The document discusses the use of Java for data mining in production, exploring topics like machine learning, Hadoop, and Spark. It highlights typical data mining questions, various algorithms, and the importance of data preparation and feature extraction. Additionally, it emphasizes the growing significance of Python and methods for integrating Java with data mining technologies such as Weka and Mahout.

1. Java in production for
Data Mining Research
projects
Alexey Zinovyev, Java Trainer in EPAM

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About
I am a <graph theory, machine learning,
traffic jams prediction, BigData algorithms>
scientist
But I'm a <Java, NoSQL, Hadoop, Spark>
programmer

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In this topic …
A lot of strange pictures and technologies from crazy zoo
We talk about
• Data Mining
• Hadoop ecosystem
• Spark and its friends
• Machine Learning libraries

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Are you a Hadoop developer?

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Let’s do THIS!

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The Good Old Days

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One of these fine days...

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We need in Python dev 'cause Data Mining

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No, you are JavaEE developer only, continue …

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Write your backends, dude!

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Let’s talk about it, Java-boy...

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Can a Java programmer to be a Data Scientist?

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Sexy Data Scientist

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Real Data Scientist

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And what I tell you, young man

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And what I tell you, young man

17. WHAT IS DATA MINING?

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Statistics?

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Tag cloud from B2B conference?

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Not OLAP, 100%

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Hey, man, predict something!

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Hey, man, predict something!

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Man or sofa?

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SUBJECT AREA

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Typical questions for DM
• Which loan applicants are high-risk?

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Typical questions for DM
• Which loan applicants are high-risk?
• How do we detect phone card fraud?

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Typical questions for DM
• Which loan applicants are high-risk?
• How do we detect phone card fraud?
• What is the revenue prediction for next year?

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Typical questions for DM
• Which loan applicants are high-risk?
• How do we detect phone card fraud?
• What is the revenue prediction for next year?
• Can you recommend music for users?

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It’s Time for Java Superhero, yeah!

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Before patterns discovering you should ..
• Select small pieces
• Define default values for missed
data
• Remove strange signals from data
• Merge some tables in one if
required

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TARGET DATA &
PERSONAL DATA

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Targeting
by …

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Pay with your personal data
All your personal data (PD) are
being deeply mined

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Pay with your personal data
The industry of collecting,
aggregating, and brokering PD is
“database marketing.”

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Pay with your personal data
1.1 billion browser cookies, 200
million mobile profiles, and an
average of 1,500 pieces of data per
consumer in Acxiom

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RTB

37. DATA

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Datasets
• Facebook users, tweets
• Trade transactions
• Government
• Medicine (genomic data)
• Telecommunications

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Data Sources
• Relational Databases
• Data warehouses (Historical data)
• Files in CSV or in binary format
• Internet or electronic mails
• Scientific, research (R, Octave,
Matlab)

40. PATTERN MINING

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Association rule learning

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What is Cluster Analysis?
It is the process of finding model of function that describes
and distinguishes data class to predict the class of objects
whose class label is unknown.

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Different algorithms – different results

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Regression

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• Training set of classified
examples (supervised learning)
Classification

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• Training set of classified
examples (supervised learning)
• Test set of non-classified items
Classification

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• Training set of classified
examples (supervised learning)
• Test set of non-classified items
• Main goal: find a function
(classifier) that maps input data
to a category (class)
Classification

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Decision trees

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Cruel Tree

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Green circle is blue square or red
triangle? Let’s ask its neighbors!
kNN (k-nearest neighbor)

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Hit parade of algorithms

52. FASHION LANGUAGES

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Octave

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• A small amount of ML algorithms
• All your matrixes are belong to us!
• Single thread model
• Java support
• Octave in Java?
Why not Octave?

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Do you like
this GUI?

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• 25% of R packs are written in Java
• Syntax is too sweet
• You should read 1000 lines in docs
to write 1 line of code
• Single thread model for 95%
algorithms
Why not R?

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Now Python is an idol for young scientists
due to the low barrier to entry
Why not Python?

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• High-level language
• Have you ever heard about a
Jython?
• Long way to real Highload
production
• We are not Python developers
Why not Python?

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DM libraries

60. Let’s run on
JVM!

61. JAVA ECOSYSTEM

62. Family

63. Spring Data

64. HADOOP

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Hadoop

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Hadoop and Data Knights

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MapReduce for WordCount

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How to make features from Hadoop cluster?

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Pig & Hive

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Hive

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PIG

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PIG (Triangle count)

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Pig
• User Defined Functions (UDF)

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Pig
• User Defined Functions (UDF)
• FOREACH

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Pig
• User Defined Functions (UDF)
• FOREACH
• Pipeline-style

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Pig
• User Defined Functions (UDF)
• FOREACH
• Pipeline-style
• Easy parallelization

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Pig

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Why do we need in special graph approach?

79. HOW TO MAKE GRAPH
FEATURES

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SNA

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MapReduce for iterative calculations
• High complexity of graph problem reduction to key-value
model
• Iteration algorithms, but multiple chained jobs in M/R
with full saving and reading of each state
Think like a vertex…

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Data vs
Graph

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Messaging

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TRAIN
MODEL

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Java API for Data mining, JSR 73 and JSR 247
• javax.datamining.supervised defines the supervised
function-related interfaces
• javax.datamining.algorithm contains all mining algorithm
subclass packages
• JDM 2.0 adds Text Mining, Time series and so on..
JDM

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Who knows Weka?

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• Connectors to R, Octave, Matlab, Hadoop, NoSQL/SQL
databases
• Source code of all algorithms in Java
• Preprocessing tools: discretization, normalization,
resampling, attribute selection, transforming and combining
Weka

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Weka

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Weka +
Hadoop

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SPMF
• It’s codebase of algorithms in pattern mining field
• It has cool examples and implementation of 109
algorithms
• Cool performance results in specific area
• Codebase grows very fast
• Not so many classification algorithms are covered

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Mahout
• Scalable machine learning with Samsara
• Advanced Implementations of Java’s Collections Framework
for better Performance.
• New algorithms will build on Spark platform
• Collaborative Filtering, Classification, Clustering,
Dimensionality Reduction, Miscellaneous are supported

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Collaborative Filtering

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Code sample Mahout (K-Means)
// read the point values and generate vectors from input data
final List vectors = vectorize(points);
// Write data to sequence hadoop sequence files
writePointsToFile(configuration, vectors);
// Write initial centers for clusters
writeClusterInitialCenters(configuration, vectors);
// Run K-means algorithm
final Path inputPath = new Path(POINTS_PATH);
final Path clustersPath = new Path(CLUSTERS_PATH);
final Path outputPath = new Path(OUTPUT_PATH);
HadoopUtil.delete(configuration, outputPath);
KMeansDriver.run(configuration, inputPath, clustersPath, outputPath, 0.001, 10, true, 0, false);
// Read and print output values
readAndPrintOutputValues(configuration);

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Hadoop
ecosystem

95. HADOOP IS NOT SEXY

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Whaaaat?

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Map Reduce Job Writing

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Hadoop
Jobs

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Hadoop
Jobs

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YARN?

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SPARK: the bloody son of MR
• MapReduce in memory
• Up to 50x faster than Hadoop
• RDD is a basic building block
(immutable distributed
collections of objects)
• Pipeline API (no needs in PIG)

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GC & Spark
• > 100 GB for Spark apps
• big pauses as a result
• Garbage-First GC
• play with spark.storage.memoryFraction (cached
data/heap for transformation)
• no one recipe

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Mahout’s killer?

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MLlib supports
• Classification and regression
• Collaborative filtering
• Clustering
• Dimensionality reduction
• Optimization

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Code sample MLlib (K-Means)
// Cluster the data into two classes using KMeans
int numClusters = 2;
int numIterations = 20;
KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);
// Evaluate clustering by computing Within Set Sum of Squared Errors
double WSSSE = clusters.computeCost(parsedData.rdd());
System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
// Save and load model
clusters.save(sc.sc(), "myModelPath");
KMeansModel sameModel = KMeansModel.load(sc.sc(), "myModelPath");

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MLlib
• .. extends scikit-learn (Python lib) and Mahout
• .. runs fully on Spark and supports Spark’s Pipeline API
• .. dataset is represented by Spark SQL’s SchemaRDD
• .. supports Hive like external data source
• .. is well for large datasets and parallelized algorithms

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It solves all problems!

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In conclusion
• Think about your data

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In conclusion
• Think about your data
• Have friendship with DevOps engineer

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In conclusion
• Think about your data
• Have friendship with DevOps engineer
• Run Spark

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In conclusion
• Think about your data
• Have friendship with DevOps engineer
• Run Spark
• Learn algorithms

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In conclusion
• Think about your data
• Have friendship with DevOps engineer
• Run Spark
• Learn algorithms
• Write Java code

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Think Java

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Contacts
E-mail : Alexey_Zinovyev@epam.com
Twitter : @zaleslaw @BigDataRussia
LinkedIn: https://www.linkedin.com/in/zaleslaw