Innovation for Service value Networks

Cheng Hsu

Professor, RPI

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

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

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

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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Mini Metadatabase

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”.