1) Big Data tutorial by Marko Grobelnik
Source: http://www.youtube.com/watch?v=D1oHfE0kka4
Big data applies to information that can't be processed or analyzed using traditional processes or tools. IBM claims in its 2012 report that every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Contents:
Source: http://www.youtube.com/watch?v=D1oHfE0kka4
Big data applies to information that can't be processed or analyzed using traditional processes or tools. IBM claims in its 2012 report that every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Contents:
0:00 Big - Data Tutorial
0:35 Outline
1:18 Big data - a growing torrent
3:00 Big data - capturing its value
3:38 What is Big - Data?
4:41 Characterization of Big - Data
5:39 Big - Data popularity on the Web
7:47 Big - Data in Gartner Hype - Cycle 2011
9:05 Why Big - Data?
9:34 Enabler: Data storage
10:35 Enabler: Computation capacity
12:04 Enabler: Data availability
13:27 Type of available data
14:40 Data available from social networks and mobile devices
16:09 Data available from ''Internet of Things''
17:43 Big - Data value chain
18:52 Gains from Big - Data per sector
21:54 Predicted lack of talent for Big - Data related technologies
23:16 Big - Data value chain
24:40 Tools
24:43 Types of tools typically used in Big - Data scenarios
27:02 Distributed infrastructure
28:31 Distributed processing
30:52 MapReduce
32:30 High - performance schema - free databases
35:31 Techniques
35:33 When Big - Data is really a hard problem?
38:39 What matters when dealing with data?
42:39 Meaningfulness of Analytic Answers (1/2)
43:58 Meaningfulness of Analytic Answers (2/2)
48:09 What are ''atypical'' operators on Big - Data
51:12 Analytical operators on Big - Data
51:28 What are ''atypical'' operators on Big - Data
53:06 Analytical operators on Big - Data
53:51 ...guide to Big - Data algorithmics
54:34 Applications
54:46 Application: Recommendation
55:47 The context of each click on the web site used for recommendation
56:33 Application: Online Advertising for NYTimes
56:59 Scale of one day NYTimes data
57:21 Application: Telecommunication Network Monitoring
59:07 Application: Monitoring global main stream news
59:12 http://newsfeed.ijs.si/
59:51 Semantic text enrichment (DBpedia, OpenCyc, ...) with Enrycher
60:10 Application: Text visualization
60:15 Application: Analysis of MSN - Messenger Social - network
61:00 Data Statistic: Total activity
61:50 Who talks to whom: Number of conversations
62:33 Who talks to whom: Conversation duration
63:14 Geography and communication
63:48 How is Europe talking
64:10 Network: Small - word
66:28 Literature on Big - Data
67:42 ...to conclude
2) Making Big Data Analytics Interactive
2) Making Big Data Analytics Interactive
Source: http://www.youtube.com/watch?v=JNpFVEXlZAE
"Hot topics at EECS Research Centers- Grad student presentations
Making Big Data Analytics Interactive - Matei Zaharia, AMP Lab (Algorithms, Machines and People Lab)
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