A Case-Based Reasoning Approach to the Reusability of CWM Metadata Lamiae Demraoui
Hicham Behja
El Moukhtar Zemmouri
Rachid Ben Abbou
SIA Laboratory Faculté des Sciences et Techniques, USMBA Fes, Morocco
[email protected]. ma
RI Laboratory Ecole Nationale Supérieure d'Electricité et de Mécanique Casablanca, Morocco
[email protected]
LM2I Laboratory Ecole Nationale Supérieure d’Arts et Métiers Meknes, Moulay Ismail University, Morocco
[email protected]
SIA Laboratory Faculté des Sciences et Techniques, USMBA, Fes, Morocco
[email protected]
Abstract— A decisional process is usually held by one or more expert users having different viewpoints. That is, they manipulate several knowledge and know-how with different objectives, preferences, competencies, and different visions on the data warehouse entities. In this paper, we propose to establish coordination and knowledge sharing between users of a decisional process based on the Common Warehouse Metamodel (CWM) standard to enhance reusability in terms of viewpoint within a system and to promote a good coordination between users of a multi-view analysis. After a brief review of our approach of viewpoint in CWM, we will first model and formalize interaction and interdependence between various analyses according to different viewpoints through introducing a set of relations between viewpoints. These relations allow us to ensure coordination and mutual understanding between different users of a decisional process based on the CWM. Then, based on those relations, we propose a Case-Based Reasoning (CBR) process with the aim to improve reusability of users’ analysis and experiences, and to guide novice users during the exploitation of the decisional process in terms of viewpoint. Keywords— CWM; Metadata; Viewpoint; Reusability; Case Based Reasoning
I.
INTRODUCTION
To overcome the management and integration metadata problems, the Object Management Group (OMG) 1 provided the Common Warehouse Metamodel (CWM) standard [1]. The CWM enables interoperability and easy interchange of warehouse and business intelligence metadata between heterogeneous systems. It represents one of the most suitable approaches in the data warehousing domain; it has been constructed as a metadata standard for the data warehouse domain and it is supported by a large number of data warehouse software vendors. Also, the CWM’s specification is comprehensive to facilitate the management of complexity in the modeling of large data warehouses by breaking up the descriptions of both the problem domain and its solutions into easily managed and reusable parts [2].
1
OMG, Object Management Group, http://www.omg.org
As known, a decisional process is usually held by one or more expert users who manipulate several knowledge and know-how, they have different objectives and preferences, different competencies, and different visions of the data warehouse entities. In brief, they have different viewpoints [3]. Even though that the CWM covers the data warehouse life cycle, its metamodels lack of the precise semantics and constructs to model users’ preferences and experiences taken along the exploitation of the decisional process. With this attention, our main objective was to extend the CWM metamodel with a new entity that is a viewpoint that allowed to model and keep trace of user preferences, objectives, and decisions made during a decisional process. This extension will allow to capitalize users’ experiences in order to enhance reusability of metadata and coordination between users. In the remainder of this introductory section we briefly recall our approach of viewpoint in CWM to fix context of our work. This approach was initiated in our previous work [4]. The CWM can be accessed through different ways and by a variety of roles and profiles: warehouse platform and tool vendors, professional service providers, warehouse developers, warehouse administrators, end users, and information technology managers [1]. Each of those users participating in one or several stages in the development and maintenance of data warehouses and can extend the functionality provided by the base CWM metamodel with specific needs. This access can be different in order to respect the user’s needs, user’s expectation and user’s preferences that will be presented by the concept of viewpoint; Also, each user generates a set of tasks during the development of the data warehouse and explore that warehouse with different contexts, objectives and preferences, so with a different viewpoint. As an example, if we consider two CWM’s users (end user and warehouse developer) during the exploitation of a data warehouse example, the end user will be interested to make reports and establish queries while the Warehouse Developer will focus on the building of the logical design of the data warehouse and loading ETL applications. In fact, during a multi-viewpoint analysis based on the data warehouse, each user has his own viewpoint.
The viewpoint will serve to construct a knowledge base that will allow reusability and guide novice users during the exploitation of the decisional process. Then, to formalize interaction and interdependence between various analyses according to different viewpoints, we present a set of relations to link viewpoints. We have defined equivalence, inclusion, and independence relations. These relations allow us to enhance collaboration, coordination, knowledge sharing and mutual understanding between different actors of a decisional process based on CWM, and reusability in terms of viewpoint of successful data warehouse experiences within a system. Also, basing on the defined knowledge base and the relations between viewpoints we will define our CBR approach in CWM to improve reusability of users’ analysis and experiences gathered along the exploitation of the data warehouse based on the CWM, and to guide novice users during the exploitation of the decisional process in terms of viewpoint. The remainder of this paper is organized as follows. In the next section, we will present at first a state of the art of works that defined relations between viewpoint, secondly, we will define a set of relations between the viewpoints of CWM’s users, especially: equivalence, inclusion, and independence. As mentioned before, the main purpose of these relations is to provide a means of collaboration, coordination and mutual understanding between viewpoints users. Section 3 present our CBR approach in CWM in order to reuse user’s experiences during a data warehouse process based on CWM. Finally, section 4 concludes the paper and gives our perspectives. II.
RELATIONS BETWEEN VIEWPOINTS
During a multi-viewpoint process, it is important to emphasize the interaction and interdependence between the various analysis according to different viewpoints. In this section, we define a set of relations to link between the different users’ viewpoints expressed during a decisional process in the aim to allow reusability of analysis done along a decisional process based on CWM in terms of viewpoint. A. Related work In most modeling paradigms, relations between objects have a significant role in expressing dependency between entities of a conceptual domain. Many works that used the object oriented formalisms for modeling their multi-view approach defined relations between viewpoints with different nomenclature: [5] proposed a kind of gateways to connect the different viewpoints of a concept to allow reasoning between viewpoints such as consistency checking, [6] defined a dependency relation to link viewpoints in the aim to maintain consistency between user’s views, and [7] defined an equivalence and a subsumption link to establish relations between viewpoints. Also, [8] used the conceptual graphs formalism to model the viewpoint concept and established various links to handle the different relations existing between terminologies such as equivalence, inclusion and exclusion links. A lot of work which used the ontology formalism to model the viewpoint approach established different kind of gateways between classes of a viewpoint concept; [9] defined
a multitude of links (equivalent, compatible, incompatible, into, and under) to allow joint use of different contexts by making possible the exchange and reuse of knowledge from one context to another. Moreover, equivalence, inclusion and exclusion links are defined by [10] to connect the decomposed classes differently according to different viewpoints and to allow reasonning with classes and instances defined in the proposed model of viewpoint. Furthermore, [11] introduce some relations between viewpoints of a multi-view analysis based on a goal driven approach and relationships between goals (equivalence, inclusion, conflict, requirement, and independence); the main purposes of these relations are to enhance coordination and mutual understanding between viewpoint stakeholders, and to allow reusability of the KDD process in terms of viewpoint. Also, to allow comparaison and linking between different viewpoints in a user generated content, various relations have been defined by [12] such as: equality, disconnection, includes/ included, and overlap. In addition, in a multi-view ontology, [13] defined several links between viewpoints (equivalent, interpretation, inclusion, and disjunction). The particularity of those links representation is the existence of a communication channel among various viewpoints. This communication channel, called bridge rule, allows representing links between local concepts of different viewpoints. In the same context and with the same objective, a set of bridge rules has been defined such as: inclusion, inclusion with several sources, bi-directional inclusion, and bidirectional exclusion [14] in the aim to link different concepts from different viewpoint and to infer additional knowledge throught the use of those rules. Moreover, [15] identified two types of dependencies (structural and behavioral dependency) between users’ views; these dependency relations allow to provide interaction, composition and dependencies between services based on actors’ views. As mentioned in the works previously, relations allow to express dependency between entities of a conceptual model and allow to establish interaction and interdependence between the various analyses according to different viewpoints during a multi-viewpoint analysis. Accordingly, it is necessary for our approach of viewpoint in CWM to provide opportunities for expressing such interaction and dependency in terms of relations. B. Viewpoint’s relations in the CWM In order to assist users during the exploitation of the decisional process we have extended the CWM metamodel with a new entity that is viewpoint. This extension allowed to facilitate the use of the CWM standard in terms of viewpoint and to model and keep trace of user preferences, objectives, and decisions made by users during a decisional process. CWM could be accessed by a variety of users: warehouse platform and tool vendors, professional service providers, warehouse developers, warehouse administrators, end users, and information technology managers. Each of those users participating in one or several stages in the development and maintenance of data warehouses and can extend the functionality provided by the base CWM metamodel with specific needs. This access can be different in order to respect the user’s needs, user’s expectation and user’s preferences that
Fig. 1. The viewpoint metamodel
will be presented by the concept of viewpoint. Therefore, our viewpoint metamodel extended the CWM one with new modeling elements as present the Fig. 1 where the viewpoint metamodel classes are shown in gray, whereas CWM classes remain in white.
represents user’s preferences expressed along the exploitation of the data warehouse process, and in order to establish relations between the different viewpoints, each ViewPoint class is connected to the Dependency metamodel of CWM via VPDependency.
We specialized the ResponsibleParty class from the Business Information package of CWM with users’ classes: WarehousePlatform, EndUser, ProfessionalServiceProvider, WarehouseDeveloper, WarehouseAdministrator, and InformationTechnologyManager that represent the categories of users that implement a decisional process based on CWM. The MultiviewDecisionalProcess class specializes the package Classifier of CWM and represents users’ viewpoint expressed along the decisional process. This class is composed of a Base class that describes the structural and behavioral characteristics common to all the users of the MultiViewDecisionalProcess, and a set of ViewPoint class that models the structural and behavioral characteristics specific to a particular user while having the opportunity to redefine the characteristics of the base to which it is connected via the VPExtension relationship. The class viewpoint is specialized by the classes that represent the viewpoint of each users’ categories of CWM (WarehousePlatformVP, WarehouseDeveloperVP, EndUserVP, ProfessionalServiceProviderVP, WarehouseAdministratorVP, and InformationTechnologyManagerVP), characterized by a Context, an Objective of analysis and a Preference class that
Consequently, and basing on the works presented previously, we define some relations between viewpoints of a decisional process based on CWM referring to the attributes: context, objective, and preference. The main purposes of these relations are to enhance coordination and mutual understanding between users’ viewpoint, and to allow reusability of the analysis done along a decisional process based on CWM in terms of viewpoint. Therefore, we defined three types of links to handle the different possible relations existing between viewpoints in a multi-viewpoint analysis based on CWM: equivalence, inclusion, and independence link.
Equivalence link
Let VP1 and VP2 be two distinct viewpoints. VP1 is equivalent to VP2 if VP1 and VP2 have the same context, the same preference, and the same objective. The equivalence link between two viewpoints enables to identify two user’s views having the same meaning, but used in different contexts (and perhaps named differently). The equivalence link serves mainly the purpose of reusability in a decisional process based on CWM in terms of viewpoint. For
instance, this will be beneficial if two CWM’s users apply in different phases of a decisional process with the same context, objective and preference but with different names of the viewpoint. As a result, this relation may be beneficial within a system to reuse successful experiences to achieve different objectives associated to different viewpoints.
processes involves a number of more specific steps; this cycle is illustrated in Fig. 2.
Inclusion link
Let VP1 and VP2 be two distinct viewpoints. VP1 includes VP2 if the satisfaction of the VP1’s objectives implies the satisfaction of the VP2 objectives, the satisfaction of the VP1’s context implies the satisfaction of the VP2 context, and the satisfaction of the VP1’s preference implies the satisfaction of the VP2 preference. Taking the example of the two CWM’s users: Warehouse Developer and Warehouse Administrator, the first user develops the logical design of the data warehouse while the Warehouse Administrator defines the physical one, so, the viewpoint of the Warehouse Developer implies that of the Warehouse Admin.
Independence link
Let VP1 and VP2 be two distinct viewpoints. VP1 and VP2 are independent if the existence of VP1 doesn’t influence neither positively nor negatively the existence of VP2. This link is useful in the case of two different viewpoints expressed in different phases of the exploitation of the data warehouse in which the presence of one viewpoint doesn’t influence the presence of the other one. The inclusion and independence link can be used to provide assistance for novice users of CWM to achieve their goals basing on different viewpoints. III.
OUR CBR APPROACH IN CWM
In this section, we initiate our case based reasoning approach in CWM based on the relations between viewpoints defined previously, the CBR approach in CWM will allow to improve reusability of users’ analysis and experiences gathered along the exploitation of the data warehouse based on the CWM, and to guide novice users during the exploitation of the decisional process in terms of viewpoint. A. The CBR process The main feature of CBR is its ability to emulate human reasoning for solving new problems by remembering past experiences. In CBR, past experiences are stored as cases; each one encloses the description of a problem (source problem) and its associated solution (source solution). At the highest level of generality, a general CBR cycle may be described by four processes: Retrieve, Reuse, Revise, and Retain [16]. A new problem is solved by retrieving one or more previously experienced cases, reusing the case in one way or another, revising the solution based on reusing a previous case, and retaining the new experience by incorporating it into the existing knowledge-base (case-base). Each of the four
Fig. 2. The CBR cycle [16]
An initial description of a problem defines a new case. This new case is used to RETRIEVE a case from the collection of previous cases. The retrieved case is combined with the new case - through REUSE - into a solved case, i.e. a proposed solution to the initial problem. Through the REVISE process this solution is tested for success, and repaired if failed. During RETAIN, useful experience is retained for future reuse, and the case base is updated by a new learned case, or by modification of some existing cases. CBR allows reducing the knowledge acquisition task; it has an ability to support long-term learning, a capacity for reasoning with incomplete or imprecise data, a vicinity with human reasoning and an ability to create and to maintain a computer decision support tool [17]. Its ability to solve problems enhances with the increasing amount of accumulated cases. By adopting a CBR system, organizations are able to gradually deal with new problems by referring to their past knowledge more effectively [18]. In fact, and due to its ability to cope with complex problems, CBR has been widely used in various studies and issues, including diagnosis applications [19], medical applications [20][21][22], logistics [23][24], education [25][26], knowledge management [27], image processing [28][29], decision making [30][31], or recommender systems [32][33]. All the works previously mentioned concluded that CBR is a suitable technology for retaining knowledge learned from experience for future reuse. In fact, in our approach we will base on the CBR process to reuse users’ experiences during the development of data warehouse based on the CWM in terms of viewpoints. B. Our proposal CBR approach in CWM Our approach of integrating viewpoint into the CWM standard aimed to capture and formalize knowledge taken by analysts during the exploitation of a decisional process based on CWM; the approach interest allowed also to keep trace of reasoning and the main decisions taken by the analyst to
ensure coordination and comprehensibility between the different users. Then, through the formalization of the viewpoints of the different analysts of a multi-viewpoint analysis, we will be able to reuse experiences gathered during the development of data warehouses in terms of viewpoints. After the definition of our viewpoint metamodel by the extension of the CWM [4], we will reuse the successful users’ experiences gathered during the exploitation of the decisional process based on CWM in terms of viewpoint.
analysis) and by using the relations defined previously (equivalence, inclusion and independence). Our base case will contain structured and detailed annotations of metadata about the successful experiences of CWM’s users gathered along the exploitation of the decisional process.
Retrieval: according to the new defined viewpoint, the CBR system retrieves, from the base case, previous viewpoints that are fairly similar to the new case; here the main issue is the similarity measurement to define how the CBR process compares the new case with prior ones.
Reuse the selected case from the base case; Most of the time, the solution of a retrieved case that fulfills the requirements of the new problem has to be adapted.
Adaptation by modifying the old case to conform to the new one result in a proposed case, for that aim various adaptation techniques will be used [17]; or, Revise the retrieved case solution in the context of the new case by identifying the differences between the retrieved and the current case; and identifying the part of a retrieved case that can be transferred to the new case.
Retention: Once a satisfactory solution is reached, the CBR system can learn the new case by its incorporation into the case base. This new learned case increases the CBR system’s effectiveness by enlarging its coverage of the problem and solution spaces.
Fig 3. Example of the general architecture of our CBR process
We take as an example the Warehouse Developer’s viewpoint in a decisional process who is interested in building the logical design of the data warehouse and loading ETL applications (Fig. 3). The Warehouse Developer starts by defining his viewpoint. Our case base contained the structured and detailed annotations of the successful experiences gathered during the exploitation of the decisional process by the CWM’s users (preferences, objectives, context, the selected attributes…), those annotations are conforming to the viewpoint metamodel defined previously. The tools of the case based reasoning allow searching similar case of the decisional process basing on the definition of the users’ viewpoints (preference, context, and objective of the analysis) and on the relations between viewpoints previously defined (equivalence, inclusion and independence). Figure 4 illustrates the scenario of our CBR process:
The proposal CBR approach in CWM will allow to improve reusability of users’ analysis and experiences gathered along the exploitation of the data warehouse based on the CWM, and to guide novice users during the exploitation of the decisional process in terms of viewpoint. This is will be done by using the fourth phases of the CBR process: looking for similar cases in the case base (annotation base) basing on the definition of the users’ viewpoints and on the use of the defined relations between viewpoints, choosing the cases to reuse, adapting or revising cases, and learning the new cases by their incorporation into the case base. IV.
Fig. 3. Our CBR approach in CWM
Searching a similar case in the case base basing on the definition of the user’s viewpoint (preferences, context and objective of the
CONCLUSION
In this paper, we have at first introduced our viewpoint approach in the CWM, which was integrated by extending the CWM metamodel in the aim to assist users during the exploitation of the data warehouse. Secondly, with the interest to formalize interaction and interdependence between various analyses according to different viewpoints, we have presented some related works of viewpoints interactions that have allowed us to define a set of semantic relations between viewpoints. These relations allowed us to enhance coordination, knowledge sharing and mutual understanding between different users of a multi-view analysis, and reusability in terms of viewpoint of successful data warehouse experiences within a system. Finally, based on those relations,
we propose a case-based reasoning approach to improve reusability of users’ analysis and experiences gathered along the exploitation of the data warehouse based on the CWM, and to guide novice users during the exploitation of the decisional process in terms of viewpoint.
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