Embedded DMAIC Methodology into Financial Knowledge Management System

Yi-Chuan Lu1, Hilary Cheng2, Calvin Sheu3

1Department of Information Management, Yuan Ze University, Chung-Li 320, Taiwan 2College of Management, Yuan Ze University, Chung-Li 320, Taiwan

3Department of Information Management, Tungnan University, New Taipei City 222, Taiwan

Abstract - We present a “define, measure, analyze, improve, and control” (DMAIC) methodology to model the knowledge discovery process for BI applications, specifically in statistical analysis and data mining. We implemented the DMAIC methodology embedded into the existed Financial Knowledge Management System (FKMS), which can be used as an effective means to dramatically improve knowledge product and process quality. It is meant for the achievement of total financial industry power users’ satisfaction by producing near defect-free financial knowledge. We have emphasized the knowledge engineering perspective for developing knowledge sets into historic reference decision with regard to formed knowledge management and sharing architecture in the knowledge discovery process in addition to the importance of leveraging structures and DMAIC methodology in design as well as implementation. The resulting knowledge from each experiment defined as a knowledge set consisting of strings of data, model, parameters, and reports are stored, shared, disseminated, and thus made helpful to support decision making. We finally illustrate the above claims with a process of applying data mining techniques to support corporate bonds classification.

Keywords: DMAIC, financial knowledge management system

1 Introduction One of the biggest challenges that most security investment institutions experienced was the lack of an intelligent data mining system to support investment researches decisions. The problems their system encountered included the following (Cheng, Lu, Wang, & Sheu, 2004): Though data for financial applications are simple data, the data typically include time series information, and the relationships among the financial instruments are complex. For example, consider a derivative security objects. The derivative security object often shares underlying securities with other derivatives. Underlying securities can come from many classes of instruments, from a simple currency to an interest rate swap to a hedge. As the securities become more complex, the data management and knowledge discovery problems become more difficult. Consider a security portfolio. The portfolio construction is a process of

quantitative analysis over massive amounts of data, and the Data Cube and ad-hoc analysis techniques are invisible solutions to support this process. We present the concept of FKMS, which is a prototype of a KM environment specifically for financial research purposes. The environment generates groups of knowledge sets with strings of data, models, parameters, and reports and tracking of the lifecycle of knowledge set creation among build, use, maintenance, and desertion. The ontology design of knowledge management and knowledge sharing is presented. Finally, a realization of decision support and knowledge sharing processes is illustrated. With FKMS, knowledge workers can freely extract sets of financial and economic data, analyze data with different decision support modules, rerun experiments with different sets of parameters, and finally disseminate value-added information (knowledge) through middleware or the Internet to remote clients. Not to mention that the knowledge generated is being collected, classified, and shared with colleagues, and thus it is well archived into corporate business intelligence databank. The remainder of this paper proceeds as follows. Section 2 introduces a perspective of knowledge engineering in the knowledge discovery process. Section 3 presents the DMAIC methodology. Finally, in Section 4 we conclude this paper.

2 The Perspective of Knowledge Engineering in the Knowledge Discovery Process

The development of knowledge representation is considered complex, and both the domain knowledge defined and the implemented feasible are subject to considerable uncertainty. The tendency exists therefore to focus knowledge discovery process modeling development from the knowledge management and sharing architecture to the domain knowledge form of enterprise production, which is logically described as the knowledge factory. As mentioned in Lu and Cheng (2003), a successful knowledge management system enhances the way people work together and enables knowledge workers and partners to share information easily so they can build on each other's ideas and work more effectively and efficiently. The goal is to gather company proprietary knowledge to come up with the best decision making or to quickly seize the initiative with innovative ideas.

To optimize the flow of information and interaction among knowledge workers so that the company can always make better trading or investment decisions, the specific group of data and modeling results should be administered and properly shared. Future knowledge can be generated by capturing existing (shared) knowledge via filtering, storing, retrieving, and disseminating explicit knowledge and by creating and testing new knowledge (Nemati, Steiger, Iyer, & Herschel, 2002).

According to the knowledge discovery process defined by Fayyad et al. (1996), as shown Fig. 1, the flow of data and shared information proceeds as follows: users select data with different criterion to perform quant analysis. The data retrieved will automatically be saved as data cubes. Each data cube records the time this file has been created, the screening rules, the data item names and types, etc., so that colleagues can easily share and exchange their knowledge and value-added information with others. In the meantime, the company will keep good management of each individual worker’s knowledge and so will be ready for good customer relationship management. However, this traditional knowledge discovery process is based on the prospect of problem solving (McDermott, 1988; Decker, Daniel, Erdmann, & Studer, 1997) in database. These knowledge engineering activities are quite unique in contrast to those from other types of processes, such as manufacturing. From the perspective of knowledge engineering, the knowledge discovery process is a transfer process (Hayes-Roth, Waterman, & Lenat, 1983) and a modeling process (Clancey, 1989; Morik, 1990). So, unlike a manufacturing process, a completed knowledge discovery process is non-repetitive. Each knowledge discovery instance invokes a different set of inputs and outputs. The higher levels of cognition increase the process variance. To further explain this, consider the following example (see Fig. 2)

Based on the input set X={X1, X2, .., Xn}, a set of knowledge discovery processes is P ={P1, P2, ..., Pn} that was invoked by knowledge workers {A, B, C} and then produced knowledge sets K ={K1, K2, .., Kn}. So, the formula of knowledge sets is K=P(X). Much of the statistical analysis was done based on the assumption of a stable process. Due to the uniqueness of the knowledge discovery process, the collection of individual instances of the knowledge discovery processes is no longer stable. Since process capability represents the full range of normal process variations for a given characteristic, it makes little sense to speak of process capabilities in the knowledge discovery process.

3 DMAIC Methodology We know the business process design and modeling technologies are being increasingly used by both traditional and newly formed ecommerce’s enterprises in order to improve the quality and efficiency of their administrative and production processes. From a process modeling and automation perspective, this has several implications; for example, the business processes should be correctly designed, their execution should be supported by a system platform that can fit the knowledge worker’s requirements, and the process resources should be able to carry out their work in a timely change. We applying DMAIC methodology to optimize knowledge management and sharing architecture that supports business and IT users in managing process implementation quality. FKMS can support full modeling tool suite in the business process, since it is based on the application of business intelligence techniques to business processes. In fact, FKMSTM store many types of incident that occur during process executions, including the start and completion time of each stage, its input and output data, the resource that executed it, and any failure that occurred during stages or process execution. By process records into a warehouse and by analyzing them with business intelligence technologies, we can extract knowledge about the circumstances in which high- or low-quality executions occurred in the past, and use this information to explain why they occurred as well as predict potential problems in running processes. The architecture of the FKMSTM is a layered structure as shown in Fig. 3.

The DMAIC methodology provides the financial industry with the opportunity to achieve quality and cost-effective development measures that will not only save companies substantial financial resources in rework and waste, but will also ultimately ensure financial knowledge continuity.

P1

P2

Pn

…

X1

X2

Xn Kn

K2

K1 K=P(X)

The collection of knowledge sets K={K1, K2, .., Kn} form the final knowledge

Knowledge Managements Platform

…

…

Fig. 2 Each Knowledge Discovery Instance Invokes a Different Set of I/O.

 Selection Pre-

processingInductionAlgorithm

Post-processing/

Interpretation

PreprocessedData

Target Data

Data Model/Patterns

Knowledge

Fig. 1 The knowledge discovery process (adapted from Fayyad et. al. 1996)

Applying DMAIC methodology in the development of the knowledge discovery process requires a fundamental cultural change as well as management commitment.

We refined the full lifecycle of a knowledge set of knowledge discovery results consisting of simple four basic phases: building, use, maintenance/update, and desertion, as depicted in Fig 4. During the first phase, the knowledge set must be designed and built with the discipline of DMAIC domain expertise based on FKMS to ensure that the knowledge that is delivered to production meets the domain expert’s needs. Once in production, the knowledge set must be maintained and improved over time to ensure a long and opulent lifecycle of the business’s financial investment.

Fig. 4 The Lifecycle of Knowledge Set Creation

Since DMAIC was developed specifically to improve the existing knowledge discovery processes, it seems only natural to apply the knowledge engineering perspective roadmaps into an extended roadmap (shown in Fig 5.) that can be used to ensure this DMAIC methodology quality throughout the lifecycle of the knowledge set creation. No matter which modeling framework is chosen, the DMAIC methodology can always be incorporated, as outlined below.

Define. The program of knowledge discovery should be defined as early as the knowledge engineering project-planning phase. The DMAIC first asks knowledge workers to

define their core processes following two steps: 1) Define knowledge project goals by domain expert. 2) Build knowledge sets, recognition of data cube’s factors, data cube gathering for training and testing, conceptual application for domain customers, model selection about mathematical models like neural networks (NNs) and fuzzy logic (FL), and designing model. Finally, the result will be created. To design the knowledge sets, we have to clearly conceptualize recognition of the data cube’s factors. To further specify the problem, a set of measurable requirements was gathered. For knowledge sets, these requirements are usually of the following types:

Data cube’s factors. For example, 1) relation to the operation of the actual data cube on corporate bond clusters, 2) requirements defining lower bounds on the data cube.

Model’s parameters. 1) Integration with certain models’ data mining techniques, like neural networks (NNs) and fuzzy logic (FL). 2) Set parameters from reliability metrics.

This is the phase in which one gathers the data and model of resources on the importance of knowledge quality measures and expectations. The progress of the knowledge discovery program should be tracked as part of the project schedule tracking activities. The milestones to be achieved by

Model Data

Parameters Application XML/WSDL

Knowledge Set

Build Use Maintenance/Update

Desertion

Reuse

Fig. 3 The System Architecture of FKMS

Quants Research

FKMSBasic Time Series Sets

FKMS Cube

ManagementTools

TrainingData

Evaluation Acceptable

stored into

Data

Resulting Knowledge

Data, Model, ParametersApplication,

Results

Derived Time Series Sets

Training / Testing Data

Benchmark Data

Data Mining Tool Sets

NN

Fuzzy

Gradient Maximization

Simulated Annealing

Benchmark

AssumedKnowledge

No

Yes

ModelApplicationParameters

XML/WSDL

Define

Measure Analyze

Improve Control

Fig. 5 Embedded DMAIC Methodology into FKMS

knowledge discovery program should be reflected as project milestones. A high level process map should also be outlined at this stage.

Measure. In this phase, the process is measured to determine current performance and to quantify the problem. One must answer the following questions: 1) What should be monitored? 2) How should the benchmark be monitored? 3) What is the plan to describe signals? Who will own the plan? 4) How will the monitoring system be maintained? Who will maintain it? This process defines what constitutes a mathematical model weight, the training time period, and the measurement unit for benchmark. Then a data collection plan should be drawn and people assigned to be responsible for identifying the needed data. This plan should be carried out during the whole knowledge development lifecycle for measuring key process deliverables.

Analyze. In the analyze phase, the system provides a signal when there is suggestion by the domain expert or other statistical proof that there has been a significant change in one of the leading indicator values. Then data collected are analyzed in addition to the model’s parameters that must be tuned, monitored, and maintained to ensure the classifier’s optimal performance and that the root causes of benchmark are determined. By doing so, mathematical model weights for improvement are identified. In this step, each statistical tool can be used to validate the root causes of the benchmark to identify data sources of process variations and to determine the most promising alternatives.

Improve. During the improve step of the DMAIC methodology, ideas and solutions are put to work. In this phase, the process provided maintenance and updating to improve knowledge sets’ quality by optimization of mathematical model weights, use of design of experiments, and identification of possible solutions. The target process is improved by designing creative solutions to rectify and prevent mathematical model weights. There must be checks to ensure that the desired results are being achieved. Some experiments and trials may be required in order to find the best model weights.

Control. During this phase, future process performance is controlled. This is done through performance tracking mechanisms and measurements based on FKMS platform in order to ensure, at a minimum, that the gains made in the knowledge engineering project are not lost over a period of time. The improvements are also institutionalized through the modification of systems and structures. This effort includes defining and validating in knowledge sets. Control mechanisms, development of standards and procedures, and verification of benefit and cost savings, etc., are determined. With this, the DMAIC methodology really starts to create returns; ideas and projects in one part of the organization are translated in a very rapid fashion into implementation in another part of the organization.

3.1 Ontology design based on DMAIC methodology.

From novel knowledge sets to formed historic reference decision. In this research, a multiplicity of methods has been conceived to support the knowledge discovery process modeling. The methods can be distinguished according to those aspects of the knowledge discovery processes modeling, which they aim to support, according to their chosen perspective on the problem (e.g., data cube-oriented, application-oriented, model-oriented, and result-oriented). As a result, attempts are being made to create a methodology (theory of methods) for the development methods. In the aim of categorizing and evaluating the methods, there is another set of reasons for considering knowledge management system design methodologies. These reasons result from the fact that, in general complex development, projects involve several partners who may implement diverse development methods and whose working results overlap. In this situation, only a conceptual framework which allows the categorization, of the various methods, and consequently their conformities and discrepancies, can generate mutual understanding. The fact that such a conceptual framework can, and even must, also lead to uniformity in the use of methods is, of course, also relevant. Architecture is generally understood as the art of construction. In practice, as one of the key problems of computer environment for knowledge schemes, the effective establishment and management of knowledge base systems is still troubled by the sharing and reuse of domain knowledge bodies in computational form at present.

So, what is domain knowledge in quant analysis? The answer to this question should begin with a description of knowledge itself. In this research, for supported DMAIC methodology that knowledge can be described as a set of knowledge that describes various properties and behaviors within a domain. The relation within knowledge management function is established through the knowledge set, which consists of five key parts: cube ID, model ID, results ID, application ID, and knowledge ID. We aim to define the knowledge scheme that decomposes complex knowledge sets into several simple IDs and the retrieval of knowledge of related records in FKMS by means of establishing relations within IDs. As a result, the issues of more correspondence relations existing in FKMS architecture are finally solved, as shown in Fig. 6. In the framework of this research, an extreme position on domain knowledge would be, “Any knowledge set that shapes the knowledge discovery process and experiences recorded.”

So, that which we defined with the domain experts, called upon from a committee of experienced financial staffs to conduct a clear review of a data cube of cases from our database and arrive at a unanimity decision for each case, is referred to as the standard of historic reference decision. The resulting knowledge sets containing the entire standard of historic reference decisions was used in the measurement system analysis of the original data cube. The knowledge sets

of standard of historic reference decisions also served as the benchmark decisions against which the decisions of both the knowledge sets and the current process of DMAIC could be independently compared. Without the knowledge sets of standard of historic reference decisions, there would have been scarcely able to measure the accuracy of the final knowledge set. The knowledge sets of standard of historic reference decisions were used during the building of the knowledge sets as well.

As started above, the DMAIC methodology can be a new perspective of knowledge engineering to modeling knowledge discovery process. In this analytic conceptual of the knowledge scheme, its domain knowledge base serves as a powerful tool to 1) manage data in enterprise, 2) formalize data cubes about behaviors and rules, 3) supply model management for separate application and data, 4) classify applications more effectively, 5) generate knowledge report to supply aid for users to make decisions, and 6) standardize historic reference decision to provide practical experience in the next round of the knowledge discovery process.

The rationality of the knowledge set structure directly affects creativity of knowledge schemes as well as its effective in enterprise intelligence. In addition, the modeling process can solve the efficiency issue and play a decisive role in deciding whether schemes which are finally generated could realize computer-aided innovative design in the real domain knowledge. We then proposed a basic idea in FKMS architecture that will support this DMAIC process approach and then advance domain knowledge of reusable ontology in a standard formalism, which each knowledge worker was supposed to adopt.

Meta taxonomy carried out through knowledge sets. To define the scopes of Quant research within domain knowledge that includes the following:

Data sets management. Data sets are registered and classified according to Meta Taxonomy that is named “Meta

data about data sets.” We have to define user defined data sets and derived data sets.

Applications management. Applications, models, and model associated parameters are registered and classified according to Meta Taxonomy. We call this “Meta data about applications,” “Meta data about models,” and “Meta data about model associated parameters.” After defining (and constructing) a data set for back testing, data mining models (and applications) are selected from the mining tools set. Templates/reports management. The data mining results (discovered knowledge) and reports are also registered and classified according to Meta Taxonomy.

Fig. 7. The Scheme of Knowledge Sets.

Fig. 7 shows the overall scheme of the knowledge sets and their relationships. Underlined attributes denote primary keys, while links among tables denote foreign key constraints. For clarity of presentation, in the figure we have slightly simplified the structure of the tables. In addition to instance execution tables, authorized users can access definition tables

Knowledge Set Instance Knowledge Set ID Application ID Model ID Data Set ID Results ID

Model Instance Model ID Model name Fundamental

Purpose description Register date Owner Model path …

Parameters Value 1 Value 2 …

ModelApplication Instance

Model name ID 1 Application ID 2 Application ID 3 …

Model name ID 4 Application ID 5 Application ID 6 …

Data Set Instance Data Set ID Fundamental

Purpose description Data period Owner Path …

Fields Field 1 Field 2 …

Result Instance Results ID Fundamental

Purpose description Output date Owner …

Results Content …

… … …

FKMS

Data Models Applications Discovered Knowledge

Results/ Reports

Application Application Application Instance

Model ID

Knowledge ID

Cube ID Result ID

Historic Reference Decision unique domain knowledge and experience

Data: The analyst personnel’s domain Data Cube by screening rule.

Models: Many Quant analysis methods that provided from model management module.

Applications: Completed model’s parameters setup, that it is named applications.

Discovered Knowledge: Analyst personnel’s unique domain knowledge and experience

Results/Reports: The result by finally carried out.

Fig.6. The Correspondence Relations of Knowledge Sets.

that describe the processes, nodes, services, and resources also defined in the FKMS.

We know that XML possessed characters of Metadata that TagName and TagValue have used to define Metadata about data. Users can use different elements and attributes to define the elaboration of data that users and applications can access easily. So, the XML document structure is a description result of Metadata. We also regard XML as the storage tools of data in addition to regarding the XML data model as the Metadata form of models. We also illustrated our knowledge sets scheme design with the description of two mainly XML documentations.

The content of DescriptiveModel.xml filled in while registering in the model. The content is divided into two parts to describe mainly just a single model (see left in Fig. 8). The content of ModelApplication.xml is a relevant description that records between mode and application. Every tag will be named with the model or application name and also have another ID attribute added as identification. The level 2 recording model and level 3 recording derivational application of model are demonstrated (see right in Fig. 8).

Fig. 8. DescriptiveModel.xml and ModelApplication.xml.

Ontology of knowledge abstraction. This research addresses the concept of FKMS, which is a prototype of KM environment specifically used for financial research purposes. The environment generates groups of knowledge sets with strings of data, models, applications, and reports. Based on the above taxonomy of enterprise metadata management, the data administrator can define enterprise metadata to align with corporation structure and operations based on the business requirements. Enterprise metadata management is the brain of the data conversion layer. For the purpose of being expandable, scalable, and portable, the ontology of the knowledge abstraction was represented with XBRL schema. Also, the design of enterprise metadata management is flexible to accommodate corporate structure and business changes if a corporate re-engineering is needed.

4 Conclusions Domain experts need a flexible system environment where they can freely select data and models and running different settings of parameters for decision support purposes.

Knowledge sets of each research experiment containing data, models, parameters, and results essentially provide great value for business intelligence generation. In this study, we propose a DMAIC methodology to optimize knowledge management and sharing architecture in financial service institutions. Then the integration of decision support and knowledge management processes is crucial for enterprises to create its niche business intelligence and to maintain global competitive advantages.

We implement the DMAIC methodology embedded into the FKMS, which can convert data from various sources into the data warehouse and retrieve data cubes in response to different knowledge workers’ request for report generating or for running decision support applications.

We have emphasized the knowledge engineering perspective for developing knowledge sets into the historic reference decision to form knowledge management and sharing architecture in the knowledge discovery process and the importance of leveraging structures and DMAIC methodology in design as well as implementation. We have demonstrated how this can be achieved by describing the development of knowledge sets embedded in the FKMS. In conclusion, we would like to highlight the key elements of the DMAIC roadmap as they apply to the development and implementation of any knowledge set for decision support.

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