Yi-Min Chee

Agility of Enterprise Operations across Distributed Organizations: A Model of Cross Enterprise Collaboration

We discuss the need for agility of business operations in a collaborative services ecosystem of partners and providers, and present a new system architecture for cross collaboration among multiple service enterprises. We demonstrate the importance and inevitability of such collaboration along with challenges in its proper realization through several real-life examples taken from different business domains. We then show that these challenges are rooted in two key factors: unpredictability and responsiveness; agility enables optimal response to unpredictable events. The key contribution of this manuscript is the presentation of a new model, centered on the modeling of work-asa-service (WaaS) and an intelligent hub for coordinating cross enterprise collaboration. This hub is constructed in a manner intended to directly identify and solve the two key fundamental challenges of cross enterprise collaboration. As such, we expect it to outperform other means of collaboration across service providers. We demonstrate the potential for such performance using field examples.

Coordinating distributed operations

In this manuscript, we discuss the need for, and present a new system architecture for cross collaboration among multiple service enterprises. We demonstrate the importance and inevitability of such collaboration along with challenges in its proper realization through several real-life examples taken from different business domains. We then show that these challenge are rooted in two key factors: unpredictability and responsiveness. The key contribution of this manuscript is the presentation of a new model, centered on an intelligent hub, for coordinating the logistics of cross enterprise collaboration. This hub is constructed in a manner intended to directly identify and solve the two key fundamental challenges of cross enterprise collaboration. As such, we expect it to outperform other means of collaboration across service providers. We demonstrate the potential for such performance using field examples.

Enterprise oriented services

We describe a service-oriented framework that supports how distributed enterprises can collaborate on doing work. Our model separates the concerns of doing from managing work. Work is modeled as a capability and can be provided as a service by some organization. A desired business outcome can then be described by its required capabilities. The framework enables dynamic composition of capabilities into just-in-time service plans that can be executed collaboratively by distributed organizations. A Hub is used to manage and coordinate the overall work. It comprises stakeholders from the collaborating organizations. The Hubs infrastructure enables them to see the big picture, detect issues early, decide on the best response, and quickly enact their decision. Executing service plans can then be modified in real-time, providing the enterprise with agility and flexibility.

Allegro: A Metrics Framework for Globally Distributed Service Delivery

Globalization and specialization are growing trends in service organizations. Engineering and operating optimal structures within these service organizations requires coordinating the doing of service work, which in turn requires capturing and sharing information across localized teams. There is a need for a metrics framework, implemented in information technology, to make this possible. This paper describes the need for a metrics framework in global service delivery, properties of effective metrics frameworks, and Allegro, a metrics framework that is designed specifically to support globally distributed human intensive work, such as complex software development. Allegro is based on an abstract model of service delivery as a decomposition into a network of atomic service requests, each formalized according to a protocol we call Work-as-a-Service (WaaS). In this approach, Metrics do not only fulfill their traditional roles in driving quality and improvement. They also serve as configurable sensors essential to the effective coordination of a distributed service network.

Facilitating Business-Oriented Cloud Transformation Decision with Cloud Transformation Advisor

To move applications to the cloud is not only a technical decision but also a business-oriented decision, in which both business and technical factors (e.g. transformation effort, multi-tenancy and auto-scaling enablement, scalability and extensibility) should be considered. However, existing approaches and tools do not support a consumable business oriented cloud transformation decision to select more suitable transformation solution with the right cloud delivery model, services type, affordable transformation effort and etc. In this paper, we introduce a practical three-step approach and a tool, CTA (Cloud Transformation Advisor) to enable decision makers to identify the most suitable cloud transformation solution to satisfy their business goals based on a well-structured cloud transformation knowledge base.

What Code Implements Such Service? A Behavior Model Based Feature Location Approach

Enterprises today are keen to unlock new business values of their legacy services towards new trends (e.g., cloud and mobile). To accelerate such process, automatic feature location techniques can enable developers to rapidly locate/understand implementations of certain services (e.g., services to expose, transform or improve). Existing feature location techniques [1-3, 5-10, 32] provide a good foundation but have several key limitations: limited leverage of description sources, less considerations of internal behaviors, and ineffectiveness for the identification of service-relevant code entries. To address these limitations, we propose a behavior model based feature location approach and implement a tool named BMLocator. In the offline phase, BMLocator applies Natural Language Processing (NLP) techniques and static code analysis to extract “behavior models” of code units via considering multiple information sources. While in the online phase, given a service description, BMLocator first extracts its behavior model and then recommends service-relevant code units/entries by matching its behavior model with code units under analysis. Through evaluations with public service requests of open-source projects (e.g., Tomcat and Hadoop), we show that the approach is more effective in recommending service-relevant code entries (e.g., most of entries are prioritized as the first ones) than existing techniques (i.e., TopicXP[37], CVSSearch[6]).