Index

A

• Activation Function / Perceptron
• advanced visualization technique
  • about / Advanced visualization technique
  • prefuse / Prefuse
  • IVTK Graph toolkit / IVTK Graph toolkit
• Alternating Least Square (ALS) / Alternating least square – collaborative filtering
• Apache Kafka
  • about / Apache Kafka
  • IoT sensors, integration / Apache Kafka
  • social media real-time analytics / Apache Kafka
  • healthcare analytics / Apache Kafka
  • log analytics / Apache Kafka
  • risk aggregation, in finance / Apache Kafka
• Apache Spark
  • about / Apache Spark
  • concepts / Concepts
  • transformations / Transformations
  • actions / Actions
  • Spark Java API / Spark Java API
  • samples, Java 8 used / Spark samples using Java 8
  • data, loading / Loading data
  • data operations / Data operations – cleansing and munging
  • data, analyzing / Analyzing data – count, projection, grouping, aggregation, and max/min
  • common transformations, on Spark RDDs / Analyzing data – count, projection, grouping, aggregation, and max/min
  • actions, on RDDs / Actions on RDDs
  • paired RDDs / Paired RDDs
  • data, saving / Saving data
  • results, collecting / Collecting and printing results
  • results, printing / Collecting and printing results
  • programs, executing on Hadoop / Executing Spark programs on Hadoop
  • subprojects / Apache Spark sub-projects
  • machine learning modules / Spark machine learning modules
  • Apache Mahout / Mahout – a popular Java ML library
  • Deeplearning4j / Deeplearning4j – a deep learning library
  • Apriori algorithm, implementation / Implementation of the Apriori algorithm in Apache Spark
  • FP-Growth algorithm, executing / Running FP-Growth on Apache Spark
• Apache Spark, machine learning modules
  • MLlib Java API / MLlib Java API
  • machine learning libraries / Other machine learning libraries
• Apache Spark machine learning API
  • about / The new Spark ML API
  • machine learning algorithms / The new Spark ML API
  • features handling tools / The new Spark ML API
  • model selection / The new Spark ML API
  • tuning tools / The new Spark ML API
  • utility methods / The new Spark ML API
• Apriori algorithm
  • implementation, in Apache Spark / Implementation of the Apriori algorithm in Apache Spark
  • using / Implementation of the Apriori algorithm in Apache Spark
  • disadvantages / Implementation of the Apriori algorithm in Apache Spark
• artificial neural network / Introduction to neural networks

B

• bagging / Bagging
• bag of words / Bag of words
• bar chart
  • about / Bar charts
  • dataset, creating / Bar charts
• base project setup / Base project setup
  • default Kafka configurations, used / Base project setup
  • Maven Java project, for Spark Streaming / Base project setup
• bayes theorem / Bayes theorem
• bid data
  • Analytical products / Basics of Hadoop – a Java sub-project
  • Batch products / Basics of Hadoop – a Java sub-project
  • Streamlining / Basics of Hadoop – a Java sub-project
  • Machine learning libraries / Basics of Hadoop – a Java sub-project
  • NoSQL / Basics of Hadoop – a Java sub-project
  • Search / Basics of Hadoop – a Java sub-project
• bidirected graph / Refresher on graphs
• big data
  • data analytics on / Why data analytics on big data?
  • for data analytics / Big data for analytics
  • to bigger pay package, for Java developers / Big data – a bigger pay package for Java developers
  • Hadoop, basics / Basics of Hadoop – a Java sub-project
• big data stack
  • HDFS / Basics of Hadoop – a Java sub-project
  • Spark / Basics of Hadoop – a Java sub-project
  • Impala / Basics of Hadoop – a Java sub-project
  • MapReduce / Basics of Hadoop – a Java sub-project
  • Sqoop / Basics of Hadoop – a Java sub-project
  • Oozie / Basics of Hadoop – a Java sub-project
  • Flume / Basics of Hadoop – a Java sub-project
  • Kafka / Basics of Hadoop – a Java sub-project
  • Yarn / Basics of Hadoop – a Java sub-project
• binary classification dataset / What are the feature types that can be extracted from the datasets?
• boosting / Boosting
• bootstrapping / Bagging
• box plots / Box plots

C

• charts
  • used, in big data analytics / Using charts in big data analytics
  • for initial data exploration / Using charts in big data analytics
  • for data visualization and reporting / Using charts in big data analytics
• clustering
  • about / Clustering
  • customer segmentation / Clustering
  • search engines / Clustering
  • data exploration / Clustering
  • epidemic breakout zones, finding / Clustering
  • biology / Clustering
  • news categorization / Clustering
  • news, summarization / Clustering
  • types / Types of clustering
  • hierarchical clustering / Hierarchical clustering
  • K-means clustering / K-means clustering
  • k-means clustering, bisecting / Bisecting k-means clustering
  • for customer segmentation / Clustering for customer segmentation
• clustering algorithm
  • changing / Changing the clustering algorithm
• code
  • diving / Diving into the code:
• cold start problem / Content-based recommendation systems
• collaborative recommendation systems
  • about / Collaborative recommendation systems
  • advantages / Advantages
  • disadvantages / Disadvantages
  • collaborative filtering / Alternating least square – collaborative filtering
• common transformations, on Spark RDDs
  • Filter / Analyzing data – count, projection, grouping, aggregation, and max/min
  • Map / Analyzing data – count, projection, grouping, aggregation, and max/min
  • FlatMap / Analyzing data – count, projection, grouping, aggregation, and max/min
  • other transformations / Analyzing data – count, projection, grouping, aggregation, and max/min
• Conditional-FP tree / Efficient market basket analysis using FP-Growth algorithm
• Conditional FP Tree / Efficient market basket analysis using FP-Growth algorithm
• Conditional Pattern / Efficient market basket analysis using FP-Growth algorithm
• Conditional Patterns Base / Efficient market basket analysis using FP-Growth algorithm
• conditional probability / Conditional probability
• content-based recommendation systems
  • about / Content-based recommendation systems
  • Euclidean Distance / Content-based recommendation systems
  • Pearson Correlation / Content-based recommendation systems
  • dataset / Dataset
  • content-based recommender, on MovieLens dataset / Content-based recommender on MovieLens dataset
  • collaborative recommendation systems / Collaborative recommendation systems
• content-based recommender
  • on MovieLens dataset / Content-based recommender on MovieLens dataset
• context
  • building / Building SparkConf and context
• customer segmentation / Customer segmentation
  • clustering / Clustering for customer segmentation

D

• data
  • cleaning / Data cleaning and munging, Cleaning and munging the data
  • munging / Data cleaning and munging, Cleaning and munging the data
  • unwanted data, filtering / Data cleaning and munging
  • missing data, handling / Data cleaning and munging
  • incomplete data, handling / Data cleaning and munging
  • discarding / Data cleaning and munging
  • constant value, filling / Data cleaning and munging
  • average value, populating / Data cleaning and munging
  • nearest neighbor approach / Data cleaning and munging
  • converting, to proper format / Data cleaning and munging
  • basic analysis, with Spark SQL / Basic analysis of data with Spark SQL
  • parsing / Load and parse data
  • loading / Load and parse data
  • Spark-SQL way / Analyzing data – the Spark-SQL way
  • Spark SQL, for data exploration and analytics / Spark SQL for data exploration and analytics
  • Apriori algorithm / Market basket analysis – Apriori algorithm
  • Full Apriori algorithm / Full Apriori algorithm
  • preparing / Preparing the data
  • formatting / Formatting the data
  • storing / Storing the data
• data analytics
  • on big data / Why data analytics on big data?
  • distributed computing, on Hadoop / Distributed computing on Hadoop
  • HDFS concepts / HDFS concepts
  • Apache Spark / Apache Spark
• data exploration
  • of text data / Data exploration of text data
  / Data exploration, Data exploration
• dataframe / Dataframe and datasets
• DataNode / Main components of HDFS
• dataset / Dataset, Dataset
  • URL, for downloading / All India seasonal and annual average temperature series dataset
  • fields / All India seasonal and annual average temperature series dataset
  • data / All India seasonal and annual average temperature series dataset
  • reference link / Predicting house prices using linear regression
  • data, munging / Data cleaning and munging
  • full batch approach / Accuracy of multi-layer perceptrons
  • partial batch approach / Accuracy of multi-layer perceptrons
• dataset, linear regression
  • data, cleaning / Data cleaning and munging
  • exploring / Exploring the dataset
  • number of rows / Exploring the dataset
  • average price per zipcode, sorting by highest on top / Exploring the dataset
  • linear regression model, executing / Running and testing the linear regression model
  • linear regression model, testing / Running and testing the linear regression model
• dataset, logistic regression
  • data, cleaning / Data cleaning and munging
  • data, munging / Data cleaning and munging
  • data, missing / Data cleaning and munging
  • categorical data / Data cleaning and munging
  • data exploration / Data exploration
  • executing / Running and testing the logistic regression model
  • testing / Running and testing the logistic regression model
• dataset object / Training and testing the model
• datasets / Datasets, Dataframe and datasets
  • airports dataset / Datasets
  • routes dataset / Datasets
  • airlines dataset / Datasets
• datasets splitting
  • features selected / Choosing the best features for splitting the datasets
  • Gini Impurity / Choosing the best features for splitting the datasets
• data transfer techniques
  • Flume / Getting and preparing data in Hadoop
  • FTP / Getting and preparing data in Hadoop
  • Kafka / Getting and preparing data in Hadoop
  • HBase / Getting and preparing data in Hadoop
  • Hive / Getting and preparing data in Hadoop
  • Impala / Getting and preparing data in Hadoop
• data visualization
  • with Java JFreeChart / Data visualization with Java JFreeChart
  • charts, used in big data analytics / Using charts in big data analytics
• decision tree
  • about / What is a decision tree?
  • for classification / What is a decision tree?
  • for regression / What is a decision tree?
  • building / Building a decision tree
  • datasets splitting, features selected / Choosing the best features for splitting the datasets
  • advantages / Advantages of using decision trees
  • disadvantages / Disadvantages of using decision trees
  • dataset / Dataset
  • data exploration / Data exploration
  • data, cleaning / Cleaning and munging the data
  • data, munging / Cleaning and munging the data
  • model, training / Training and testing the model
  • model, testing / Training and testing the model
• deep learning
  • about / Deep learning
  • advantages / Advantages and use cases of deep learning
  • use cases / Advantages and use cases of deep learning
  • no feature engineering required / Advantages and use cases of deep learning
  • accuracy / Advantages and use cases of deep learning
  • information / More information on deep learning
• deeplearning4j / Deeplearning4j
  • references / Deeplearning4j
• Deeplearning4j
  • about / Deeplearning4j – a deep learning library
  • data, compressing / Compressing data
  • Avro / Avro and Parquet
  • Parquet / Avro and Parquet
• distributed computing
  • on Hadoop / Distributed computing on Hadoop

E

• edges / Refresher on graphs
• efficient market basket analysis
  • FP-Growth algorithm, used / Efficient market basket analysis using FP-Growth algorithm
• ensembling
  • about / Ensembling
  • voting / Ensembling
  • averaging / Ensembling
  • machine learning algorithm, used / Ensembling
  • types / Types of ensembling
  • bagging / Bagging
  • boosting / Boosting
  • advantages / Advantages and disadvantages of ensembling
  • disadvantages / Advantages and disadvantages of ensembling
  • random forest / Random forests
  • Gradient boosted trees (GBTs) / Gradient boosted trees (GBTs)

F

• feature selection
  • filter methods / How do you select the best features to train your models?
  • pearson correlation / How do you select the best features to train your models?
  • chi-square / How do you select the best features to train your models?
  • wrapper method / How do you select the best features to train your models?
  • forward selection / How do you select the best features to train your models?
  • backward elimination / How do you select the best features to train your models?
  • embedded method / How do you select the best features to train your models?
• FP-Growth algorithm
  • used, for efficient market basket analysis / Efficient market basket analysis using FP-Growth algorithm
  • transaction dataset / Efficient market basket analysis using FP-Growth algorithm
  • frequency of items, calculating / Efficient market basket analysis using FP-Growth algorithm
  • priority, assigning to items / Efficient market basket analysis using FP-Growth algorithm
  • array items, by priority / Efficient market basket analysis using FP-Growth algorithm
  • FP-Tree, building / Efficient market basket analysis using FP-Growth algorithm
  • frequent patterns, identifying from FP-Tree / Efficient market basket analysis using FP-Growth algorithm
  • conditional patterns, mining / Efficient market basket analysis using FP-Growth algorithm
  • conditional patterns, from leaf node Diapers / Efficient market basket analysis using FP-Growth algorithm
  • executing, on Apache Spark / Running FP-Growth on Apache Spark
• Frequent Item sets / Efficient market basket analysis using FP-Growth algorithm
• Frequent Pattern Mining
  • reference link / Running FP-Growth on Apache Spark
• Full Apriori algorithm
  • about / Full Apriori algorithm
  • dataset / Full Apriori algorithm
  • apriori implementation / Full Apriori algorithm

G

• Gradient boosted trees (GBTs)
  • about / Advantages and disadvantages of ensembling, Gradient boosted trees (GBTs)
  • dataset, used / Classification problem and dataset used
  • issues, classifying / Classification problem and dataset used
  • data exploration / Data exploration
  • random forest model, training / Training and testing our random forest model
  • random forest model, testing / Training and testing our random forest model
  • gradient boosted tree model, testing / Training and testing our gradient boosted tree model
  • gradient boosted tree model, training / Training and testing our gradient boosted tree model
• graph analytics
  • about / Graph analytics
  • path analytics / Graph analytics
  • connectivity analytics / Graph analytics
  • community analytics / Graph analytics
  • centrality analytics / Graph analytics
  • GraphFrames / GraphFrames
  • GraphFrames, used for building a graph / Building a graph using GraphFrames
  • on airports / Graph analytics on airports and their flights
  • on flights / Graph analytics on airports and their flights
  • datasets / Datasets
  • on flights data / Graph analytics on flights data
• graphs
  • refresher / Refresher on graphs
  • representing / Representing graphs
  • adjacency matrix / Representing graphs
  • adjacency list / Representing graphs
  • common terminology / Common terminology on graphs
  • common algorithms / Common algorithms on graphs
  • plotting / Plotting graphs
• graphs, common algorithms
  • breadth first search / Common algorithms on graphs
  • depth first search / Common algorithms on graphs
  • dijkstra shortest path / Common algorithms on graphs
  • PageRank algorithm / Common algorithms on graphs
• graphs, common terminology
  • vertices / Common terminology on graphs
  • edges / Common terminology on graphs
  • degrees / Common terminology on graphs
  • indegrees / Common terminology on graphs
  • outdegrees / Common terminology on graphs
• GraphStream library
  • reference link / Plotting graphs

H

• Hadoop
  • basics / Basics of Hadoop – a Java sub-project
  • features / Basics of Hadoop – a Java sub-project
  • distributed computing on / Distributed computing on Hadoop
  • core / Distributed computing on Hadoop
  • HDFS / Distributed computing on Hadoop
• Hadoop Distributed File System (HDFS)
  • about / Distributed computing on Hadoop
  • Open Source / Design and architecture of HDFS
  • Immense scalability, for amount of data / Design and architecture of HDFS
  • failover support / Design and architecture of HDFS
  • fault tolerance / Design and architecture of HDFS
  • data locality / Design and architecture of HDFS
  • NameNode / Main components of HDFS
  • DataNode / Main components of HDFS
  / Real-time SQL queries using Impala
• hand written digit recognizition
  • using CNN / Hand written digit recognizition using CNN
• HBase / Real-time data processing
• HDFS concepts
  • about / HDFS concepts
  • architecture / Design and architecture of HDFS
  • design / Design and architecture of HDFS
  • components / Main components of HDFS
  • simple commands / HDFS simple commands
• hierarchical clustering / Hierarchical clustering
• histogram
  • about / Histograms
  • using / When would you use a histogram?
  • creating, JFreeChart used / How to make histograms using JFreeChart?
• human neuron
  • dendrite / Introduction to neural networks
  • cell body / Introduction to neural networks
  • axom terminal / Introduction to neural networks
• hyperplane / Scatter plots, What is simple linear regression?

I

• Impala
  • used, for real-time SQL queries / Real-time SQL queries using Impala
  • advantages / Real-time SQL queries using Impala
  • flight delay analysis / Flight delay analysis using Impala
  • Apache Kafka / Apache Kafka
  • Spark Streaming / Spark Streaming, Typical uses of Spark Streaming
  • trending videos / Trending videos
• Iris dataset
  • reference link / Flower species classification using multi-Layer perceptrons
• IVTK Graph toolkit
  • about / IVTK Graph toolkit
  • other libraries / Other libraries

J

• JFreeChart API
  • dataset loading, Apache Spark used / Simple single Time Series chart
  • chart object, creating / Simple single Time Series chart
  • dataset object, filling / Bar charts
  • chart component, creating / Bar charts

K

• k-means clustering
  • bisecting / Bisecting k-means clustering
• K-means clustering / K-means clustering

L

• linear regression
  • about / Linear regression
  • using / Where is linear regression used?
  • used, for predicting house prices / Predicting house prices using linear regression
  • dataset / Dataset
• line charts / Line charts
• logistic regression
  • about / Logistic regression
  • mathematical functions, used / Which mathematical functions does logistic regression use?
  • Gradient ascent or descent / Which mathematical functions does logistic regression use?
  • Stochastic gradient descent / Which mathematical functions does logistic regression use?
  • used for / Where is logistic regression used?
  • heart disease, predicting / Where is logistic regression used?
  • dataset / Dataset

M

• machine learning
  • about / What is machine learning?
  • example / Real-life examples of machine learning
  • at Netflix / Real-life examples of machine learning
  • spam filter / Real-life examples of machine learning
  • Hand writing detection, on cheque submitted via ATMs / Real-life examples of machine learning
  • type / Type of machine learning
  • supervised learning / Type of machine learning
  • un-supervised learning / Type of machine learning
  • semi supervised learning / Type of machine learning
  • supervised learning, case study / A small sample case study of supervised and unsupervised learning
  • unsupervised learning, case study / A small sample case study of supervised and unsupervised learning
  • issues / Steps for machine learning problems
  • model, selecting / Choosing the machine learning model
  • training/test set / Choosing the machine learning model
  • cross validation / Choosing the machine learning model
  • features extracted from datasets / What are the feature types that can be extracted from the datasets?
  • categorical features / What are the feature types that can be extracted from the datasets?
  • numerical features / What are the feature types that can be extracted from the datasets?
  • text features / What are the feature types that can be extracted from the datasets?
  • features, selecting to train models / How do you select the best features to train your models?
  • analytics, executing on big data / How do you run machine learning analytics on big data?
  • data, preparing in Hadoop / Getting and preparing data in Hadoop
  • data, obtaining in Hadoop / Getting and preparing data in Hadoop
  • models, storing on big data / Training and storing models on big data
  • models, training on big data / Training and storing models on big data
  • Apache Spark machine learning API / Apache Spark machine learning API
• massive graphs
  • on big data / Massive graphs on big data
  • graph analytics / Graph analytics
  • graph analytics, on airports / Graph analytics on airports and their flights
• maths stats
  • min / Box plots
  • max / Box plots
  • mean / Box plots
  • median / Box plots
  • lower quartile / Box plots
  • upper quartile / Box plots
  • outliers / Box plots
• mean squared error (MSE) / Bisecting k-means clustering
• median value / Box plots
• MNIST database
  • reference link / Hand written digit recognizition using CNN
• model
  • selecting / Training and storing models on big data
  • training / Training and storing models on big data, Training and testing the model
  • storing / Training and storing models on big data
  • testing / Training and testing the model
• multi-Layer perceptron
  • used, for flower species classification / Flower species classification using multi-Layer perceptrons
• multi-layer perceptron
  • about / Multi-layer perceptrons
  • accuracy / Accuracy of multi-layer perceptrons
• multiple linear regression / What is simple linear regression?

N

• N-grams
  • about / N-grams
  • examples / N-grams
• NameNode / Main components of HDFS
• Natural Language Processing (NLP) / What are the feature types that can be extracted from the datasets?, Concepts for sentimental analysis
• Naïve bayes algorithm
  • about / Naive Bayes algorithm
  • advantages / Advantages of Naive Bayes
  • disadvantages / Disadvantages of Naive Bayes
• neural networks / Introduction to neural networks

O

• OpenFlights airports database
  • reference link / Datasets

P

• paired RDDs
  • about / Paired RDDs
  • transformations / Transformations on paired RDDs
• perceptron
  • about / Perceptron
  • issues / Problems with perceptrons
  • Logical AND / Problems with perceptrons
  • Logical OR / Problems with perceptrons
  • sigmoid neuron / Sigmoid neuron
  • multi-layer perceptron / Multi-layer perceptrons
• PFP / Running FP-Growth on Apache Spark
• prefuse
  • about / Prefuse
  • reference link / Prefuse

R

• random forest / Random forests
• real-time analytics
  • about / Real-time analytics
  • fraud analytics / Real-time analytics
  • sensor data analysis (Internet of Things) / Real-time analytics
  • recommendations, giving to users / Real-time analytics
  • in healthcare / Real-time analytics
  • ad-processing / Real-time analytics
  • big data stack / Big data stack for real-time analytics
• real-time data ingestion / Real-time data ingestion and storage
  • Apache Kafka / Real-time data ingestion and storage
  • Apache Flume / Real-time data ingestion and storage
  • HBase / Real-time data ingestion and storage
  • Cassandra / Real-time data ingestion and storage
• real-time data processing / Real-time data processing
  • Spark Streaming / Real-time data processing
  • Storm / Real-time data processing
• real-time SQL queries
  • on big data / Real-time SQL queries on big data
  • impala / Real-time SQL queries on big data
  • Apache Drill / Real-time SQL queries on big data
  • Impala, used / Real-time SQL queries using Impala
• real-time storage / Real-time data ingestion and storage
• Recency, Frequency, and Monetary (RFM) / Customer segmentation
• recommendation system
  • about / Recommendation systems and their types
  • types / Recommendation systems and their types
  • content-based recommendation systems / Content-based recommendation systems
• Resilient Distributed Dataset (RDD) / Concepts, Dataframe and datasets

S

• scatter plots / Scatter plots
• sentimental analysis
  • about / Sentimental analysis
  • concepts / Concepts for sentimental analysis
  • tokenization / Tokenization
  • stemming / Stemming
  • N-grams / N-grams
  • term presence / Term presence and Term Frequency
  • term frequency / Term presence and Term Frequency
  • Term Frequency and Inverse Document Frequency (TF-IDF) / TF-IDF
  • bag of words / Bag of words
  • dataset / Dataset
  • text data, data exploration / Data exploration of text data
  • on dataset / Sentimental analysis on this dataset
• sigmoid neuron / Sigmoid neuron
• simple linear regression / Linear regression, What is simple linear regression?
• smoothing factor / Disadvantages of Naive Bayes
• SOLR / Real-time data processing
• SPAM Detector Model / Type of machine learning
• SparkConf
  • building / Building SparkConf and context
• Spark ML / Apache Spark machine learning API
• Spark SQL
  • used, for basic analysis on data / Basic analysis of data with Spark SQL
  • SparkConf, building / Building SparkConf and context
  • context, building / Building SparkConf and context
  • dataframe / Dataframe and datasets
  • datasets / Dataframe and datasets
  • data, loading / Load and parse data
  • data, parsing / Load and parse data
• Spark Streaming
  • about / Spark Streaming, Typical uses of Spark Streaming
  • use cases / Typical uses of Spark Streaming
  • data collection, in real time / Typical uses of Spark Streaming
  • storage, in real time / Typical uses of Spark Streaming
  • predictive analytics, in real time / Typical uses of Spark Streaming
  • windowed calculations / Typical uses of Spark Streaming
  • cumulative calculations / Typical uses of Spark Streaming
  • base project setup / Base project setup
• stemming / Stemming
• stop words removal / Stop words removal
• Storm / Spark Streaming
• sum of mean squared errors (SMEs) / Bisecting k-means clustering
• supervised learning
  • about / Type of machine learning
  • classification / Type of machine learning
  • regression / Type of machine learning
• Support Vector Machine (SVM) / SVM or Support Vector Machine

T

• tendency / Content-based recommendation systems
• term frequency
  • about / Term presence and Term Frequency
  • example / Term presence and Term Frequency
• Term Frequency and Inverse Document Frequency (TF-IDF) / TF-IDF
  • about / TF-IDF
  • term frequency / TF-IDF
  • inverse document frequency / TF-IDF
• TimeSeries chart
  • about / Time Series chart
  • all india seasonal / All India seasonal and annual average temperature series dataset
  • annual average temperature series dataset / All India seasonal and annual average temperature series dataset
  • simple single TimeSeries chart / Simple single Time Series chart
  • multiple TimeSeries, on single chart window / Multiple Time Series on a single chart window
• tokenization
  • about / Tokenization
  • regular expression, used / Tokenization
  • pre-trained model, used / Tokenization
  • stop words removal / Stop words removal
• trending videos
  • about / Trending videos
  • sentiment analysis, at real time / Sentiment analysis in real time

V

• vertexes / Refresher on graphs
• Visualization ToolKit (VTK)
  • about / IVTK Graph toolkit
  • URL / IVTK Graph toolkit

W

• windowed calculations / Trending videos