Databases Reference
In-Depth Information
If we can classify the data into a set of hierarchies, we can determine whether
a particular data belongs to a set or not. This would be considered a nominal
analysis. If we have an established hierarchy, we can deduce the set membership
for higher levels of the hierarchy.
In ordinal analysis, we can compare two data items. We can deduce whether
a data is better, higher, or smaller than another based on comparative algebra
available to ordinal analytics. Sentiment analysis is one such comparison. For
example, let us take a statement we analyzed from a customer complaint.
“Before 12 days, I was recharged my Data Card with XXX Plan. But i am
still not able to connect via internet. I have made twise complain. But all
was in vain. The contact number on Contact Us page is wrong, no one is
picking up. I have made call to customer care but every guy telling me...”
As humans, it is obvious to us that the sentiment of this sentence is negative.
However, Big Data requires sentiment analysis on terabytes of data, which means
we need to assign a positive or negative sentiment using a computer program.
Use of words or phrases such as “I am not able,” “complain,” “in vain,” and “no
one is picking up” are examples of negative sentiments. A sentiment lexicon can
be used as a library to compare words against known “positive” or “negative”
sentiment. A count of the number of negative sentiments is qualitative analytics
that can be performed on sentiment data, as we can differentiate between positive
and negative sentiment and conclude that positive sentiment is better than
negative sentiment. We can also create qualiiers such as “strong” sentiment
and “weak” sentiment and compare the two sets of comments.
In typical interval scaled data, we can assign relative values to data but may
not have a point of origin. As a result, we can compute differences and deduce
that the difference between two data items is higher than another set of data
items. For example, a strong positive sentiment may be better than weak positive
sentiment. However, these two data items are more similar than the pair of a
strong positive and a strong negative sentiment.
Focus on Speciic Time Slice or Using Other Dimensions
Data Warehouses are at the receiving end of a large number of transactions. The
source data is typically created by a series of (mainframe) applications, which are
connected together in a food chain where the output of one program became the
input of the next. I studied a data accumulation process where the inancial organ-
ization was the recipient of these cascading transactions, and the organization
needed to balance the topics in a short time. If the transactions failed in source
systems, the inancial reports were likely to be delayed. They were tasked with
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