubiquitous computing environment might interconnect lighting and environmental controls with personal biometric monitors woven into clothing so that illumination and heating conditions in a room might be modulated, continuously and imperceptibly. Another common scenario posits refrigerators “aware” of their suitably tagged contents, able to both plan a variety of menus from the food actually on hand, and warn users of stale or spoiled food.[6]

Ubiquitous computing presents challenges across computer science: in systems design and engineering, in systems modelling, and in user interface design. Contemporary human-computer interaction models, whether command-line, menu-driven, or GUI-based, are inappropriate and inadequate to the ubiquitous case. This suggests that the “natural” interaction paradigm appropriate to a fully robust ubiquitous computing has yet to emerge – although there is also recognition in the field that in many ways we are already living in a ubicomp world (see also the main article on natural user interfaces). Contemporary devices that lend some support to this latter idea include mobile phonesdigital audio playersradio-frequency identification tags, GPS, and interactive whiteboards.

Mark Weiser proposed three basic forms for ubiquitous computing devices:[7]

  • Tabs: a wearable device that is approximately a centimeter in size
  • Pads: a hand-held device that is approximately a decimeter in size
  • Boards: an interactive larger display device that is approximately a meter in size

Ubiquitous computing devices proposed by Mark Weiser are all based around flat devices of different sizes with a visual display.[8] Expanding beyond those concepts there is a large array of other ubiquitous computing devices that could exist. Some of the additional forms that have been conceptualized are:[5]

  • Dust: miniaturized devices can be without visual output displays, e.g. micro electro-mechanical systems (MEMS), ranging from nanometres through micrometers to millimetres. See also Smart dust.
  • Skin: fabrics based upon light emitting and conductive polymers, organic computer devices, can be formed into more flexible non-planar display surfaces and products such as clothes and curtains, see OLED display. MEMS device can also be painted onto various surfaces so that a variety of physical world structures can act as networked surfaces of MEMS.
  • Clay: ensembles of MEMS can be formed into arbitrary three dimensional shapes as artefacts resembling many different kinds of physical object (see also tangible interface).

In Manuel Castells‘ book The Rise of the Network Society, Castells puts forth the concept that there is going to be a continuous evolution of computing devices. He states we will progress from stand-alone microcomputers and decentralized mainframes towards pervasive computing. Castells’ model of a pervasive computing system, uses the example of the Internet as the start of a pervasive computing system. The logical progression from that paradigm is a system where that networking logic becomes applicable in every realm of daily activity, in every location and every context. Castells envisages a system where billions of miniature, ubiquitous inter-communication devices will be spread worldwide, “like pigment in the wall paint”.

Ubiquitous computing may be seen to consist of many layers, each with their own roles, which together form a single system:

  • Layer 1: Task management layer
    • Monitors user task, context and index
    • Map user’s task to need for the services in the environment
    • To manage complex dependencies
  • Layer 2: Environment management layer
    • To monitor a resource and its capabilities
    • To map service need, user level states of specific capabilities
  • Layer 3: Environment layer
    • To monitor a relevant resource
    • To manage reliability of the resources


Mark Weiser coined the phrase “ubiquitous computing” around 1988, during his tenure as Chief Technologist of the Xerox Palo Alto Research Center (PARC). Both alone and with PARC Director and Chief Scientist John Seely Brown, Weiser wrote some of the earliest papers on the subject, largely defining it and sketching out its major concerns.[7][9][10]

Recognizing the effects of extending processing power

Recognizing that the extension of processing power into everyday scenarios would necessitate understandings of social, cultural and psychological phenomena beyond its proper ambit, Weiser was influenced by many fields outside computer science, including “philosophyphenomenologyanthropologypsychologyand sociology of science “. He was explicit about “the humanistic origins of the invisible ideal'”,[10] referencing as well the ironically dystopian Philip K. Dick novel Ubik.

Andy Hopper from Cambridge University UK proposed and demonstrated the concept of “Teleporting” – where applications follow the user wherever he/she moves.

Roy Want, while a researcher and student working under Andy Hopper at Cambridge University, worked on the “Active Badge System”, which is an advanced location computing system where personal mobility that is merged with computing.

Bill Schilit (now at Google) also did some earlier work in this topic, and participated in the early Mobile Computing workshop held in Santa Cruz in 1996.

Ken Sakamura of the University of TokyoJapan leads the Ubiquitous Networking Laboratory (UNL), Tokyo as well as the T-Engine Forum. The joint goal of Sakamura’s Ubiquitous Networking specification and the T-Engine forum, is to enable any everyday device to broadcast and receive information.[11][12]

MIT has also contributed significant research in this field, notably Things That Think consortium (directed by Hiroshi IshiiJoseph A. Paradiso and Rosalind Picard) at the Media Lab[13] and the CSAIL effort known as Project Oxygen.[14] Other major contributors include University of Washington‘s Ubicomp Lab (directed by Shwetak Patel), Dartmouth College‘s DartNets LabGeorgia Tech‘s College of ComputingCornell University‘s People Aware Computing LabNYU‘s Interactive Telecommunications ProgramUC Irvine‘s Department of Informatics, Microsoft ResearchIntel Research and Equator,[15] Ajou University UCRi & CUS.[16]


One of the earliest ubiquitous systems was artist Natalie Jeremijenko‘s “Live Wire”, also known as “Dangling String”, installed at Xerox PARC during Mark Weiser’s time there.[17] This was a piece of string attached to a stepper motor and controlled by a LAN connection; network activity caused the string to twitch, yielding a peripherally noticeable indication of traffic. Weiser called this an example of calm technology.[18]

A present manifestation of this trend is the widespread diffusion of mobile phones. Many mobile phones support high speed data transmission, video services, and other services with powerful computational ability. Although these mobile devices are not necessarily manifestations of ubiquitous computing, there are examples, such as Japan’s Yaoyorozu (“Eight Million Gods”) Project in which mobile devices, coupled with radio frequency identification tags demonstrate that ubiquitous computing is already present in some form.[19]

Ambient Devices has produced an “orb”, a “dashboard”, and a “weather beacon“: these decorative devices receive data from a wireless network and report current events, such as stock prices and the weather, like the Nabaztag produced by Violet Snowden.

The Australian futurist Mark Pesce has produced a highly configurable 52-LED LAMP enabled lamp which uses Wi-Fi named MooresCloud after Gordon Moore.[20]

The Unified Computer Intelligence Corporation launched a device called Ubi – The Ubiquitous Computer designed to allow voice interaction with the home and provide constant access to information.[21]

Ubiquitous computing research has focused on building an environment in which computers allow humans to focus attention on select aspects of the environment and operate in supervisory and policy-making roles. Ubiquitous computing emphasizes the creation of a human computer interface that can interpret and support a user’s intentions. For example, MIT’s Project Oxygen seeks to create a system in which computation is as pervasive as air:

In the future, computation will be human centered. It will be freely available everywhere, like batteries and power sockets, or oxygen in the air we breathe…We will not need to carry our own devices around with us. Instead, configurable generic devices, either handheld or embedded in the environment, will bring computation to us, whenever we need it and wherever we might be. As we interact with these “anonymous” devices, they will adopt our information personalities. They will respect our desires for privacy and security. We won’t have to type, click, or learn new computer jargon. Instead, we’ll communicate naturally, using speech and gestures that describe our intent…[22]

This is a fundamental transition that does not seek to escape the physical world and “enter some metallic, gigabyte-infested cyberspace” but rather brings computers and communications to us, making them “synonymous with the useful tasks they perform”.[19]

Network robots link ubiquitous networks with robots, contributing to the creation of new lifestyles and solutions to address a variety of social problems including the aging of population and nursing care.[23]


Privacy is easily the most often-cited criticism of ubiquitous computing (ubicomp), and may be the greatest barrier to its long-term success.[24]

Public policy problems are often “preceded by long shadows, long trains of activity”, emerging slowly, over decades or even the course of a century. There is a need for a long-term view to guide policy decision making, as this will assist in identifying long-term problems or opportunities related to the ubiquitous computing environment. This information can reduce uncertainty and guide the decisions of both policy makers and those directly involved in system development (Wedemeyer et al. 2001). One important consideration is the degree to which different opinions form around a single problem. Some issues may have strong consensus about their importance, even if there are great differences in opinion regarding the cause or solution. For example, few people will differ in their assessment of a highly tangible problem with physical impact such as terrorists using new weapons of mass destruction to destroy human life. The problem statements outlined above that address the future evolution of the human species or challenges to identity have clear cultural or religious implications and are likely to have greater variance in opinion about them.[19]

Ubiquitous computing research centres

This is a list of notable institutions who claim to have a focus on Ubiquitous computing sorted by country:Canada

Topological Media Lab, Concordia University, CanadaFinland

Community Imaging Group, University of Oulu, FinlandGermany

Tele cooperation Office (TECO), Karlsruhe Institute of Technology, GermanyIndia

Ubiquitous Computing Research Resource Centre (UCRC), Centre for Development of Advanced Computing[25]Pakistan

Centre for Research in Ubiquitous Computing (CRUC), Karachi, Pakistan.Sweden

Mobile Life Centre, Stockholm UniversityUnited Kingdom

Mixed Reality Lab, University of Nottingham

See also


  1. ^ Nieuwdorp, E. (2007). “The pervasive discourse”. Computers in Entertainment5(2): 13. doi:10.1145/1279540.1279553S2CID 17759896.
  2. ^ Hansmann, Uwe (2003). Pervasive Computing: The Mobile World. Springer. ISBN 978-3-540-00218-5.
  3. ^ Greenfield, Adam (2006). Everyware: The Dawning Age of Ubiquitous Computing. New Riders. pp. 11–12. ISBN 978-0-321-38401-0.
  4. ^ “World Haptics Conferences”. Haptics Technical Committee. Archived from the original on 16 November 2011.
  5. a b Poslad, Stefan (2009). Ubiquitous Computing Smart Devices, Smart Environments and Smart Interaction (PDF). Wiley. ISBN 978-0-470-03560-3.
  6. ^ Kang, Byeong-Ho (January 2007). “Ubiquitous Computing Environment Threats and Defensive Measures”International Journal of Multimedia and Ubiquitous Engineering2 (1): 47–60. Retrieved 2019-03-22.
  7. a b Weiser, Mark (1991). “The Computer for the 21st Century”. Archived from the original on 22 October 2014.
  8. ^ Weiser, Mark (March 23, 1993). “Some Computer Science Issues in Ubiquitous Computing”. CACM. Retrieved May 28, 2019.
  9. ^ Weiser, M.; Gold, R.; Brown, J.S. (1999-05-11). “Ubiquitous computing”. Archived from the original on 10 March 2009.
  10. a b Weiser, Mark (17 March 1996). “Ubiquitous computing”. Archived from the original on 2 June 2018.
  11. ^ Krikke, J (2005). “T-Engine: Japan’s ubiquitous computing architecture is ready for prime time”. IEEE Pervasive Computing4 (2): 4–9. doi:10.1109/MPRV.2005.40S2CID 11365911.
  12. ^ “T-Engine Forum Summary”. Archived from the original on 21 October 2018. Retrieved 25 August 2011.
  13. ^ “MIT Media Lab – Things That Think Consortium”MIT. Retrieved 2007-11-03.
  14. ^ “MIT Project Oxygen: Overview”MIT. Retrieved 2007-11-03.
  15. ^ “Equator”UCL. Retrieved 2009-11-19.
  16. ^ “Center of excellence for Ubiquitous System” (in Korean). CUS. Archived from the original on 2 October 2011.
  17. ^ Weiser, Mark (2017-05-03). “Designing Calm Technology”. Retrieved May 27,2019.
  18. ^ Weiser, Mark; Gold, Rich; Brown, John Seely (1999). “The Origins of Ubiquitous Computing Research at PARC in the Late 1980s”IBM Systems Journal38 (4): 693. doi:10.1147/sj.384.0693S2CID 38805890.
  19. a b c Winter, Jenifer (December 2008). “Emerging Policy Problems Related to Ubiquitous Computing: Negotiating Stakeholders’ Visions of the Future”. Knowledge, Technology & Policy21 (4): 191–203. doi:10.1007/s12130-008-9058-4hdl:10125/63534S2CID 109339320.
  20. ^ Fingas, Jon (13 October 2012). “MooresCloud Light runs Linux, puts LAMP on your lamp (video)”. Retrieved 22 March 2019.
  21. ^ “Ubi Cloud”. Archived from the original on 2 January 2015.
  22. ^ “MIT Project Oxygen: Overview”. Archived from the original on July 5, 2004.
  23. ^ “Network Robot Forum”. Archived from the original on October 24, 2007.
  24. ^ Hong, Jason I.; Landay, James A. (June 2004). “An architecture for privacy-sensitive ubiquitous computing” (PDF). Proceedings of the 2nd international conference on Mobile systems, applications, and services – MobiSYS ’04. pp. 177=189. doi:10.1145/990064.990087ISBN 1581137931S2CID 3776760.
  25. ^ “Ubiquitous Computing Projects”Department of Electronics & Information Technology (DeitY). Ministry of Communications & IT, Government of India. Archived from the original on 2015-07-07. Retrieved 2015-07-07.

Further reading

External links

Wikimedia Commons has media related to Ubiquitous computing.


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