About Graph Database, Healthcare/Life Sciences Impact on Graph Database

1. Sannapureddy Bhaskara Reddy, Senior Project Manager @Infosys, +91-7702577769
What is graph database

2. graph database
• Graph database, also called a graph-oriented database, is a type of NoSQL
database that uses graph theory to store, map and query relationships.
• Graph databases are well-suited for analyzing interconnections, which is why
there has been a lot of interest in using graph databases to mine data from social
media. Graph databases are also useful for working with data in business
disciplines that involve complex relationships and dynamic schema, such as
supply chain management, cargo transport and telecommunications.
• A graph database is essentially a collection of nodes and edges. Each node
represents an entity (such as a person) and each edge represents a connection or
relationship between two nodes. Each node in a graph database is defined by a
unique identifier, a set of outgoing edges and/or incoming edges and a set of
properties expressed as key/value pairs. Each edge is defined by a unique
identifier, a starting-place and/or ending-place node and a set of properties.
Continued…..

3. saMpLe graph
Continued…..

4. GRAPH DATABASE
• A graph database, also called a graph-oriented database, is a type
of NoSQL database that uses graph theory to store, map and query
relationships.
• A graph database is essentially a collection of nodes and edges. Each
node represents an entity (such as a person or business) and each edge
represents a connection or relationship between two nodes. Every node
in a graph database is defined by a unique identifier, a set of outgoing
edges and/or incoming edges and a set of properties expressed
as key/value pairs. Each edge is defined by a unique identifier, a
starting-place and/or ending-place node and a set of properties.

5. GRAPH DATABASES ImPAcT on
HEAlTHcARE SEcToR
• Healthcare sector startups are ripe for
exploiting NoSQL graph databases. With a
data model predicated on nodes/vertices and
relationships/edges, graph databases provide
a sturdy means to probe connections
between entities, especially the farther
removed from each other they are.
Continued…..

6. • Life sciences and Big Data analytics platform companies (health-focused startups ) leveraging graph
database technology as one important component of its service offerings. The companies takes data in a
variety of forms from some 3500 sources – including public sources such as ClinicalTrials.gov and PubMed,
as well as private data from partners and from customers’ own internal systems – to help pharmaceuticals
and medical device companies understand and segment their target markets within a hierarchy or ontology
of predefined categories, such as who publishes the most research in a certain area and who has formal
leadership positions in particular fields.
• Companies combines all the requisite data – regardless of its structure or even if it has no structure at all –
to deliver to its customers a single and unified profile of doctors and hospitals that are important to them.
It can drive these conclusions based on the connections its products can make and the insights they can
draw across data of any kind, without ever having to predefine data structures, thanks to their being built
atop Neo4j’s open source graph database.
• Company Health’s use cases that take advantage of graph database technology range across four life
sciences quadrants: medical affairs, sales and marketing, payers and clinical development.
• “The graph provides the search layer, that which handles the interconnections between disparate pieces of
data and lets business users interact with it in a meaningful way,” .

7. Business ApplicAtions
of GrAph DAtABAses in
life sciences
• The vendor’s medical solutions focuses on helping pharma companies find the right thought-leader doctors to talk to related to
the development or marketing of a drug, based on querying data represented in a graph model to understand overlaps across
their patient populations, treatment preferences, influence network, and so on. Its sales and marketing product aims to help
teams in these areas understand which medical accounts to target for the adoption of a medical device or application, with
greater relationships insight into instance rates of treatment or referrals by doctors associated with a particular condition.
• Selection criteria for conducting clinical trials, after all, potentially should factor in whether there is a strong intersection among a
patient population dealing with the disease being investigated, a hospital known for focusing on that issue, and a key opinion
leader or doctor influential in the treatment of that condition. In a graph database, a complex query to help find the optimal site
for a clinical trial – where the results set will come from the connectivity of many different data elements whose relationship to
each other is as important as the items themselves – will execute via a high-performance traversal of the various
nodes/relationships that comply with the request:
• “As long as you can structure data in a reliable and predictable way – as long as you know what data you’ll get upfront –
traditional database solutions work,” . “But they don’t work because we are interested in variety, and we needed a way to get any
data, with no precognition of what it is, and bring it into the system and store it. Where the graph plays is that we can search it in
an efficient way without knowing what data it is.”

8. GrAph DAtABAse AnD
GrAph AnAlytics
references
• Graph Database
(http://en.wikipedia.org/wiki/Graph_database)
– Native Graph Database (Neo4j)
– Pregel/Giraph (Distributed Graph Processing
Engine)
• Neo4j/Titan/GraphLab/GraphSQL

9. thAnK you

10. THANK YOU