RESUME

QUALIFICATIONS
> 3 years of IT work experience in multiple fields: education, telecommunications, bank, and insurance.
> Solid C/C++ programming skills (Borland C++ Builder, STL and Visual C++)
> Solid RDBMS/SQL skills (SQL Server and Oracle)
> Hands-on business intelligence project experience (Cognos, Datastage, DTS, Brio, SPSS, and Crystal report)
> Hands-on skills and experience on whole SDLC.
PROFESSIONAL EXPERIENCE
Currently I am studying at the University of Technology, Sydney, as a PhD student. My major is computing science and research interests include business intelligence, data warehouse, decision support systems, executive support system, and data mining
> 8/2/2007 – 27/3/2007 Professional Advantage (North Sydney)
Position: BI systems Analyst
--Reviewing, devise and develop business intelligence focused routines, analyses systems and processes within the SunSystems team.
> 10/2004 - 5/2005 Brilliance Technology (Shanghai, China)
Position: Senior Software Engineer
--Led a team (6 people) on business intelligence projects.
--Worked as a major developer in two call center projects.
--Worked as a major developer in a bank credit cards system.
> 10/2003 - 08/2004 Continuous Technologies International (Guangzhou, China)
Position: Senior developer
--Took responsibility for analyzing, designing and implementing CRM (Customer Relationship Management) systems and Call Center systems.
> 07/2002 – 08/2003 Guangzhou Sunrise Electronic Development Ltd (Guangzhou, China)
Position: Software Engineer
--Developed large scale data warehouse for China Mobile Guangdong, responsible for ETL and data mining modules.

Publications

Conference Papers

1. Niu, L., J. Lu, and G. Zhang, Enriching executives' situation awareness and mental models: a conceptual ESS framework, in 9th International Conference on Enterprise Information Systems. 2007: Funchal, Madeira, Portugal.

2. Niu, L., J. Lu, and G. Zhang, Cognition-driven decision support system framework, in the First International Conference on Risk Analysis and Crisis Response 2007, Atlantis Press Shanghai, China.

3. Niu, L., et al., An exploratory cognitive business intelligence system, in the 2007 IEEE / WIC /ACM International Conference on Web Intelligence. 2007, IEEE: Silicon Valley, USA.

4. Niu, L., Lu, J. & Zhang, G. 2007, 'Cognitive orientation in business intelligence systems', in D. Ruan & F. Hardeman (eds), Intelligent Decision and Policy Making Support Systems, Springer.

5. Niu, L., J. Lu, and G. Zhang, Improved business intelligence analytics on manager’s experience, in the 2008 IEEE Congress on Evolutionary Computation (IEEE CEC 2008) 2008, IEEE: Hong Kong.

6. Niu, L. and G. Zhang, A model of cognition-driven decision process for business intelligence, in The 2008 IEE/WIC/ACM International Conference on Web Intelligence. 2008: Sydney, Australia.

Monograph

Niu, L. , J. Lu, and G. Zhang, Cognition-Driven Decision Support for Business Intelligence: A Model and Techniques, Springer Verlag, to be appeared Later 2009

Why is current business intelligence insufficient for decision making?

Business domain has the characteristics for which the theories of NDM, SA, and mental model can be applied. Today’s companies operate in a turbulent business environment where different sectors interact with and affect each other. Walters, Jiang et al (2003) summarize in six internal business environment sectors (market research, product R&D, basic engineering, financial management, cost controls, and operational efficiency) and six external ones (market, technological, competitive, political/legal, economic, and socio-cultural). For the survival of the company, the executive needs to keep aware of each sector of the environment. Moreover, the speed and quality with which business decisions must be made has increased substantially with the trend of economy globalization. The complexity, uncertainty, dynamics, and time pressure of today’s business decision making show the potential of applying NDM theory to support business management.

An executive need rich SA and mental models for the success of business management, particularly at strategic level. SA is about knowing what is going on around the decision-maker. In business domain, the executive’s SA can be regarded as the perceived and understood information about the company, e.g. statistics of sales, marketing, product development, and competitors. As such the executive has the ability to anticipate the future events, seek opportunities, and identify threats. SA creates a big picture of the company within the manager’s mind and enables the manager to be capable of predicting the future and making decisions. Mental models reflect the executive’s past management experience. They provide the executive with the ability to simplify the complexity of business environments (Porac & Thomas 1990; Schwenk 1984). Mintzberg (1973) categorizes executives’ work into ten different roles and connects them with managers’ mental models. He finds that managers spend most of their time communicating with other people and thinking, by which their mental models are built based on their past experience. With rich and solid mental models, the executive can envision possible future business scenarios that may cause problems or bring opportunities and then make appropriate strategies to respond.

From information systems perspective, cognitive orientation can be taken into account in order to enhance the functionality of traditional BI systems. As the most promising BI application, OLAP-based ad hoc query and reporting is mainly pre-defined information representation. In the decision process, the executives are provided with information in the form of report, ad hoc analysis, or some so called knowledge which is pulled from data warehouse according to pre-defined queries, such as SQL sentences and multidimensional expressions (MDX). The emphasis is the manipulation of large volumes of both internal and external company data in terms of technology, rather than supporting executives’ decision making from cognitive perspective. In this research, we focus on supporting the executive to develop and enrich SA and mental models, and then eventually cognitively support decision making through information systems techniques.

References:

Mintzberg 1973, The nature of managerial work, Harper & Row, New York.

Porac, J.F. & Thomas, H. 1990, 'Taxonomic mental models in competitor definition', Academy of Management Review, vol. 15, no. 2, pp. 224-240.

Schwenk, C.R. 1984, 'Cognitive simplification processes in strategic decision-making', Strategic Management Journal, vol. 5, no. 2, pp. 111-128.

Walters, B.A., Jiang, J.J. & Klein, G. 2003, 'Strategic information and strategic decision making: the EIS/CEO interface in smaller manufacturing companies ', Information & Management vol. 40, no. 6, pp. 487-495

The Architecture of Cognitive Business Intelligence System

The Big Picture (Reserach Framework)

Current research project: "Cognition-Driven Decision Process on Business Intelligence Systems"

This research is an attempt toward high degree of user-centered human-computer interaction for better decision making in complex situations with ill-defined goals, uncertainty, time pressure and high personal stake. The fundamental point of view on decision making, in this research, is that humans are superior to computers when handling unstructured problems. Consequently a decision (in complex situations) should be made by humans rather than a computational output of computer programs. Following this view, a framework of cognitive business intelligence (BI) systems is proposed based on cognitive consideration of human situation awareness (SA), mental models, and experience (case). Both human and computer are incorporated into this framework and become components of the unified decision-making system. Human cognition is represented as information object and used to drive the process of human-computer interaction and eventually facilitate the cognition-driven decision process. This research is grounded in BI area. The proposed decision process model and system framework are used to improve the performance of current BI systems, particularly the analytical functionality.

Cognitive Orientation in Decision Support Systems

Cognitive orientation has long been thought of as a very important consideration to decision support systems (DSS). Situation awareness (SA) and mental models, as two key concepts in cognitive psychology, are important for understanding ill-structured problems and making effective decisions. In many dynamic decision-making situations, especially those with high uncertainty, complexity and high personal stake, much research has proven that there is a close connection between SA, mental models and the performance of decision making: good SA and mental models are very likely to lead to good decisions and ultimately good performance. This research develops a conceptual information system framework for cognition-driven decision support system based on cognitive orientation, which is expected to be able to provide better decision support in ill-defined decision situations.