Llbl 564 Quiz - 1

Data comes from various sources such as sensors, businesses, and social media. Sensors collect data from the environment and physical objects, providing valuable information about temperature, pressure, and other variables. Businesses generate data through their operations, including sales, customer interactions, and financial transactions. Social media platforms collect data from user activities, such as posts, comments, and likes. Therefore, data can be found everywhere, as it is constantly being generated and collected from numerous sources in our digital world.

The correct answer is "None of these". This means that all of the options listed (inventions, literary works, and logo designs) are considered intellectual property. Intellectual property refers to creations of the mind, such as inventions, artistic works, and designs, that are protected by law from being copied or used without permission. Therefore, all of the options listed fall under the category of intellectual property.

Data Mining is normally used with Archived Data. This is because data mining involves extracting useful information and patterns from large datasets. Archived data refers to historical data that has been stored for future reference. This type of data is often used for analysis and decision-making purposes. Current data refers to real-time or up-to-date information, while stream data refers to continuous and rapidly changing data. Therefore, archived data is the most suitable for data mining as it provides a comprehensive and stable dataset for analysis.

Normalizing the data is the process in which an individual prepares the raw data before analyzing it to remove any spurious data. This involves organizing and standardizing the data in a consistent format, ensuring that it is free from any inconsistencies or errors. By normalizing the data, it becomes easier to analyze and compare different datasets, as well as identify any outliers or anomalies that may affect the accuracy of the analysis.

"Internet Searches" refers to the act of individuals using search engines to find information on the internet. As it is performed by humans, it falls under the category of human-sourced information.

The first Big Data challenge is the 1880 US Census. This was a significant milestone in the field of data collection and analysis, as it involved the processing and interpretation of a massive amount of data. The census collected detailed information about the population, such as demographics, occupations, and social characteristics. This required the development of new methods and technologies to handle and analyze such a vast amount of data, making it the first major challenge in dealing with Big Data.

The statement "Finding the internal data is the easy part of Big Data" is true. This implies that once the data is collected, the process of accessing and analyzing the data is relatively straightforward compared to other challenges in dealing with Big Data. However, it is important to note that while finding the internal data may be considered easier, the real challenge lies in extracting meaningful insights and value from the vast amount of data available.

Both JClouds and Cloud Bursting provide a hybrid model for using cloud resources as spare capacity to handle load. JClouds is an open-source library that allows developers to use cloud resources from various providers, including public and private clouds, to build and deploy applications. Cloud Bursting, on the other hand, is a technique that allows organizations to scale their on-premises infrastructure by using additional resources from the cloud when needed. Both options offer the flexibility to leverage cloud resources as spare capacity, making them suitable for handling load in a hybrid model.

The Big Data Task Team has provided three categories for data classification and labeling.

Business Solution Architects are responsible for putting together and organizing the discovered data so that it is ready to analyze. They have the expertise to design and implement data solutions, ensuring that the data is structured and accessible for analysis. They work closely with stakeholders to understand their requirements and translate them into effective data solutions. By organizing the data, Business Solution Architects enable businesses to derive valuable insights and make informed decisions based on the analyzed data.