I talk about the fundamental of Big Data, which includes Hadoop, Intensive Data Computing, and NoSQL database that have received highlight to compute and store BigData that is usually greater then Peta-byte data. Besides, I introduce the case studies that use Hadoop and NSQL DB.

1. HiPIC
Big Data Fundamentals in the
Emerging New Data World
PIT (Product Innovation Team)
Samsung Electronics America
San Jose, CA
Aug 17th 2012
Jongwook Woo (PhD)
High-Performance Internet Computing Center (HiPIC)
Educational Partner with Cloudera and Grants Awardee of Amazon AWS
Computer Information Systems Department
California State University, Los Angeles
Jongwook Woo
CSULA

2. HiPIC Contents
 Fundamentals of Big Data
 NoSQL DB: HBase, MongoDB
 Data-Intensive Computing: Hadoop
 Big Data Supporters and Use Cases
CSULA
Jongwook Woo

3. HiPIC Experience in Big Data
 Several publications regarding Hadoop and NoSQL
 “Apriori-Map/Reduce Algorithm”, Jongwook Woo, PDPTA
2012, Las Vegas (July 16-19, 2012)
 “Market Basket Analysis Algorithm with no-SQL DB HBase and
Hadoop”,Jongwook Woo, Siddharth Basopia, Yuhang Xu, Seon
Ho Kim, EDB 2012, Incheon, Aug. 25-27, 2011
 “Market Basket Analysis Algorithm with Map/Reduce of Cloud
Computing”, Jongwook Woo and Yuhang Xu, PDPTA 2011,
Las Vegas (July 18-21, 2011)
 Jongwook Woo, “Introduction to Cloud Computing”, in the
10th KOCSEA Technical Symposium, UNLV, Dec 18 - 19, 2009
 Talks in Korean Universities and companies
 Yonsei, Sookmyung, KAIST, Korean Polytech Univ
– Winter 2011
 VanillaBreeze
– Winter 2011
CSULA
Jongwook Woo

4. HiPIC Experience in Big Data (Cont’d)
 Grants
 Received Amazon AWS in Education Research Grant (July
2012 - July 2014)
 Received Amazon AWS in Education Coursework Grants (July
2012 - July 2013, Jan 2011 - Dec 2011
 Partnership
 Received Academic Education Partnership with Cloudera since
June 2012
 Certificate
 Certificate of Achievement in the Big Data University Training
Course, “Hadoop Fundamentals I”, July 8 2012
 Cloud Computing Blog
 http://dal-cloudcomputing.blogspot.com/
CSULA
Jongwook Woo

5. What is Big Data, Map/Reduce, Hadoop, NoSQL DB on
HiPIC Cloud Computing
Clo
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AWS ra
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No
ork
s
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Jongwook Woo

6. HiPIC Big Data
Too much data
Tera-Byte (1012), Peta-byte (1015)
– Because of web
– Sensor Data, Bioinformatics, Social
Computing, smart phone, online game…
Cannot handle with the legacy
approach
Too big
Un-/Semi-structured data
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Jongwook Woo

7. HiPIC Two Issues in Big Data
How to store Big Data
NoSQL DB
How to compute Big Data
Parallel Computing with multiple cheap
computers
– Not need super computers
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Jongwook Woo

8. HiPIC Contents
 Fundamentals of Big Data
 NoSQL DB: HBase, MongoDB
 Data-Intensive Computing: Hadoop
 Big Data Supporters and Use Cases
CSULA
Jongwook Woo

9. HiPIC New Data Trend
 Sparsity
Schema free data with sparse attributes
– Document Term vector
– User-Item matrix
– Semantic or social relations
No relational property
– nor complex join queries
• Log data
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Jongwook Woo

10. HiPIC New Data Trend (Cont’d)
Immutable
No need to update and delete data
– Only insert with versions
• Tracking history
• Lock-free (key based autonomicity)
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Jongwook Woo

11. HiPIC Big Data for RDBMS
 Issues in RDBMS
Hard to scale
– Relation gets broken
• Partitioning for scalability
• Replication for availability
Speed
– The Seek times of physical storage
• Slower than N/W speed
• 1TB disk: 10Mbps transfer rate
– 100K sec =>27.8 hrs
– With Multiple data sources at difference places
• 100 10GB disks: each 10Mbps transfer rate
– 1K sec =>16.7min
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Jongwook Woo

12. HiPIC Big Data for RDBMS (Cont’d)
Issues in RDBMS (Cont’d)
Data Integration
– Not good for un-/semi-structured data
• Many unstructured data
– Web or log data etc
RDB not good in parallelization
– Cannot split 1000 tasks to cheap 1000 PCs
efficiently
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Jongwook Woo

13. HiPIC RDBMS Issues
Solution
Big Data
⇒Data Cleansing by Hadoop
⇒ Data Computation (MapReduce, Pig)
⇒ Data Repositories (NoSQL DB: HBase,
Cassandra, MongoDB)
⇒Business Intelligence (Data Mining,
OLAP, Data Visualization, Reporting):
Hive, Mahout
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14. HiPIC NoSQL DBs
 not primarily built on tables,
 generally do not use SQL for data manipulation
 non-relational, distributed data stores
– often do not provide ACID (atomicity, consistency, isolation,
durability)
• which are the key attributes of classic RDB
 Fast Index on large amount of data
 Lookup by keys (key/value)
 NoSQL normally supports MapReduce
 Parallel computation
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Jongwook Woo

15. HiPIC Use Cases for NoSQL DB [1]
RDBMS replacement
for high-traffic web applications
Semi-structured content management
Real-time analytics & high-speed logging
Web Infrastructure
Web 2.0, Media, SaaS, Gaming,
Finance, Telecom, Healthcare, Government
Three NoSQL DB Approaches
Key/Value, Column, Document
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Jongwook Woo

16. HiPIC Data Store of NoSQL DB
 Key/Value store
(Key, Value)
Functions
– Index, versioning, sorting, locking, transaction,
replication
Apache Cassandra, Memcached
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Jongwook Woo

17. HiPIC Data Store of NoSQL DB (Cont’d)
 Column-Oriented Stores (Extensible Record
Stores)
stores data tables as sections of columns of data
– rather than as rows of data, like most RDBMS
• Sparse fields in RDBMS
– well-suited for OLAP-like workloads (e.g., data
warehouses)
Extensible record horizontally and vertically
partitioned across nodes
– Rows and Columns are distributed over multiple
nodes
BigTable, HBase, Cassandra, Hypertable
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Jongwook Woo

18. HiPIC Data Store of NoSQL DB (Cont’d)
StudentId Lastname Firstname email
1 Smith Joe smith@hi.com
2 Jones Mary mary@hi.com
3 Johnson Cathy cathy@hi.com
 Row Oriented
– 1,Smith, Joe, smith@hi.com;
– 2,Jones, Mary, mary@hi.com;
– 3,Johnson, Cathy, cathy@hi.com;
Column Oriented
– 1,2,3;
– Smith, Jones, Johnson;
– Joe, Mary, Cathy;
– smith@hi.com, mary@hi.com, cathy@hi.com;
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19. HiPIC
HBase Schema Example (Student/Course)
 RDBMS
 Students: (id, name, sex, age)
 Courses: (id, title, desc, teacher_id)
 S_C: (s_id, c_id, type)
 HBase
Column Families
id Info: Course
<student_id> Info:name Info:sex Info:age Course:<course_id>=
type
Column Families
id Info: student
<course_id> Info:title Info:desc Info:teacher_id student:<student_id>
=type
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Jongwook Woo

20. HiPIC Data Store of NoSQL DB (Cont’d)
 Document Store
Collections and Documents
– vs Tables and Records of RDB
Used in Search Engine/Repository
Multiple index to store indexed document
– no fixed fields
Not simple key-value lookup
– Use API
Functions
– No locking, Replication, Transaction
MongoDB, CouchDB, ThruDB, SimpleDB
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21. HiPIC The Great Divide [1]
MongoDB
HBase
MongoDB sweet spot: Easy, Flexible, Scalable
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22. HiPIC Understanding the Document Model [1]
{
_id:“A4304”
author: “nosh”,
date: 22/6/2010,
title: “Intro to MongoDB”
text: “MongoDB is an open source..”,
tags: [“webinar”, “opensource”]
comments: [{author: “mike”,
date: 11/18/2010,
txt: “Did you see the…”,
votes: 7},….]
}
Documents->Collections->Databases
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Jongwook Woo

23. HiPIC Document Model Makes Queries Simple [1]
Operators:
$gt, $lt, $gte, $lte, $ne, $all, $in, $nin, count, limit,
skip, group
Example:
db.posts.find({author: “nosh”,
tags: “webinar”})
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Jongwook Woo

24. HiPIC Selected Users [1]
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Jongwook Woo

25. HiPIC Contents
 Fundamentals of Big Data
 NoSQL DB: HBase, MongoDB
 Data-Intensive Computing: Hadoop
 Big Data Supporters and Use Cases
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Jongwook Woo

26. HiPIC Data nowadays
• Data Issues
o data grows to 10TB, and then 100TB.
o Unstructured data coming from sources
 like Facebook, Twitter, RFID readers, sensors,
and so on.
 Need to derive information from both the
relational data and the unstructured data
• as soon as possible.
• Solution to efficiently compute Big
Data
o Hadoop Map/Reduce
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27. HiPIC Solutions in Big Data Computation
 Map/Reduce by Google
 (Key, Value) parallel computing
 Apache Hadoop
 Big Data
⇒Data Computation (MapReduce, Pig)
 Integrating MapReduce and RDB
 Oracle + Hadoop
 Sybase IQ
 Vertica + Hadoop
 Hadoop DB
 Greenplum
 Aster Data
 Integrating MapReduce and NoSQL DB
 MongoDB MapReduce
 HBase
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28. HiPIC Apache Hadoop
 Motivated by Google Map/Reduce and GFS
 open source project of the Apache Foundation.
 framework written in Java
– originally developed by Doug Cutting
• who named it after his son's toy elephant.
 Two core Components
 Storage: HDFS
– High Bandwidth Clustered storage
 Processing: Map/Reduce
– Fault Tolerant Distributed Processing
 Hadoop scales linearly with
 data size
 Analysis complexity
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Jongwook Woo

29. HiPIC Hadoop issues
 Map/Reduce is not DB
 Algorithm in Restricted Parallel Computing
 HDFS and HBase
 Cannot compete with the functions in RDBMS
 But, useful for
 Semi-structured data model and high-level dataflow query
language on top of MapReduce
– Pig, Hive, Jsql, Cascading, Cloudbase
 Useful for huge (peta- or Terra-bytes) but non-complicated data
– Web crawling
– log analysis
• Log file for web companies
– New York Times case
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30. HiPIC MapReduce Pros & Cons Summary
 Good when
Huge data for input, intermediate, output
A few synchronization required
Read once; batch oriented datasets (ETL)
 Bad for
Fast response time
Large amount of shared data
Fine-grained synch needed
CPU-intensive not data-intensive
Continuous input stream
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31. HiPIC MapReduce in Detail
Functions borrowed from functional
programming languages (eg. Lisp)
Provides Restricted parallel programming
model on Hadoop
User implements Map() and Reduce()
Libraries (Hadoop) take care of
EVERYTHING else
– Parallelization
– Fault Tolerance
– Data Distribution
– Load Balancing
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Jongwook Woo

32. HiPIC Map
Convert input data to (key, value) pairs
map() functions run in parallel,
 creating different intermediate (key, value)
values from different input data sets
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Jongwook Woo

33. HiPIC Reduce
 reduce() combines those intermediate values
into one or more final values for that same
key
 reduce() functions also run in parallel,
each working on a different output key
 Bottleneck:
reduce phase can’t start until map phase is
completely finished.
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Jongwook Woo

34. HiPIC Example: Sort URLs in the largest hit order
Compute the largest hit URLs
 Stored in log files
Map()
 Input <logFilename, file text>
 Output: Parses file and emits <url, hit counts> pairs
– eg. <http://hello.com, 1>
Reduce()
 Input: <url, list of hit counts> from multiple map
nodes
 Output: Sums all values for the same key and emits
<url, TotalCount>
– eg.<http://hello.com, (3, 5, 2, 7)> => <http://hello.com, 17>
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Jongwook Woo

35. HiPIC Map/Reduce for URL visits
Input Log Data
Map1() Map2() … Mapm()
(http://hi.com, 1) (http://halo.com, 1)
(http://hello.com, 3) (http://hello.com, 5)
… …
Data Aggregation/Combine
(http://hi.com, <1, 1, …, 1>) (http://halo.com, <1, 5,>)
(http://hello.com, <3, 5, 2, 7>)
Reduce1 () Reduce2() … Reducel()
(http://hi.com, 32) (http://halo.com, 6)
(http://hello.com, 17)
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36. HiPIC Legacy Example
In late 2007, the New York Times
wanted to make available over the web
its entire archive of articles,
11 million in all, dating back to 1851.
four-terabyte pile of images in TIFF format.
needed to translate that four-terabyte pile of TIFFs
into more web-friendly PDF files.
– not a particularly complicated but large computing chore,
• requiring a whole lot of computer processing time.
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Jongwook Woo

37. HiPIC Legacy Example (Cont’d)
In late 2007, the New York Times
wanted to make available over the web
its entire archive of articles,
a software programmer at the Times, Derek Gottfrid,
– playing around with Amazon Web Services, Elastic
Compute Cloud (EC2),
• uploaded the four terabytes of TIFF data into Amazon's
Simple Storage System (S3)
• In less than 24 hours, 11 millions PDFs, all stored
neatly in S3 and ready to be served up to visitors to the
Times site.
 The total cost for the computing job? $240
– 10 cents per computer-hour times 100 computers times 24 hours
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Jongwook Woo

38. HiPIC Contents
 Fundamentals of Big Data
 NoSQL DB: HBase, MongoDB
 Data-Intensive Computing: Hadoop
 Big Data Supporters and Use Cases
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Jongwook Woo

39. HiPIC
Supporters of Big Data
 Apache Hadoop Supporters
 Cloudera
– Like Linux and Redhat
– HiPIC is an Academic Partner
 Hortonworks
– Pig
 Facebook
– Hive
 IBM
– Jaql
 NoSQL DB supporters
 MongoDB
– HiPIC tries to collaborate
 HBase, CouchDB, Apache Cassandra (originally by FB) etc
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40. HiPIC Similarities in Pig, Hive, and Jaql
• translate high-level languages into MapReduce jobs
o the programmer can work at a higher level
 than writing MapReduce jobs in Java or other
lower-level languages
• programs are much smaller than Java code.
• option to extend these languages,
o often by writing user-defined functions in Java.
• Interoperability
o programs written in these high-level languages can
be imbedded inside other languages as well.
• the same limitations as Hadoop does
o non-supporting random reads and writes
o and low-latency queries.
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41. HiPIC Pig
• developed at Yahoo Research around 2006
o moved into the Apache Software Foundation in
2007.
• PigLatin,
o Pig's language
o a data flow language
o well suited to processing unstructured data
 Unlike SQL, not require that the data have a
schema
 However, can still leverage the value of a schema
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42. HiPIC Hive
• developed at Facebook
o turns Hadoop into a data warehouse
o complete with a dialect of SQL for querying.
• HiveQL
o a declarative language (SQL dialect)
• Difference from PigLatin,
o you do not specify the data flow,
 but instead describe the result you want
 Hive figures out how to build a data flow to
achieve it.
o a schema is required,
 but not limited to one schema.
o data can have many schemas
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43. HiPIC Hive (Cont'd)
• Similarity with PigLatin and SQL,
o HiveQL on its own is a relationally complete
language
 but not a Turing complete language,
 That can express any computation
o can be extended through UDFs (User Defined
Functions) of Java
 just like Pig to be Turing complete
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44. HiPIC Jaql
• developed at IBM.
• a data flow language
o its native data structure format is JSON (JavaScript
Object Notation).
• Schemas are optional
• Turing complete on its own
o without the need for extension through UDFs.
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45. HiPIC
Use Cases
 Amazon AWS
 Facebook
 Twitter
 Craiglist
 HuffPOst | AOL
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46. HiPIC Amazon AWS
 amazon.com
 Consumer and seller business
 aws.amazon.com
 IT infrastructure business
– Focus on your business not IT management
 Pay as you go
– Pay for servers by the hour
– Pay for storage per Giga byte per month
– Pay for data transfer per Giga byte
 Services with many APIs
– S3: Simple Storage Service
– EC2: Elastic Compute Cloud
• Provide many virtual Linux servers
• Can run on multiple nodes
– Hadoop and HBase
– MongoDB
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47. HiPIC Amazon AWS (Cont’d)
 Customers on aws.amazon.com
 Samsung
– Smart TV hub sites: TV applications are on AWS
 Netflix
– ~25% of US internet traffic
– ~100% on AWS
 NASA JPL
– Analyze more than 200,000 images
 NASDAQ
– Using AWS S3
 HiPIC received research and teaching grants
from AWS
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48. HiPIC Facebook [7]
 Using Apache HBase
 For Titan and Puma
 HBase for FB
– Provide excellent write performance and good reads
– Nice features
• Scalable
• Fault Tolerance
• MapReduce
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49. HiPIC Titan: Facebook
 Message services in FB
Hundreds of millions of active users
15+ billion messages a month
50K instant message a second
 Challenges
High write throughput
– Every message, instant message, SMS, email
Massive Clusters
– Must be easily scalable
 Solution
Clustered HBase
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50. HiPIC Puma: Facebook
 ETL
 Extract, Transform, Load
– Data Integrating from many data sources to Data Warehouse
 Data analytics
– Domain owners’ web analytics for Ad and apps
• clicks, likes, shares, comments etc
 ETL before Puma
 8 – 24 hours
– Procedures: Scribe, HDFS, Hive, MySQL
 ETL after Puma
 Puma
– Real time MapReduce framework
 2 – 30 secs
– Procedures: Scribe, HDFS, Puma, HBase
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51. HiPIC Twitter [8]
 Three Challenges
Collecting Data
– Scribe as FB
Large Scale Storage and analysis
– Cassandra: ColumnFamily key-value store
– Hadoop
Rapid Learning over Big Data
– Pig
• 5% of Java code
• 5% of dev time
• Within 20% of running time
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52. HiPIC Craiglist in MongoDB [9]
 Craiglist
~700 cities, worldwide
~1 billion hits/day
~1.5 million posts/day
Servers
– ~500 servers
– ~100 MySQL servers
 Migrate to MongoDB
Scalable, Fast, Proven, Friendly
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53. HiPIC
HuffPost | AOL [10]
Two Machine Learning Use Cases
Comment Moderation
– Evaluate All New HuffPost User Comments
Every Day
• Identify Abusive / Aggressive Comments
• Auto Delete / Publish ~25% Comments Every
Day
Article Classification
– Tag Articles for Advertising
• E.g.: scary, salacious, …
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54. HiPIC HuffPost | AOL [10]
 Parallelize on Hadoop
Good news:
– Mahout, a parallel machine learning tool, is
already available.
– There are Mallet, libsvm, Weka, … that support
necessary algorithms.
Bad news:
– Mahout doesn’t support necessary algorithms
yet.
– Other algorithms do not run natively on Hadoop.
 build a flexible ML platform running on
Hadoop
Pig for Hadoop implementation.
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55. HiPIC
MapReduce Example
 Word Count in the previous slide
 Shortest Path in the graph
 Graph algorithm is very suitable for M/R, especially BFS
– Spreading activation type of processing
 Map:
– Input: a node n as a key, and (D, points-to) as its value
• D is the distance to the node from the start
• points-to is a list of nodes reachable from n
– Output: ∀p ∈ points-to, emit (p, D+1)
 Reduce:
– Input: possible distances to a given p
– Output: selects the minimum one
• Perform multiple iterations
 Iterative process for matrix, graph, network
– Apache HAMA needed?
• Iterative Process on Hadoop
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56. HiPIC
MapReduce Example (Cont’d)
 Social N/W analysis
 Recommend new friends (friend of a friend: FOAF)
 Map
– In: (x, <friendsx>)
– Out: if (u, x) are friends
• (u, < friendsx / friendsu >)
– < friendsx / friendsu >: friends of x but not friends of u
– Otherwise
• nil
 Reduce
– In: (u, < < friendsa / friendsu >, < friendsa / friendsu >, …>)
• Friends list of all users a, b, … who are friends of u
– Out: (u, < (X1 , N1 ), (X2 , N2 ), …>)
• Xm : FOAF of u
• Nm : Total number of occurrences in all FOAF lists
– To sort or rank the results
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57. HiPIC
MapReduce Example (Cont’d)
 Inverted Indexing (Full Text Search)
 Map (3 nodes):
– Input:
• Doc1: “Columbus’s egg”
• Doc 2: “The chicken and egg problem”
• Doc 3: “Easter Egg”
– Output:
• Map1: (“columbus’s”, (doc1, 1)), (“egg”, (doc1, 2))
• Map2: (“the”, (doc2, 1)), (“chicken”, (doc2, 2)), (“and”,
(doc2, 3)), (“egg”, (doc2, 4)), (“problem”, (doc2, 5))
• Map3: (“easter”, (doc3, 1)), (“egg”, (doc3, 2))
 Intermediate Shuffle
– (“columbus’s”, (doc1, 1)), (“egg”, <(doc1, 2), (doc2, 4), (doc3,
2)>), (“the”, (doc2, 1)), (“chicken”, (doc2, 2)), (“and”, (doc2,
3)), (“problem”, (doc2, 5)), (“easter”, (doc3, 1)))
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58. HiPIC
MapReduce Example (Cont’d)
Inverted Indexing (Full Text Search)
(Cont’d)
Reduce
– Input: (“columbus’s”, (doc1, 1)), (“egg”,
<(doc1, 2), (doc2, 4), (doc3, 2)>), (“the”,
(doc2, 1)), (“chicken”, (doc2, 2)), (“and”,
(doc2, 3)), (“problem”, (doc2, 5)), (“easter”,
(doc3, 1)))
– Output: same as above
• Assuming (“egg”, <(doc1, 2), (doc1, 4),
(doc3, 2)>), output is:
– (“egg”, <(doc1, <2, 4>), (doc3, 2)>),
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59. HiPIC Conclusion
 Era of Big Data
 Need to store and compute Big Data
 Storage: NoSQL DB
 Computation: Hadoop MapRedude
 Need to analyze Big Data in mobile computing, SNS
for Ad, User Behavior, Patterns …
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60. HiPIC
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61. HiPIC References
1) Introduction to MongoDB, Nosh Petigara, Jan 11, 2011
2) Hadoop Fundamental I, Big Data University
3) “Large Scale Data Analysis with Map/Reduce”, Marin
Dimitrov, Feb 2010
4) “BFS & MapReduce”, Edward J Yoon
http://blog.udanax.org/2009/02/breadth-first-search-
mapreduce.html, Feb 26 2009
5) “Market Basket Analysis Algorithm with no-SQL DB HBase
and Hadoop”,Jongwook Woo, Siddharth Basopia, Yuhang
Xu, Seon Ho Kim, The Third International Conference on
Emerging Databases (EDB 2011), Songdo Park Hotel,
Incheon, Korea, Aug. 25-27, 2011
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62. HiPIC References
6) “Market Basket Analysis Algorithm with Map/Reduce of
Cloud Computing”, Jongwook Woo and Yuhang Xu, The
2011 international Conference on Parallel and Distributed
Processing Techniques and Applications (PDPTA 2011),Las
Vegas (July 18-21, 2011)
7) Building Realtime Big Data Services at Facebook with
Hadoop and Hbase, Jonathan Gray, Facebook, Nov 11, 2011,
Hadoop World NYC
8) Analyzing Big Data at Twitter, Kevin Well, Web 2.0 Expo,
NYC, Sep 2010
9) Lessons Learned from Migrating 2+ Billion Documents at
Craigslist, Jeremy Zawodny, 2011
10) Machine Learning on Hadoop at Huffington Post | AOL, Thu
Kyaw and Sang Chul Song, Hadoop DC, Oct 4, 2011
CSULA
Jongwook Woo

63. HiPIC References
11) “MapReduce Debates and Schema-Free”, Woohyun Kim,
www.coordguru.com, http://blog.naver.com/wisereign, March
3 2010
12) “Large Scale Data Analysis with Map/Reduce”, Marin
Dimitrov, Feb 2010
13) “HBase Schema Design Case Studies”, Qingyan Liu, July 13
2009
CSULA
Jongwook Woo