1 KNOWLEDGE MANAGEMENT SYSTEM IMPROVEMENT TOWARDS SERVICE DESK OF OUTSOURCING IN BANKING BUSINESS MR PADEJ PHOMASAKHA NA SAKOLNAKORN A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN INFORMATION TECHNOLOGY DEPARTMENT OF INFORMATION TECHNOLOGY GRADUATE COLLEGE KING MONGKUT'S UNIVERSITY OF TECHNOLOGY NORTH BANGKOK ACADEMIC YEAR 2007 COPYRIGHT OF KING MONGKUT'S UNIVERSITY OF TECHNOLOGY NORTH BANGKOK
2 Name : Mr. Padej Phomasakha Na Sakolnakorn Thesis Title : Knowledge Management System Improvement towards Service Desk of IT Outsourcing in Banking Business Major Field : Information Technology King Mongkut s University of Technology North Bangkok Thesis Advisor : Assistant Professor Dr. Phayung Meesad Co-Advisor : Dr. Gareth Clayton Academic Year : 2007 Abstract In business, knowledge is an organizational asset that enables corporations to sustain competitive advantages. In addition to increasing the demands of IT outsourcing to deliver world-class services, the Information Technology Infrastructure Library (ITIL) is a key concept to provide the high quality service, and the IT service desk is a crucial function for a whole concept of IT service management. Three current problems include 1) technical staff turnover is very high 2) more than sixty percent of all resolving time is spent to resolve the repeat incidents and 3) the assigned resolver group to deal with the incident may be inaccurate due to human error. Thus, this thesis proposes a framework for a knowledge management system with root cause analysis so, called KMRCA IT service desk system and evaluates its performance. The system is composed of two main functions, a searching knowledge function, and an automatic assignment function. This thesis evaluated the performance of the searching knowledge function using a simulation study and concluded that the system could significantly reduce time in resolving incidents. Moreover, my thesis enhances the framework to select the most suitable resolver group to deal with the incident using Text mining discovery methods. The ID3 decision tress method could increase productivity and decrease reassignment turnaround times. Furthermore, the rules resulting from the rule generation from the decision tree could be properly kept in a knowledge database in order to support and assist with future assignments. (Total 153 pages) Keywords : knowledge management, service desk, outsourcing, text mining, ITIL, performance evaluation, simulation study, and decision tree. Advisor ii
4 ACKNOWLEDGEMENTS I wish to express my gratitude to a number of people who became involved with this thesis. Foremost, I would like to thank my advisors, Assist. Prof. Dr. Phayung Meesad, and Dr. Gareth Clayton for providing me with the opportunity to complete my PhD thesis at King Mongut s University of Technology North Bangkok. I, especially, would like to thank at points on my advisor, Assist. Prof. Dr. Phayung whose support and guidance made my thesis work possible. He has been actively interested in my work and has always been available to advise me. I am very grateful for his motivation, enthusiasm, and immense knowledge. He also contributes on my work to be onboard of international publishing. I would like to thank Dr. Gareth Clayton whose advances research methodology, particular statistics and simulation techniques providing to me both concepts and real practices with consciously and unconsciously ideas how good is good enough in experimental design should be taken together that make him a great mentor. Moreover, I would like to show my faithful thank to Assoc. Prof. Dr. Utomporn Phalavonk whose advocate of scheduling and recommendations of graduate college s regulations made me complete in my planning and performing administrative tasks. I would like to sincerely thank to Dr. Choochart Haruechaiyasak whose knowledge and technical suggestions about text mining discovery algorithms in particular word extraction and machine learning to facilitate the approach of automatic resolve group assignment in place of the IT service desk agent s tasks. Thanks to Taweesak Suwanjaritkul and Pisit Thongngok whose knowledge with regard to Visual Basic programming and SQL server 2005 database management that made the prototype of KMRCA IT service desk system worked effectively. Thanks to members of IT admin staff whose works made the most of my administrative documents done during my study at the university. This thesis could not be complete without my wife and all people in my family particular Dad and Mom who have supported me since I was born. Padej Phomasakha Na Sakolnakorn iv
5 TABLE OF CONTENTS Page Abstract (in English) ii Abstract (in Thai) iii Acknowledgements iv List of Tables vii List of Figures viii Chapter 1 Introduction Background and Statement of the Problem Objectives Hypothesis Scope of the Study Utilization of the Study 5 Chapter 2 Literature Review Knowledge Management Root Cause Analysis Case-Based Reasoning ITIL-Based IT Service Desk Function Technologies for Service Desk IT Service Desk Outsourcing Decision Support System Classification trees Summary 28 Chapter 3 Methodology Research Process Information Collection and Requirement Analysis Constructing an Instrument for Data Collection The Proposed KMRCA IT Service Desk Framework Methodology of Automatic Resolver Assignment Summary 59 v
6 TABLE OF CONTENTS (CONTINUED) Page Chapter 4 Experimental Results The Results of Text Mining Discovery Methods of Automatic Assign Function The Results of Design of Experiment The Results of Performance Evaluation Summary 69 Chapter 5 Conclusion Conclusion Discussion Future Work 73 References 75 Appendix A 81 Appendix B 89 Appendix C 129 Biography 153 vi
7 LIST OF TABLES Table Page 3-1 The Rate of Incident Calls during Time in Business Day and Holiday Percentage of Incident Calls by Severity Classification of Calls by Incident Category Summary of Probability Distributions for Computer Simulation Comparison of Square Error by Function A Good-of-fit Test of Time in Resolving Incidents by Severity The Number of Incidents of System Types and Resolver Groups The Number and Percentage of Correct Incident for Various Types of Decision Trees The Speed Compared with the Accuracy of Classification Assigned Factor Values for Two-Level Full Factorial Design of DOE for Responses Y of O Coded Design Matrix of O Absolute Value of Coefficients for Average O 1 and P-Value Absolute Value of Coefficients for Average O 4 and P-Value Comparison Tests of KMRCA and Typical IT Service Desk Systems Comparison Outputs of KMRCA and Typical IT Service Desk Systems 68 vii
8 LIST OF FIGURES Figure Page 2-1 The Case-Based Reasoning Cycle Classification Hierarchy of Case-Based Reasoning Applications Incident Management Process Overview The Incident Life Cycle First, Second, and Third Line Supports Relationship between Incidents Handling Incident Work-arounds and Resolutions Input Analyzed Results Probability Plot of Time between Arrivals Probability Plot for Resolving Time by Severity A Typical IT Service Desk Outsourcing Overview Information Flow of IT Service Desk A Conceptual Model of IT Service Desk System A Proposed Framework of KMRCA IT Service Desk System Information Flow of KMRCA IT Service Desk System KMRCA IT Service Desk Process Search Knowledge Procedure Typical IT Service Desk and KMRCA IT Service Desk The System Development Life Cycle (SDLC) A Sample Display of Search Knowledge and Input Resolution A Sample Display of Searching Results A Sample Display of Assign Resolver Group KMRCA IT Service Desk with Automatic Assignment Function A Process of Automatic Resolver Group Assignment Processes of Model Approach for Automatic Assignment Pareto of Coefficients for Average Response Y of O Pareto of Coefficients for Average Response Y of O 4 66 viii
9 CHAPTER 1 INTRODUCTION 1.1 Background and Statement of the Problem Knowledge management is the business process of managing the organization s knowledge by means of systematic and organizational specific processes for acquiring, organizing, sustaining, applying, sharing, and renewing both tacit knowledge and explicit knowledge by employees not only to enhance the organizational performance, but also to create value [1, 2, 3, 4]. Due to the rapid change in technology and competition among global financial institutions, the banks in Thailand also need to reduce costs and to improve their quality of services by strategic information technology (IT) outsourcing such as data processing and system development to the third parties. IT outsourcings are understood as a process in which certain service providers, external to organizations, takes over IT functions formerly conducted within the boundaries of the firm [5, 6]. The IT service desk is a crucial function of incident management driven by alignment with the business objectives of the enterprise that requires IT support, balancing theirs operations and achieving desired service level targets while IT Infrastructure Library (ITIL) has become a strategic tool for efficiency and effectiveness of IT outsourcing providers to provide a competitive approach. The ITIL defines a set of the best practice processes to align IT services to business needs and constitutes the framework for IT service management [7, 8]. The primary objective of the IT service desk is to resolve incidents related to IT in the organization. As the case study, it appears that the IT service desk outsourcing s role is not quite a single point of contact . The bank takes ownership of the help desk agent called the first level support (FLS) which acts as more than just an interface for internal users and external customers. Consequently, IT service desk as a second level support (SLS) will resolve the assigned incidents from the FLS by ensuring that the incident is in the outsourcing scope and still owned, tracked, and monitored throughout its life cycle.
10 2 For the technologies regarding service desk, many organizations have focused on computer telephony integration (CTI). The basis of CTI is to integrate computers and telephones so that they can work together seamlessly and intelligently . The major hardware technologies are as follows: automatic call distributor (ACD), voice response unit (VUR) and interactive voice response unit (IVR) . These technologies are used to make the existing process more efficient by focusing on minimizing the agent s idle time. In resolving the incident effectively, IT service desk agents must be very knowledgeable of their service supports, applications, and support teams. Most efforts at improving service desk performance have been to make the current system more efficient through applications of information technologies. Those technologies do not address the problem of resolving performance dropped due to incorrect assignments. This thesis identifies three problems as follows: The employee turnover is very high, particularly for technical employees . For the reason that service desk staff store significant knowledge regarding the systems such as business processes, and technologies and if they leave their knowledge often goes with them More than sixty percent of all resolving time is spent to resolve the repeat incident  The assigned resolver group to deal with the incident may be mistaken due to human errors. Because the resolver group assignments are still performed manually by IT service desk agents. The first of two problems can be resolved by keeping employee s knowledge along with the organization by knowledge management approach and to conduct the way to prevent the recurring incidents by using root cause analysis. The activities are becoming the primary internal IT service desk functions of the outsourcing and they are the potential to provide the competitive advantages. The last problem of underlying for the incorrect resolver group assignment can be resolved by means of automatic assignment approach. The Text mining discovery methods can find out the suitable methods such as decision trees to support the correct assign and the rule resulting from the rule generation from the decision tree could be properly kept in a knowledge database in order to support and assist with further assignments.
11 3 1.2 Objectives The objectives of this dissertation are as follows: To propose a framework for knowledge management system with root cause analysis based on ITIL best practice for IT service desk outsourcing in the banking business called KMRCA IT service desk system To evaluate the performance of the KMRCA IT service desk system before-and-after usage by using experimental design and simulation study. 1.3 Hypothesis For the reason that the performance of KMRCA IT service desk system will be higher than the Typical IT service desk system in terms of speed in resolving incidents. Therefore, the defined hypothesis of the alternative hypothesis (H 1 ) is the average time in resolving incidents for all calls except for critical calls will be lower in KMRCA IT service desk system than the currently Typical IT service desk system and null hypothesis (H 0 ) is that the average time in resolving incident of the both systems are the same. Two rival hypotheses are compared by a statistical hypothesis test. H 0 : µ 1 = µ 2, and H 1 : µ 1 < µ 2, where µ 1 and µ 2 are the average time in resolving incidents of KMRCA IT service desk system and the average time in resolving incidents of Typical IT service desk, respectively. The statistical hypothesis test approach is to calculate the probability that the observed effect will occur if the null hypothesis is true. In other words, if the p-value is small then the result is called statistically significant and the null hypothesis is rejected in favour of the alternative hypothesis. If not, then the null hypothesis is not rejected. Incorrectly rejecting the null hypothesis is a Type I error incorrectly failing to reject it is a Type II error. 1.4 Scope of the Study The scope of this dissertation is as follows: This study focuses on the performance evaluation in terms of throughput and average time taken in resolving incidents.
12 The performance evaluation is to compare before-and-after employment KMRCA IT service desk system by using simulation study within Arena software package and design of experiment of 2 3 factorial design For the framework, IT service desk outsourcing includes IT service desk agents and five resolver groups, including EOS (enterprise operating service), IE-AMS (application management service), NWS (network service), OS-EC (operation service), and VEN (vendor service) ITIL-based KMRCA IT service desk processes include IT service desk function, Incident management process and problem management process The proposed KMRCA IT service desk system developed based on system analysis, system development life cycle (SDLC) method. In addition, the system composes of two main functions, a searching knowledge function based on case-based reasoning, and an automatic resolver group assign function based on the method generating from text mining discovery algorithms The text mining discovers algorithms is to find out the strongest methods by comparing seven decision trees within WEKA  machine learning, Decision stump, ID3, J48, NBTree, Random Forest, Random Tree and REPTree The resolver groups are always available when they receive the assigned incidents from the IT service desk agents For performance evaluation, a sample of incident data collected from Tivoli CTI system of IT service desk outsourcing of selected 12,198 calls (prime time on the working days) for 4-month during April to July For the study of automatic resolver assign, a sample of incident data collected from Tivoli CTI system of IT service desk outsourcing of all 14,440 cases for 4-month during April to July Obviously, the sample sizes are different from each other because there are on the different sides of the study objectives. For performance evaluation using simulation study, a sample size is selected 12,198 calls during the prime time on the working days since the aim needs the simulation output as real as possible. Another of automatic resolver group assignment, a sample size is all 14,440 cases because the main purpose of the study requires all data to execute to the system no matter what time concerns, but determine to assign correctly as relevant symptoms of the incident.
13 5 1.5 Utilization of the Study The Performance evaluation using simulation study and experimental design can be adopted to find out the specification of the knowledge management system. For example, the performance evaluation of KMRCA IT Service Desk can be applied to the other service desk functions to identify the KMRCA specifications and then it can be modified according to the organization s requirements The simulation study is also used to evaluate KMRCA IT service desk system s performance without interrupting the daily IT service desk s operations. Moreover, the way of simulation can be applied in several industries processes in time being concern in order to manage constrictions of the system The ITIL-based IT service desk function in incident management and problem management processes can be adopted and adapted to the organizational outsourcing to deal with the ITIL certification The data preparation process and text mining discovery algorithm method can be applied to the empirical studies that need data pre-processing and transforming the results to find the strongest method for the classification approach The suitable decision tree-based in the function of IT service desk system provides not only automatic resolver group assign, but also the knowledge acquisitions that are the rules resulting from the rule generating from the decision tree method. The acquired knowledge can be kept to support and assist to the further assignments. This thesis organizes the remainders as follows. Chapter 2 describes literature review, including knowledge management (KM), root cause analysis (RCA), case based reasoning (CBR), ITIL-based IT service desk, technologies for IT service desk, IT service desk outsourcing, decision support system (DSS) for resource assignments and classification trees. The details of the proposed model frameworks are illustrated in Chapter 3. Chapter 4 gives results of the study and discussion. Finally, conclusion and future work are presented in Chapter 5.
14 CHAPTER 2 LITERATURE REVIEW This chapter describes the review of several literatures with regard to the study, including knowledge management, root cause analysis, and case-based reasoning which are illustrated in Sections 2.1, 2.2, and 2.3. Sections 2.4 and 2.5 describe ITILbased service desk function, and technologies for service desks. The IT service desk outsourcing is describes in Section 2.6. Decision support system considering resource assignment and Classification trees are illustrated in Sections 2.7 and 2.8. Moreover, the summary is shown in Section Knowledge Management The study of knowledge management started from Polanyi s Tacit Dimension. His analysis emphasized several key concepts. Firstly, the ability to identify the outside objects, and then to know, is learned through a process of personal experience. Secondly, tacitness and explicitness are distinct dimensions the increase of one does not come at the decrease of the other. Thirdly, since tacit knowing is an essential element of any kind of knowledge and is acquired through personal experience called indwelling, any effort to achieve absolute detachment, the objective of knowledge is misdirected and self defeating. Polanyi s work was situated in a philosophical context, and focused on the definition of knowledge but not on the systematic effort of managing it . The conceptualization of KM was not developed until knowledge became central to production and innovation in the 1990s. Peter Drucker  is among the first who advocated the advent of a knowledge society. In the Post-Capitalist Society , Drucker  documented the transformation from a capitalist to a Knowledge Society, which began shortly after World War II, noting that the foremost economic resource is no longer capital, land, or labor. Rather, it is and will be knowledge . The field of knowledge management has also been developed by the experience and philosophy of Eastern society.
15 8 Nonaka and Takeuchi s Knowledge-Creating Company , based on experience in Japanese companies, is a pioneer work in mapping explicit and implicit knowledge, as well as individual, group, and organizational knowledge into one matrix describing called the dynamics of knowledge creation. They introduced the socialization, externalization, combination, and internalization processes by the SECI model that becomes popular in knowledge management today. This SECI model or SECI processes explain the organizational knowledge creation theory and serve as a method of understanding how an organization creates a new product, new process, or new organisation structure. This concept is easily understood by focusing on the project in the system solution business in which creation of a new product or new process that leads to success. Though many success cases in business activity indicate efficient and effective implementation of SECI an innovative organization does not simply solve the existing problems or process external information for adapting to environmental changes. In order to find out the problem or solution, it recreates a new environment while producing new knowledge or information are from the inside organization. For this reason, the SECI processes of knowledge management may be considered comparable to the project management for organizing a project and guiding it to success . Knowledge management (KM) is the process of managing the organization s knowledge by means of systematic and organizational processes conducted by employees to enhance the organizational performance and create value [1, 2, 3]. The development of KM, on the other hand, has been driven by practices and development in information and data management . Organizations should therefore seek and share a combination of tacit and explicit knowledge with suppliers and other parties in the value chain to satisfy customer needs in a highly competitive environment. KM is more than just the advantage of technology, intranet and internet, but includes organizational issues, assumes information resource management together with the cultural change which is important in the KM implementation process . For the organizations, the knowledge management is about acquisition and storage of employees' knowledge and making the knowledge accessible to other employees within the organization [3, 18, 19, 20]. Nonaka and Takeuch  have extensively studied knowledge in the organization and developed a model that
16 9 describes knowledge as existing in two forms. Tacit knowledge is defined as personal, context-specific knowledge that is difficult to formalize and communicate. Explicit knowledge is factual and easily codified so that it can be formally documented and transmitted. Through knowledge management, a company changes individual's knowledge into organizational knowledge . Organizational knowledge is knowledge held by the organization. The organization maintains the organizational knowledge in organizational knowledge resources which are operated on by human or computer processes that manipulate the knowledge to create value for the organization . Nonaka and Takeuchi  defined organizational learning as, a process that amplifies the knowledge created by individuals and crystallizes it as part of the knowledge network of the organization. In a service desk environment, much of the knowledge is from experiential learning [23, 24]. A challenge is how to transfer the knowledge gained by individuals into organizational knowledge. Phomasakha and Meesad  reviewed several knowledge management system (KMS) from several literatures regarding knowledge management systems and proposed the KMS compose of five processes, (1) knowledge capturing or knowledge discovery (2) knowledge creation (3) knowledge inventory or storing knowledge (4) knowledge sharing and (5) knowledge transfer which are working in cycle and the knowledge sharing and knowledge transfer are conveyed to the community of practice (CoP) which people know how to use the real knowledge. However, the IT is used to support only knowledge creation and knowledge inventory that are conducted to the organizational memory (OM) . For the service desk, the relevant knowledge management approach is of problem solving. Gray  presented a framework that categorizes knowledge management according to a problem solving perspective. The framework was defined four cells according to the type of problem and the process supported. Along the horizontal axis they defined two classes of problems as new problems and previously solved problems. Along the vertical axis they define two processes of problem recognition and problem solving. The primary function of the service desk is problem solving of both new and previously solved problems. When solving new problems, Gray  called this knowledge creation. Solving previously solved problems was called knowledge acquisition.
17 10 Several characteristics can be defined that will make a KMS successful in the service desk. The KMS must be able to gather knowledge from humans and other sources. In an environment of IT outsourcing in banking business, IT service desk outsourcing is a curial functions of an IT outsourcing provider who takes over IT functions from its customer or the bank. However, the bank desires service level targets based on service level agreement (SLA) to control the IT service desk operations . The purpose of the IT service desk outsourcing is to support customer services on behalf of the bank s business goals with technology driven. The role of IT service desk is to ensure that IT incident tickets are owned, tracked, and monitored throughout their life cycle. 2.2 Root Cause Analysis A root cause analysis (RCA) is a structural investigation that aims to identify the true cause of a problem, and the actions necessary to be taken to eliminate it . The RCA is the process to identify effortless factors using structured approach with techniques decided to provide a focus on identifying and resolving problems. The RCA also provides objectivity for problem solving, assists in developing solutions, predicts other problems, gathers contributing incidents, and focus attention on preventing recurrences. The techniques of the root cause analysis are often applied for input for decision making process. The root cause analysis identifies and prevents future errors in the proactive mode . However, root cause analysis will tell the real reasons for problems . The results of RCA, when eliminated or changed, will prevent the recurrence of the specific or similar problems, and therefore the benefits of the RCA are to improve the service level agreement (SLA) attainment and to enhance quality services as well as customer satisfaction. In this study is to develop not only knowledge management system (KMS), but also the RCA embedded into the system in order to prevent the recurring incidents oin the KMRCA IT service desk system. The KMS is designed to be incorporated into the daily operation of the service desk to ensure high utilization and maintenance of the knowledge stores . Moreover, the knowledge-based library of RCA models could be hierarchically structured and interconnected failure trees, the abnormalities in process operations and output quality can originate from abnormalities in equipment or in process conditions possibly due to basic failures .
18 Case-Based Reasoning Case-Based Reasoning (CBR) is widely used in resolving incident that is able to resolve a new incident by remembering a previous similar situation and by reusing information and knowledge of that situation [32, 33]. More specifically, CBR uses a database of incident to resolve new incidents. The database can be built through the knowledge management process or it can be collected from the previous cases. In resolving incident, each case would describe an incident and a resolution to that incident occurred. The reasoner resolves new incidents by adapting relevant cases from the library . In addition, CBR can learn from previous experiences. When an incident is resolved the case-based reasoner can add the incident description and the solution to the case library. The new case that in general represented as a pair of incident and resolution is immediately available and can be considered as a new piece of knowledge. According to Doyle et al. , Case-Based Reasoning is different from other artificial intelligence (AI) approaches in following ways: (a) Traditional AI approaches rely on general knowledge of an incident domain and tend to solve incidents on a first-principle while CBR systems solve new incidents by utilizing specific knowledge of past experiences. (b) CBR supports incremental, sustained learning. After CBR solves an incident, it will make the incident available for future incidents. In 1977, Schank and Abelson s  work brought CBR from research into cognitive science . They proposed that general knowledge about situations be recorded as scripts that allow us to set up expectations and perform inferences . Schank  then investigated the role that the memory of previous situations and situation patterns scripts, MOPS play in incident solving and learning . Almost at a similar time, Gentner  investigated analogy reasoning that is related to CBR while Carbonell  explored the role of analogy in learning and plan generalization [38, 39]. Subsequently, increasing numbers of research paper and applications were published, and CBR has grown into a field of widespread interest. It has proven itself to be a methodology suited to solving weak theory incidents where it is difficult or impossible to elicit first principle rules from which solutions may be created .
19 The CBR Cycle The CBR process can be represented by a schematic cycle, as shown in Figure 2-1. Aamodt and Plaza  described CBR typically as 4-RE cyclical process comprising as follows: 1) RETRIVE the most similar cases during this process, the CB reasoner searches the database to find the most approximate case to the current situation. 2) REUSE the cases to attempt to solve the incident this process includes using the retrieved case and adapting it to the new situation. At the end of this process, the reasoner might propose a solution. 3) REVISE the proposed solution if necessary since the proposed solution could be inadequate, this process can correct the first proposed solution. 4) RETAIN the new solution as a part of a new case. FIGURE 2-1 The Case-Based Reasoning Cycle . This process enables CBR to learn and create a new solution and a new case that should be added to the case base. It should be noted that the Retrieve process in CBR is different from the process in a database. If you want to query data, the database only retrieves some data using an exact matching while a CBR can retrieve data using an approximate matching. As shown in Figure 2-1, the CBR cycle starts with the description of a new incident, which can be solved by retrieving previous cases and reusing solved cases, if possible, giving a suggested solution or revising the solution, retaining the repaired case and incorporating it into the case base.
20 13 However, this cycle rarely occurs without human intervention that is usually involved in the Retain step. Many application systems and tools act as a case retrieval system, such as some help desk systems and customer support systems A Classification of CBR Applications Althoff  suggested a classification method of CBR application as shown in Figure 2-2. Under this classification scheme, CBR applications can be classified into two categories as follows: (a) Classification tasks (b) Synthesis tasks FIGURE 2-2 Classification Hierarchy of Case-Based Reasoning Applications . Classification tasks are very common in business and everyday life. A new case is matched against those in the case-base from which an answer can be given. The solution from the best matching case is then reused. In fact, most commercial CBR tools support classification tasks. Synthesis tasks attempt to get a new solution by combining previous solutions and there are a variety of constraints during synthesis. Usually, they are harder to implement. CBR systems that perform synthesis tasks must make use of adaptation and are usually hybrid systems combining CBR with other techniques .
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