Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML

Investigating pluralistic data architectures in data warehousing

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonUnderstanding and managing change is a strategic objective for many organisations to successfully compete in a market place; as a result, organisations are leveraging their data asset and implementing data warehouses to gain business intelligence necessary to improve their businesses. Data warehouses are expensive initiatives, one-half to two-thirds of most data warehousing efforts end in failure. In the absence of well-formalised design methodology in the industry and in the context of the debate on data architecture in data warehousing, this thesis examines why multidimensional and relational data models define the data architecture landscape in the industry. The study develops a number of propositions from the literature and empirical data to understand the factors impacting the choice of logical data model in data warehousing. Using a comparative case study method as the mean of collecting empirical data from the case organisations, the research proposes a conceptual model for logical data model adoption. The model provides a framework that guides decision making for adopting a logical data model for a data warehouse. The research conceptual model identifies the characteristics of business requirements and decision pathways for multidimensional and relational data warehouses. The conceptual model adds value by identifying the business requirements which a multidimensional and relational logical data model is empirically applicable

Application of knowledge-based system in automated data warehouse design

Data warehouse has become more and more popular for an enterprise as a data repository system.Yet tools to appropriately design its conceptual model are rarely available, even though this model is known as a key for the successful of the overall design. In this paper we propose an approach and a tool to guide the decision makers in designing data warehouse conceptual model based on the Entity Relationship (ER) model of the existing operational database systems. Using this approach, the ER model is automatically transformed into the multidimensional model

Design of a Multidimensional Model Using Object Oriented Features in UML

A data warehouse is a single repository of data which includes data generated from various operational systems. Conceptual modeling is an important concept in the successful design of a data warehouse. The Unified Modeling Language (UML) has become a standard for object modeling during analysis and design steps of software system development. The paper proposes an object oriented approach to model the process of data warehouse design. The hierarchies of each data element can be explicitly defined, thus highlighting the data granularity. We propose a UML multidimensional model using various data sources based on UML schemas. We present a conceptual-level integration framework on diverse UML data sources on which OLAP operations can be performed. Our integration framework takes into account the benefits of UML (its concepts, relationships and extended features) which is more close to the real world and can model even the complex problems easily and accurately. Two steps are involved in our integration framework. The first one is to convert UML schemas into UML class diagrams. The second is to build a multidimensional model from the UML class diagrams. The white-paper focuses on the transformations used in the second step. We describe how to represent a multidimensional model using a UML star or snowflake diagram with the help of a case study. To the best of our knowledge, we are the first people to represent a UML snowflake diagram that integrates heterogeneous UML data sources

Differentiated Multiple Aggregations in Multidimensional Databases

International audienceMany models have been proposed for modeling multidimensional data warehouse and most consider a same function to determine how measure values are aggregated according to different data detail levels. We provide a conceptual model that supports (1) multiple aggregations, associating to the same measure a different aggregation function according to analysis axes or hierarchies, and (2) differentiated aggregation, allowing specific aggregations at each detail level. Our model is based on a graphical formalism that allows controlling the validity of aggregation functions (distributive, algebraic or holistic). We also show how conceptual modeling can be used, in an R-OLAP environment, for building lattices of pre-computed aggregates

OLAP in Multifunction Multidimensional Database

International audienceMost models proposed for modeling multidimensional data warehouses consider a same function to determine how measure values are aggregated. We provide a more flexible conceptual model allowing associating each measure with several aggregation functions according to dimensions, hierarchies, and levels of granularity. This article studies the impacts of this model on the multidimensional table (MT) and the OLAP algebra [11]. It shows how the MT can handle several aggregation functions. It also introduces the changes of the internal mechanism of OLAP operators to take into account several aggregation functions especially if these functions are non-commutative

LEVERAGING SOCIAL NETWORK DATA FOR ANALYTICAL CRM STRATEGIES - THE INTRODUCTION OF SOCIAL BI

The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers? characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed

Examining Model of Effect of Adopting Information Technology and Relationship Marketing toward True Loyalty through Multidimensional Commitment

Nowadays, business with very tough competition is increasingly difficult to build loyalty. This phenomenon is the object of this research. The purpose of this study was to test the conceptual model of the effect of the adoption of information technology and relationship marketing to true loyalty (empirical studies of bank customers in Central Java). This is a survey typed research. The data used are primary and secondary data by taking bank customer as the object of research. Data collection instruments in the form of a list of questions (questionnaire) either by open or closed questions. Data analysis methods used include 1) instrument test analysis that is validity and reliability test, 2) descriptive statistical analysis, 3) SEM (Sequantial Equation Model) analysis. Theoretical approach used in the research are Behavioral Intention and Attribution Intention. The results showed that: 1) The conceptual model testing of the effect of adoption of information technology and relationship marketing toward true loyalty (repurchase intention and advocacy intention) through customer satisfaction and multidimensional organizational commitment (affective commitment, continuance commitment and normative commitment) using the SEM analysis, shows the model meets the criteria Goodness of fit, 2) Square Multiple Correlation (coefficient of determination) model for Repurchase Intention is of 0.808 which means the Repurchase Intention variability that can be explained by the variability of the adoption of information technology, relationship marketing, customer satisfaction, multidimensional organizational commitment (affective commitment, continuance commitment and normative commitment) is of 80,8% or Advocacy Intention of 0.995 which means Advocacy Intention variability that can be explained by the variability of the adoption of information technology, relationship marketing, customer satisfaction, multidimensional organizational commitment (affective commitment, continuance commitment and normative commitment) is of 99, 5%. This claimed that the conceptual model being tested is valid. 3) Allen and Meyer's theoretical approach (Planned Behavioral Theory), which point on the individual's relationship with organization, strongly supports the concept of marketing to build true loyalty. The main finding of this study is that to build true loyalty, building a strong relationship between the individual and the organization are needed. The relationship can be done by optimizing the use of information technology approach. Keywords: Adoption of Information Technology, Relationship Marketing, True Loyalty (Repurchase Intention, Advocacy Intention), Multidimensional Commitmen