Predictive Analytics

Predictive analytics requires structured data in general, so with this in mind. How does graph representation and graph analytics help with the unstructured data before or along with building predictive modeling? what are the trending Graph Database tools? What are the processing challenges with Graph databases compared to structured databases?

Graph representation and graph analytics is a huge help for that first step of understanding unstructured data. Unstructured data is information that isn’t arranged yet by patterns, trends, or any data model. Graph representations allows us to get a visual and draw initial conclusions and further predictions about the data we have in front of us.

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Oracle, Neo4j, and Mark Logic are a couple trending graph database tools. We explored Neo4j a little bit in our previous course and it was neat to be able to see relationships and groups in the data we have.

In a graph database, the relationship among parts of our data are stored in the individual record level. Structured databases on the other hand uses these predefined structures to hold it all together. No standardized language is one of the biggest struggles. The other main issue is that sometimes graphs aren’t the best way to show and interpret things.

Graph databases allow a flexible schema or unstructured data to have linkages to different parts of the data. With easy expression of entities and relationships between data, graph databases make it easier for programmers, users and machines to understand the data and find insights. This deeper level of understanding is vital for successful machine learning initiatives, where context-based machine learning is becoming important for feature engineering, machine-based reasoning and inferencing.

What are the trending Graph Database tools?

The top ten graph database tools are listed below from this website (Links to an external site.).

Neo4j, ArangoDB, Amazon Neptune, Dgraph, DataStax,OrientDB, FlockDB, Cassandra and Titan.

What are the processing challenges with Graph databases compared to structured databases?

Specialty graph data engineers are needed to process and model the data
Maintaining the consistency and relationships of the data
Writing graph data queries
Every query you write is not re-usable. If you have 10 questions you want answered from the data you have to write ten different queries

Graph analytics is actually built to analyze both structured and unstructured data.
As the name suggests, graph analytics is a graph model which represents entities and relationships. We see unstructured data in social media, mobile, the Internet, and the huge volume that needs to be processed in a fraction of seconds. This makes it difficult to make the data structured (like relational data) and define the proper semantic layer.

Using a graph database here like NoSQL makes it more accessible, and dynamic with a low cost for new data to be integrated into the existing system. It has the capability to process and retrieve a high volume of data from multiple sources. Due to high flexibility, it becomes more accessible to interlink data, derives meaningful analysis, and make better decisions. (Link (Links to an external site.))

Some of the trending Graph database tools are : (Link (Links to an external site.))

Neo4j: This we have covered in detail in the previous courses.
AWS Neptune
Graph QL

Since we are dealing with highly interconnected data of both structured and unstructured with any volume we face the below problems (Link (Links to an external site.))
1. To Maintain consistency of data
2. Modelling the interconnected data
3. Writing the graph query
From my previous program I knew about knowledge graphs and deep learning but I was unaware of graph analytics, which is similar to knowledge graphs. Knowledge graphs are three dimensional graphs with for example text graphs words are along two axes and the third axis provides the distance in meaning between the words. These graphs along with convolutional neural network models were used to develop original images and text based on data seeds (Chollet, 2018). The concept of utilizing graph analytics with predictive modeling is a similar methodology. The graph analytics can provide degree centrality, shortest or longest path, community Ids and pagerank results which are similar to the knowledge graph distance between word meanings and clustering algorithms.

The only Graph Database tool I have used is Neo4j. I have heard of Oracle Spatial and Graph. The following article provides ranking of current graph databases (Links to an external site.). Their top ones are ArangoDB, Neo4j, OrientDB, and AllegroGraph (Predictive Analytics Today, 2022).

With my limited experience, the issues with graph databases are that they are resource intensive and consume a significant amount of time to run complex queries. For one of my queries, I needed 64 gigabytes of RAM and a week to run the code. However, their syntax for complicated queries is easier than using PL/SQL with cursor code. PL/SQL code that is tens of lines is just a couple of lines in Neo4j, but the tradeoff is the time to run the complex query.

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