Cheryl A. Kieliszewski

Handbook of Service Science

As the service sector expands into the global economy, a new science of service is emerging, one that is dedicated to encouraging service innovation by applying scientific understanding, engineering discipline, and management practice to designing, improving, and scaling service systems. Handbook of Service Science takes the first major steps to clarifying the definition, role, and future of this nascent field. Incorporating work by scholars from across the spectrum of service research, the volume presents multidisciplinary perspectives on the nature and theory of service, on current research and practice in design, operations, delivery, and innovation of service, and on future opportunities and potential of service research. Handbook of Service Science provides a comprehensive reference suitable for a wide-reaching audience including researchers, practitioners, managers, and students who aspire to learn about or to create a deeper scientific foundation for service design and engineering, service experience and marketing, and service management and innovation.

Evolution of storage management: transforming raw data into information

Exponential growth in storage requirements and an increasing number of heterogeneous devices and application policies are making enterprise storage management a nightmare for administrators. Back-of-the-envelope calculations, rules of thumb, and manual correlation of individual device data are too error prone for the day-to-day administrative tasks of resource provisioning, problem determination, performance management, and impact analysis. Storage management tools have evolved over the past several years from standardizing the data reported by storage subsystems to providing intelligent planners. In this paper, we describe that evolution in the context of the IBM Total Storage® Productivity Center (TPC)--a suite of tools to assist administrators in the day-to-day tasks of monitoring, configuring, provisioning, managing change, analyzing configuration, managing performance, and determining problems. We describe our ongoing research to develop ways to simplify and automate these tasks by applying advanced analytics on the performance statistics and raw configuration and event data collected by TPC using the popular Storage Management Initiative-Specification (SMI-S). In addition, we provide details of SMART (storage management analytics and reasoning technology) as a library that provides a collection of data-aggregation functions and optimization algorithms.

SIMPLE: A Strategic Information Mining Platform for Licensing and Execution

Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect a business from its competition, but also generate significant opportunities in licensing, execution, long term research and innovation. In certain industries, e. g., Pharmaceutical industry, patents lead to multi-billion dollar revenue per year. In this paper, we present a holistic information mining solution, called SIMPLE, which mines large corpus of patents and scientific literature for insights. Unlike much prior work that deals with specific aspects of analytics, SIMPLE is an integrated and end-to-end IP analytics solution which addresses a wide range of challenges in patent analytics such as the data complexity, scale, and nomenclature issues. It encompasses techniques for patent data processing and modeling, analytics algorithms, web interface and web services for analytics service delivery and end-user interaction. We use real-world case studies to demonstrate the effectiveness of SIMPLE.

Data for All: A Systems Approach to Accelerate the Path from Data to Insight

Zettabytes of data are available to be harvested for competitive business advantage, sound government policies, and new insights in a broad array of applications. Yet, most of this data is inaccessible for users, since current data analysis tools require an army of technical people to find, transform, analyze, and visualize data in order to make it consumable for decision making. In this paper, we present work in progress to lower the barriers for data-driven decision making by introducing a systems approach to scale the user experience, not only in the volume and variety of data, but also in the skills required to harvest that data. We call for a new approach for data-intensive applications that engages the user as an intelligent partner in a social and intelligent conversation with data by automating, guiding, and recommending data, transformations, visualizations, analytics, and suggesting collaboration opportunities within an analytics marketplace, and leverages both metadata and semantic information about the data captured from conversations.

Information technology for healthcare transformation

Rising costs, decreasing quality of care, diminishing productivity, and increasing complexity have all contributed to the present state of the healthcare industry. The interactions between payers (e.g., insurance companies and health plans) and providers (e.g., hospitals and laboratories) are growing and are becoming more complicated. The constant upsurge in and enhanced complexity of diagnostic and treatment information has made the clinical decision-making process more difficult. Medical transaction charges are greater than ever. Population-specific financial requirements are increasing the economic burden on the entire system. Medical insurance and identity theft frauds are on the rise. The current lack of comparative cost analytics hampers systematic efficiency. Redundant and unnecessary interventions add to medical expenditures that add no value. Contemporary payment models are antithetic to outcome-driven medicine. The rate of medical errors and mistakes is high. Slow inefficient processes and the lack of best practice support for care delivery do not create productive settings. Information technology has an important role to play in approaching these problems. This paper describes IBM Researchs approach to helping address these issues, i.e., the evidence-based healthcare platform.

Analytical Pathway Methodology: Simplifying Business Intelligence Consulting

The implementation and use of advanced analytics to identify patterns and discover the non-obvious from structured and unstructured data sources to improve business performance or establish a competitive advantage is becoming more commonplace in enterprises. Delivering complex advanced analytical systems to mine unstructured data sources requires a complex decision-making process by the business analytics user to perform an analysis. In general, analytics is a process that often times requires continual iteration of the content sources(s) to locate, examine and interpret information to address an issue or hypothesis. This problem led us to explore a method to communicate, and eventually automate, an analytical pathway to be implemented with an analytical services system. In this paper we describe some of the challenges in the use of complex advanced analytics for business intelligence, an initial breakdown of the process and an approach to bringing repeatability to the analysis process.

A Service Practice Approach

In the practice of designing and engineering business systems, work is often defined and represented by a series of activities comprised of discrete tasks performed in a prescribed sequence, within a particular timeframe and set in the context of a particular technology. These elements are often reduced to a set of controlled system inputs and outputs, ignoring the complex interactions that need to be supported in highly collaborative work systems endemic of service systems . It is our position that designing and engineering service -based systems requires a new approach to understanding the interactions between the people, information technology and activities needed to enable services. We have approached service system design from the perspective of investigating and understanding work practices as the basis for system innovation . As such, our focus is on understanding what people actually do in practice, including their use of information, tools, methods and the relationships amongst these elements. This paper describes a practice-based approach for investigating work in service organizations. We argue for a need to understand work from the practice perspective, describe our practice-based approach, present a new way to represent work using practice diagrams, provide a case study as an example of our approach and make recommendations for future research.

A visualization solution for the analysis and identification of workforce expertise

Keeping sight of the enterprises workforce strengthens the entire business by helping to avoid poor decision-making and lowering the risk of failure in problem-solving. It is critical for large-scale, global enterprises to have capabilities to quickly identify subject matter experts (SMEs) to staff teams or to resolve domain-specific problems. This requires timely understanding of the kinds of experience and expertise of the people in the firm for any given set of skills. Fortunately, a large portion of the information that is needed to identify SMEs and knowledge communities is embedded in many structured and unstructured data sources. Mining and understanding this information requires non-linear processes to interact with automated tools; along with visualizations of different interrelated data to enable exploration and discovery. This paper describes a visualization solution coupled with an interactive information analytics technique to facilitate the discovery and identification of workforce experience and knowledge community capacity.

Scalability in system management GUIs: a designer's nightmare

As Information Technology (IT) advances, traditional concerns over performance are being overtaken by concerns over manageability and scalability in system management interfaces [1]. Designing effective interactions and representations of large complex systems with intricate relationships among components is a formidable challenge. In this paper we describe the design of a topology viewer application for enterprise-scale storage systems. A key issue in this design effort was to create a graphical topology viewer that would scale to the complexity of typical storage environments and support administrators effectively in various activities. Our approach to address these issues was to use semantic zooming and progressive information disclosure techniques extensively; thus essentially shifting the scalability challenge from purely visual design to mostly interaction design.

Using Digital Trace Analytics to Understand and Enhance Scientific Collaboration

Social interaction and idea flow have been shown to be important factors in the collaboration work of scientific and technical teams. This paper describes a study to investigate scientific team collaboration and activity through digital trace data. Using a 27-month electronic mail data corpus from a scientific research project, we analyze team member participation and topics of discussion as a proxy for interaction and idea flow. Our results illustrate the progression of participation and conversational themes over the project lifecycle. We identify temporal evolution of work activities, influential roles and formation of communities throughout the project, and conversational aspects in the project lifecycle. This work is the first step of a larger research program analyzing multiple sources of digital trace data to understand team activity through organic products and byproducts of work.