1.2 Motivating Challenges

As mentioned earlier, traditional data analysis techniques have often encountered practical difficulties in meeting the challenges posed by big data applications. The following are some of the specific challenges that motivated the development of data mining.

Scalability

Because of advances in data generation and collection, data sets with sizes of terabytes, petabytes, or even exabytes are becoming common. If data mining algorithms are to handle these massive data sets, they must be scalable. Many data mining algorithms employ special search strategies to handle exponential search problems. Scalability may also require the implementation of novel data structures to access individual records in an efficient manner. For instance, out-of-core algorithms may be necessary when processing data sets that cannot fit into main memory. Scalability can also be improved by using sampling or developing parallel and distributed algorithms. A general overview of techniques for scaling up data mining algorithms is given in Appendix F.

High Dimensionality

It is now common to encounter data sets with hundreds or thousands of attributes instead of the handful common a few decades ago. In bioinformatics, progress in microarray technology has produced gene expression data involving thousands of features. Data sets with temporal or spatial components also tend to have high dimensionality. For example,

consider a data set that contains measurements of temperature at various locations. If the temperature measurements are taken repeatedly for an extended period, the number of dimensions (features) increases in proportion to the number of measurements taken. Traditional data analysis techniques that were developed for low-dimensional data often do not work well for such high-dimensional data due to issues such as curse of dimensionality (to be discussed in Chapter 2 ). Also, for some data analysis algorithms, the computational complexity increases rapidly as the dimensionality (the number of features) increases.

Heterogeneous and Complex Data

Traditional data analysis methods often deal with data sets containing attributes of the same type, either continuous or categorical. As the role of data mining in business, science, medicine, and other fields has grown, so has the need for techniques that can handle heterogeneous attributes. Recent years have also seen the emergence of more complex data objects. Examples of such non-traditional types of data include web and social media data containing text, hyperlinks, images, audio, and videos; DNA data with sequential and three-dimensional structure; and climate data that consists of measurements (temperature, pressure, etc.) at various times and locations on the Earth’s surface. Techniques developed for mining such complex objects should take into consideration relationships in the data, such as temporal and spatial autocorrelation, graph connectivity, and parent-child relationships between the elements in semi-structured text and XML documents.

Data Ownership and Distribution

Sometimes, the data needed for an analysis is not stored in one location or owned by one organization. Instead, the data is geographically distributed among resources belonging to multiple entities. This requires the development

of distributed data mining techniques. The key challenges faced by distributed data mining algorithms include the following: (1) how to reduce the amount of communication needed to perform the distributed computation, (2) how to effectively consolidate the data mining results obtained from multiple sources, and (3) how to address data security and privacy issues.

Non-traditional Analysis

The traditional statistical approach is based on a hypothesize-and-test paradigm. In other words, a hypothesis is proposed, an experiment is designed to gather the data, and then the data is analyzed with respect to the hypothesis. Unfortunately, this process is extremely labor-intensive. Current data analysis tasks often require the generation and evaluation of thousands of hypotheses, and consequently, the development of some data mining techniques has been motivated by the desire to automate the process of hypothesis generation and evaluation. Furthermore, the data sets analyzed in data mining are typically not the result of a carefully designed experiment and often represent opportunistic samples of the data, rather than random samples.

View the video of the lesson you have chosen, and respond to the following items.

· Identify the instructional models exhibited by the teacher during instruction, and provide examples that validates this assumption. Refer to the  (Links to an external site.) document you used in the Week 5 Instructional Models discussion forum to refresh your memory of the four types of instructional models we learned and cite it as a source to support your response. (APA citation is shown in the required resources section for this week).

· From your vantage point, determine the teacher’s ability to engage students throughout the lesson. Were the students engaged, attentive, and having fun learning or were there areas that the teacher could have improved upon to make the lesson more engaging?

· Describe up to three things you liked about the lesson.

· Recommend one thing you would have done differently than the teacher in the video and why. If you would not change anything, justify why you think the lesson should remain as it is.

Compile the responses to the questions above in a way that it will be easy for you to transfer them to a PowerPoint presentation (e.g., bullet points would work best).

Create a PowerPoint with a slide for each of the items above (see below for instructions on how to create each slide).

· Use the Lesson Analysis PowerPoint Template   Download Lesson Analysis PowerPoint Templateprovided to create a visual of your lesson analysis.

· You will use the 7x7 rule to create your presentation. The 7x7 rule states that you use no more than seven bullet points per slide and no more than seven words per bullet point. This way your visual presentation will only show the main points on each slide without overwhelming your viewers without too many words. You still need to make your slides attractive by adding images and colors to make it attractive.

· Add your voice to fill in the gaps between the main points on your slides. Limit your narration to five minutes or less. Use your narration to explain each of your answers. More importantly, use it as an opportunity to share your passion about what you liked in the lesson and how you might modify the lesson to better engage students and make the learning experience fun. (View  Microsoft PowerPoint 2013 Tutorial | Recording Narration (Links to an external site.)  for instructions on how insert voice narration into a PowerPoint presentation.)

· If you need help with creating an effective PowerPoint presentation, please review the  How to Make a PowerPoint Presentation (Links to an external site.)  webpage from the UAGC Writing Center.

In your project,

· Create a PowerPoint with voice narration.

· Summarize the instructional models used in the lesson.

· Explain thoughts on the engagement level of the lesson.

· Describe up to three strengths of the lesson.

· Justify whether the lesson should be changed or stay the same.

The What Would You Do? final presentation

· Must use at least one scholarly source to complete this assignment. The Evidence-Based Models of Teaching   Download Evidence-Based Models of Teachingdocument you accessed in the Week 5 Instructional Models discussion forum will meet this requirement.

· Must be document all sources according to  APA Style (Links to an external site.)  as outlined in the Writing Center’s  How to Make a PowerPoint Presentation  (Links to an external site.) resource.

· Must include a separate title slide with the following:

· Title of the project in bold font

· Space should be between title and the rest of the information on the title page.

· Student’s name

· Name of institution University of Arizona Global Campus)

· Course name and number

· Instructor’s name

· Due date

· Must use at least one scholarly source in addition to the course text.

· The  Scholarly, Peer-Reviewed, and Other Credible Sources (Links to an external site.)  table offers additional guidance on appropriate source types. If you have questions about whether a specific source is appropriate for this assignment, please contact your instructor. Your instructor has the final say about the appropriateness of a specific source for a particular assignment.

· To assist you in completing the research required for this assignment, view this  UAGC Library Quick ‘n’ Dirty (Links to an external site.)  tutorial, which introduces the UAGC Library and the research process, and provides some library search tips.

· Must document any information used from sources in APA Style as outlined in the Writing Center’s  APA: Citing Within Your Paper (Links to an external site.)

· Must include a separate references slide that is formatted according to APA Style as outlined in the Writing Center. See the  APA: Formatting Your References List (Links to an external site.)  resource in the Writing Center for specifications.