Explain in your own words what a Filter Bubble is

Explain in your own words what a Filter Bubble is

BCO216 · Managing Information Systems Task brief & rubrics Final Assignment Fall 2021
Task
The student will have to answer 3 open questions and work on a case study and answer its 5 questions by himself without employing copy and paste practices or
working in teams. Cover sheet, table of content and in-text References and Bibliography are expected and will be graded.
The work needs to be converted into a pdf file before uploading to the submission point in Moodle.
Questions

Explain in your own words what a Filter Bubble is

  1. Explain in your own words what a Filter Bubble is. How can that lead to a ‘Web of One’?
  2. List at least 5 different AI systems from ‘simplest’ to most developed. Explain at least one business application for everyone. Include in every explanation
    a challenge the system faces.
  3. Digital systems are more and better connected as development progresses. Users and businesses have increasingly remote access to all kinds of data.
    a. List at least 3 challenges to privacy and best practices to mitigate the threats.
    b. List at least 3 challenges to security and best practices to prevent security breaches.
    Case Study Big Data · Big Data – Big Rewards
    Today’s companies are dealing with an avalanche of data from social media, search, and sensors as well as from traditional sources. In 2012, the amount of
    digital information generated is expected to reach 988 exabytes, which is the equivalent to a stack of books from the sun to the planet Pluto and back. Making
    sense of “big data” has become one of the primary challenges for corporations of all shapes and sizes, but it also rep-resents new opportunities. How are
    companies currently taking advantage of big data opportunities? The British Library had to adapt to handle big data. Every year visitors to the British Library Web
    site perform over 6 billion searches, and the library is also responsible for preserving British Web sites that no longer exist but need to be preserved for historical
    purposes, such as the Web sites for past politicians. Traditional data management methods proved inadequate to archive millions of these Web pages, and
    legacy analytic tools couldn’t extract useful knowledge from such quantities of data. So, the British Library partnered with IBM to implement a big data solution
    to these challenges. IBM Big Sheets is an insight engine that helps extract, annotate, and visually analyze vast amounts of unstructured Web data, delivering the
    results via a Web browser. For example, users can see search results in a pie chart. IBM Big Sheets is built atop the Hadoop framework, so it can process large
    amounts of data quickly and efficiently. State and federal law enforcement agencies are analyzing big data to discover hidden patterns in criminal activity such as
    correlations between time, opportunity, and organizations, or non-obvious relationships (see Chapter 4) between individuals and criminal organizations that
    would be difficult to uncover in smaller data sets. Criminals and criminal organizations are increasingly using the Internet to coordinate and perpetrate their
    crimes. New tools allow agencies to analyze data from a wide array of sources and apply analytics to predict future crime patterns. This means that law
    enforcement can become more proactive in its efforts to fight crime and stop it before it occurs. In New York City, the Real Time Crime Center data warehouse
    contains millions of data points on city crime and criminals. IBM and the New York City Police Department (NYPD)worked together to create the warehouse,
    which contains data on over 120 mil-lion criminal complaints, 31 million national crime records, and 33 billion public records. The system’s search capabilities
    allow the NYPD to quickly obtain data from any of these data sources. Information on criminals, such as a suspect’s photo with details of past offenses or
    addresses with maps, can be visualized in seconds on a video wall or instantly relayed to officers at a crime scene. Other organizations are using the data to go
    green, or, in the case of Vestas, to go even greener. Headquartered in Denmark, Vestas is the world’s largest wind energy company, with over 43,000 wind
    turbines across 66 countries. Location data are important to Vestas so that it can accurately place its turbines for optimal wind power generation. Areas without
    enough wind will not generate the necessary power,