Cheng Hsu

1. Innovation for Service value Networks
Cheng Hsu
Professor, RPI

2. Types: Internet e-Commerce Networks, Peer-to-Peer
Service/Collaboration networks, Social Networks, Enterprise
(Professionals) Networks, etc.
Examples: e-Bay, healthcare support, Facebook, intranets…
Innovations: e-marketing (customer recommendation,
business chaining, etc.), group activities and special interests,
on-demand business and collaboration…
Technology: data integration, network analysis, clustering
and statistics, personal tasks profiling…
Smartness: Network-Based intelligence; i.e., population
knowledge and personalization application

3. Principle One: Building the Big Data
Integration of person-centered data along the life cycle
of personal tasks and growth from all pertinent sources
Principle Two: Personalizing the Big Data for services
Development of personal service-oriented massive
analytics to support the conduct of the personal life
cycle tasks (Motto: service is the best selling)
Smart Service Value Networks: possessing the ability to
self-develop the Big Data and Massive Analytics for
constantly evolving applications – the innovation

4. Theory One: Scaling the connections up to cover the
entire population (business domain) – Big Data
Theory Two: Scaling the connections down to serve each
person (individuals of the network) – service analytics
Theory Three: Scaling the connections with network
transformation (hyper-networking) – business innovation
All for One and One for All: a moral proposition may be an
ultimate business value proposition – this is the golden rule
for building Big Data and deriving Massive Analytics

5. 1. An ontology and metadata repository for data
integration – the global information resources dictionary
2. An architecture for non-intrusive integration of
massively distributed (Internet) heterogeneous data
sources - the Metadatabase model
3. A core logic for predictive e-marketing analytics (e.g.,
the well-known customer recommendation algorithms
at some e-commerce sites)
A technology platform for developing the Big Data and
Massive Analytics can facilitate service innovation

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8. Similar Customers: 1. determine a set of defining attributes
for “similarity”; 2: compute the similarity indicator, e.g., S-C(i)
= ∑ w(j)a(j) for each customer i, and then group customers
based on this indicator; 3: recommend the additional
products that the similar customers prefer most
Similar Products: use the same logic to develop a basic
algorithm for using similar products S-P(i)
Similar Behaviors: use the same logic to develop an
algorithm from customers-products networking (compute
e.g., rating/purchase indicators and regress them on
attributes, by sub-groups)
Adaptability can be built into the logic to make it “smart”.