Creating functionality outside of what is provided by the built-in functions

Providing Functional access to user-defined data in HDFS

Providing advanced analytics to Hadoop

Providing an interface from Pig to Microsoft Excel for easier data manipulation

Provides and explanatory model

Distinguishes categorical from continuous variables

Support for unstructured data

Resiliency to complex, non-linear variable interactions

Unable to run map - > reduce execution plans

Supports applications written in Python, Java, and Scala

Less efficient processing small files than Hadoop MapReduce

Supports workflows that can return to previous work steps

2 based on the following reasoning:

TFIDF = TF1DF = 1 908

You then know that IDF will equal LOG (32)=0.954

Therefore, TFIDF=TF*0.954 = 1.908

TF will then round to 2

4 based on the following reasoning:

TFIDF = TF1DF = 1.908

You then know that IDF will equal LOG (3/1 )=0.477

Therefore, TFIDF=TF ' 0 477 = 1.908

TF will then round to 4

6 based on the following reasoning:

TFIDF = TF1DF = 1.908

You then know that IDF will equal 3/1=3

Therefore, TFIDF=TF/3 = 1.908

TF will then round to 6

11 based on the following reasoning:

TFIDF = TF1DF = 1908

You then know that IDF will equal LOG(3/2)=0.176

Therefore, TFIDF=TF " 0.176 = 1.908

TF will then round to 11

Visualization Planning

Data Preparation

Visualization Building

Discovery

Can be performed by calling the synset () function on a lemma in LNTK

Can be performed by calling the lemma() function on a synset in LNTK

Reduces words of variant forms to their base forms based on a set of heuristics

Reduces words of variant forms to their base forms based on a dictionary

Edge list

Adjacency matrix

Eigenvector centrality

Adjacency list

Discourse

Syntactic

Semantic

Acoustic

Mahout

Pig

Hive

HBase

Eigenvector centrality and betwenness

Clique size and total number of nodes in the network

Number of edges in the network and centrality measure of the cliques

Clique size and betweenness centrality