Abstract—Recent advancements in digital technologies (DTs)
have fundamentally enabled collectives to collaborate on
analyzing, disseminating, and leveraging the data for many
enterprise-wide applications. Real-time access to changing
characteristics of analytical information is critical for an
enterprise to run a competitive business and respond to a
dynamics of marketplace. Enterprises are predominantly
recognizing that business processes (BPs) are the driving force
in developing new innovations and competitive strategies to
enable appropriate Big Data Architecture Patterns (BDAPs)
such that the critical and sustainable factors are in the
existence of entities. The integration of BDAPs with BPs can
become a centralized instrument for enterprises in accurately
construe business scenarios. The generic BDAPs are immature
and requires a vast amount of BP performance data in order
to support a valuable analysis with highest level of granularity.
Existing alliances between BDAPs and BP lifecycle are
daunting to rationalize characteristics of a real-time enterprise.
In this paper, we recognize that as enterprises become more
BDAP driven, it’s only natural that those insights find their
way into the BPs that can place them into action. We provide
an approach to derive and customize BDAP for specific BP
association to timely delivery of the required information and
regardless of the underlying concerns of the DTs. We illustrate
the ability to accustom the solution that sustains real-time
capabilities of an enterprise in presence of anticipated BPs’
performance objectives utilizing BDAPs.
Index Terms—Big data architecture patterns (BDAPs),
business processes (BPs), digital technologies (DTs), degree of
coverage (DoC), real-time enterprises (REs).
Vikas S. Shah is with Connect Enterprise Services Consulting, Wipro
Technologies, East Brunswick, NJ, USA (e-mail: vikas.shah@wipro.com).
[PDF]
Cite: Vikas S. Shah, "An Integrated Framework to Quantify Strategic
Diversifications in Real-Time Enterprise Industrializing
Alliances of Big Data Architecture," International Journal of Knowledge Engineering vol. 3, no. 1, pp. 17-24 , 2017.
