Advanced SearchTechniques for Large Scale Data Analytics
Pavel Zezula and Jan Sedmidubsky
Masaryk University
http://disa.fi.muni.cz
 Teachers:
 prof. Ing. Pavel Zezula, CSc.
 RNDr. Jan Sedmidubsky, Ph.D.
 Inspiration:
 Inspired by the book of people from Stanford:
 Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of
Massive Datasets. Cambridge University Press, 2nd
Edition, 476 pages, 2014.
 Additional information: http://mmds.org/
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 2
 Introduction
 Block 1:
 Support for Distributed Processing
 Retrieval Evaluation
 Clustering
 Exercises on topics of Block 1
 Block 2:
 Finding Frequent Item Sets
 Finding Similar Items
 Searching in Data Streams
 Exercises on topics of Block 2
 Block 3:
 Link Analysis
 Search Applications
 Seznam.cz – A Search Engine in Practice
 Exercises on topics of Block 3
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 3
 Block 1:
 Support for Distributed Processing
 Distributed file system
 MapReduce, Algorithms using MapReduce
 Cost model and performance evaluation
 Retrieval Evaluation
 Retrieval metrics
 Clustering
 K-means algorithms
 Clustering in non-Euclidean spaces
 Clustering for streams and parallelism
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 4
 Block 2:
 Finding Frequent Item Sets
 Handling large datasets in main memory
 Counting frequent items in a stream
 Finding Similar Items
 Applications of near-neighbor search
 Shingling of documents
 Similarity-preserving summaries of sets
 Locality sensitive hashing
 Searching in Data Streams
 The stream data model
 Filtering streams
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 5
 Block 3:
 Link Analysis
 Page Rank
 Topic sensitive
 Link spam
 Search Applications
 Advertising on the web
 Recommendation systems (collaborative filtering)
 Mining social-network graphs
 Seznam.cz
 A Search Engine in Practice
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 6
Goals:
1) We search to get results
2) We ask to find answers
3) We use filters so that the right staff finds us
4) We browse while wandering and way-finding
in restricted space
 In reality, we move fluidly between modes of
ask, browse, filter, and search
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 8
 Defined by software
 Buy engine, then figure out what it is good for
 It often fails because
 It is not easy to use
 It is not able to handle needed content types
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 9
 85% of all web traffic comes from search
engines
 450+ million searches/day are performed in
North America alone
 70%+ of all searches are done on Google sites
Search is the most popular application
(second to E-mail??)
10Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 60% of searchers NEVER go past 1st page of
search results
 The top three results draw 80% of the
attention
 The first few results inordinately influence
query reformulation
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 When we search, our next actions are
reactions to the stimuli of a previous search
 What we find is changing what we seek
 In any case, search must be:
fast, simple, and relevant
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 Elements of global search:
Users – goals, psychology, behavior
Interface – interaction, affordances, language
Engine – features, technology, algorithms
Content – indexing structure, metadata
Creators – tools, process, incentives
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 Assuming information continually and
instantaneously available on the web:
1) We are increasingly handing off the job of
remembering to search engines
2) When we need answer, we do not think, we go
immediately to a nearest Web connection
3) When we expect information to be easily found
again, we do not remember it well
4) Our original memory of facts is changing to a
memory of ways to find the facts
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 14
Search is subjective and also depends on
visual and emotional attributes, e.g.
shocking, funny, etc.
 Browser
 not clear end-goal; series of unrelated searches;
jump across unrelated topics; expects surprises
and random search hints
 Surfer
 moderate clarity of end-goal; exploratory
actions at the beginning; e.g. planning a holiday
 Searcher
 very clear about what is searching for;
completeness and clarity of results are
important
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 15
Context
Content
Prevalent strategy
Scalability
data volume
number of users (queries)
variety of data types
multi-aspect queries
grade
high
low
well established cutting-edge research
peer-to-peer
centralized
parallel
distributed
self-organized
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Determinism
exact match ► similarity
precise ► approximate
same answer ► variable
fixed infrastr. ► mapping
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19Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
Data contains value and knowledge
 But to extract the knowledge
data needs to be
 Stored
 Managed
 And ANALYZED  this class
Data Mining ≈ Big Data ≈
Predictive Analytics ≈ Data Science
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 20
21Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
 Given lots of data
 Discover patterns and models that are:
 Valid: hold on new data with some certainty
 Useful: should be possible to act on the item
 Unexpected: non-obvious to the system
 Understandable: humans should be able to
interpret the pattern
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 Descriptive methods
 Find human-interpretable patterns that
describe the data
 Example: Clustering
 Predictive methods
 Use some variables to predict unknown
or future values of other variables
 Example: Recommender systems
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 23
 A risk with “Data mining” is that an analyst
can “discover” patterns that are meaningless
 Statisticians call it Bonferroni’s principle:
 Roughly, if you look in more places for interesting
patterns than your amount of data will support,
you are bound to find crap
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 24
Example:
 We want to find (unrelated) people who at least twice
have stayed at the same hotel on the same day
 109 people being tracked
 1,000 days
 Each person stays in a hotel 1% of time (1 day out of 100)
 Hotels hold 100 people (so 105 hotels)
 If everyone behaves randomly (i.e., no terrorists) will the
data mining detect anything suspicious?
 Expected number of “suspicious” pairs of people:
 250,000
 … too many combinations to check – we need to have some
additional evidence to find “suspicious” pairs of people in
some more efficient way
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Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 26
Scalability
Streaming
Context
Quality
Usage
 Data mining overlaps with:
 Databases: Large-scale data, simple queries
 Machine learning: Small data, Complex models
 CS Theory: (Randomized) Algorithms
 Different cultures:
 To a DB person, data mining is an extreme form of
analytic processing – queries that
examine large amounts of data
 Result is the query answer
 To a ML person, data-mining
is the inference of models
 Result is the parameters of the model
 In this class we will do both!
Machine
Learning
CS
Theory
Data
Mining
Database
systems
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 27
 This course overlaps with machine learning,
statistics, artificial intelligence, databases but
more stress on
 Scalability (big data)
 Algorithms
 Computing architectures
 Automation for handling
large data
28Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212)
Machine
Learning
Statistics
Data Mining
Database
systems
 We will learn to mine different types of data:
 Data is high dimensional
 Data is a graph
 Data is infinite/never-ending
 Data is labeled
 We will learn to use different models of
computation:
 MapReduce
 Streams and online algorithms
 Single machine in-memory
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 29
 We will learn to solve real-world problems:
 Recommender systems
 Market Basket Analysis
 Spam detection
 Duplicate document detection
 We will learn various “tools”:
 Linear algebra (SVD, Rec. Sys., Communities)
 Optimization (stochastic gradient descent)
 Dynamic programming (frequent itemsets)
 Hashing (LSH, Bloom filters)
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 30
High dim.
data
Locality
sensitive
hashing
Clustering
Dimensional
ity
reduction
Graph
data
PageRank,
SimRank
Community
Detection
Spam
Detection
Infinite
data
Filtering
data
streams
Web
advertising
Queries on
streams
Machine
learning
SVM
Decision
Trees
Perceptron,
kNN
Apps
Recommen
der systems
Association
Rules
Duplicate
document
detection
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 31
Pavel Zezula, Jan Sedmidubsky. Advanced Search Techniques for Large Scale Data Analytics (PA212) 32
How do you want that data?