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Last modified by Aaron Vimont on 2010/12/10 16:01

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 1: **Final Report and Presentation**
 2: 
 3: [[A15 - Warehouses with Interactive Content - How Massive Data Warehouses Manage Content by Team Awesome>>TeamAwesomeA15-FinalReports||rel="__blank"]]
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 5: Presentation - [[Link>>attach:HCCFpres1LEGIT.pdf||rel="__blank"]]
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 7: 
 8: **Previous Work:**
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10: Assignment 12 - [[Link>>http://xwiki.cs.colorado.edu/bin/download/HCCF2010/Team%20Awesome/HCCF-A12.pdf]]
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12: 
13: **Our Project - Warehouses with Interactive Content**
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15: We will be looking at interactive warehouses such as Netflix, YouTube and Google's 3D Warehouse to see how these sites handle changes and new content. We will primarily be focusing on Netflix because some information about their site is readily available. Netflix uses a rating system and recommendation system.  Some of their algorithms are available to the general public. We will use that information to help us understand how their system works. We will look at other sites, as mentioned before. These sites require a little more investigation and detailed information will be harder to come by. We will also research the dynamic aspect of interactive warehouses.  This should provide details as to how new content is stored and accessed by users. We will look at the system architecture, to see how warehouses distribute their content. By the end of our research, we hope to have an understanding of what works for warehouses and what does not. We will be able to provide suggestions to help improve the user experience with warehouses with interactive content.
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18: Our understanding of the project has not changed. We each understand the project and what we are trying to accomplish. We know what each person needs to research. We will collaborate together so all of us can come up with solutions together.  Our roles have so far stayed the same. We each have our area of research and will be showing the other group members what we have found. We will continue researching as the project progresses, but talk with each other about what we need to accomplish.
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20: 
21: **Our Team:**
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23: [[Adam Jackson>>XWiki.jacksoar]]
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25: Brionna Lopez
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27: [[Ian Smith>>XWiki.IanSmith]]
28: 
29: [[Aaron Vimont>>XWiki.vimonta]]
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31: 
32: **Our Work So Far:**
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34: [[Assignment 6>>A6TEAM_AWESOME]]
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36: 
37: Aaron Vimont (10/24/2010) - I have been research dynamic data warehousing and how users share and upload their own content.  Unfortunately, many of the paper I have come across and very detailed and require a better understanding of data warehouse practices. I have found useful information about the basic structure of warehouses and common practices with data mining. This has helped me better understand some of the challenges facing warehouse designers. I still would like to find specifics for sites like YouTube or Netflix. Some of that can be hard to come by. I will contact Holger and Jane Meyers and hopefully they can help with my understanding on how some of these warehouses work.
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39: Adam Jackson (10/25/2010) - My role so far has been to research the requirements that massive data warehouses have in order to operate. This covers physical demands such as space and bandwidth as well as user demands such as search and rating systems. I have discovered several articles describing how Google maintains it's worldwide data warehouses, but I have yet to find anything specific to the 3D Warehouse. My research has yet to turn up anything specific about YouTube or Netflix either, so in the next few weeks I will go outside the realm of Google searches and enlist the help of library staff to adequately search magazine, news, and research articles for relevant information.
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41: Ian Smith (10/25/2010) - I have been looking at the algorithms that power the user interfaces of data warehouses. I want to address the issues of how the content of a particular warehouse affects how its content is presented to users and how users affect the content that is presented to them. One very good resource that I've found so far is the Netflix Prize (http://www.netflixprize.com//community/viewtopic.php?id=1537), which invited people to come up with a new algorithm for how Netflix recommends users movies. Since so many warehouse algorithms are proprietary and usually kept private, it is nice to have a reference that is transparent and actually used by a real product. I've also found some interesting analyses of the YouTube recommendation algorithm (one here)

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