Modeling views in the layered view model for XML using UML

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

AOSD Ontology 1.0 - Public Ontology of Aspect-Orientation

This report presents a Common Foundation for Aspect-Oriented Software Development. A Common Foundation is required to enable effective communication and to enable integration of activities within the Network of Excellence. This Common Foundation is realized by developing an ontology, i.e. the shared meaning of terms and concepts in the domain of AOSD. In the first part of this report, we describe the definitions of an initial set of common AOSD terms. There is general agreement on these definitions. In the second part, we describe the Common Foundation task in detail

Modeling ontology views: An abstract view model for semantic web

The emergence of Semantic Web (SW) and the related technologies promise to make the web a meaningful experience. However, high level modelling, design and querying techniques proves to be a challenging task for organizations that are hoping to utilize the SW paradigm for their industrial applications. To address one such issue, in this paper, we propose an abstract view model with conceptual extensions for the SW. First we outline the view model, its properties and some modelling issues with the help of an industrial case study example. Then, we provide some discussions on constructing such views (at the conceptual level) using a set of operators. Later we provide a brief discussion on how such this view model can utilized in the MOVE [1] system, to design and construct materialized Ontology views to support Ontology extraction

Integration of Data Mining and Data Warehousing: a practical methodology

The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are difficult to analyze and require effective visual exploration that conveys semantic relationships of data. Traditional data mining techniques such as clustering clusters only the numeric data. Little research has been carried out in tackling the problem of clustering high cardinality nominal variables to get better insight of underlying dataset. Several works in the literature proved the likelihood of integrating data mining with warehousing to discover knowledge from data. For the seamless integration, the mined data has to be modeled in form of a data warehouse schema. Schema generation process is complex manual task and requires domain and warehousing familiarity. Automated techniques are required to generate warehouse schema to overcome the existing dependencies. To fulfill the growing analytical needs and to overcome the existing limitations, we propose a novel methodology in this paper that permits efficient analysis of mixed numeric and nominal data, effective visual data exploration, automatic warehouse schema generation and integration of data mining and warehousing. The proposed methodology is evaluated by performing case study on real-world data set. Results show that multidimensional analysis can be performed in an easier and flexible way to discover meaningful knowledge from large datasets

Adding semantic modules to improve goal-oriented analysis of data warehouses using I-star

The success rate of data warehouse (DW) development is improved by performing a requirements elicitation stage in which the users’ needs are modeled. Currently, among the different proposals for modeling requirements, there is a special focus on goal-oriented models, and in particular on the i* framework. In order to adapt this framework for DW development, we previously developed a UML profile for DWs. However, as the general i* framework, the proposal lacks modularity. This has a specially negative impact for DW development, since DW requirement models tend to include a huge number of elements with crossed relationships between them. In turn, the readability of the models is decreased, harming their utility and increasing the error rate and development time. In this paper, we propose an extension of our i* profile for DWs considering the modularization of goals. We provide a set of guidelines in order to correctly apply our proposal. Furthermore, we have performed an experiment in order to assess the validity our proposal. The benefits of our proposal are an increase in the modularity and scalability of the models which, in turn, increases the error correction capability, and makes complex models easier to understand by DW developers and non expert users.This work has been partially supported by the ProS-Req (TIN2010-19130-C02-01) and by the MESOLAP (TIN2010-14860) and SERENIDAD (PEII-11-0327-7035) projects from the Spanish Ministry of Education and the Junta de Comunidades de Castilla La Mancha respectively. Alejandro Maté is funded by the Generalitat Valenciana under an ACIF grant (ACIF/2010/298)

Towards a framework for developing visual analytics in supply chain environments

Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework's applicability using an actual usage scenario and left the implementation of the system for future work

Towards a framework for developing visual analytics in supply chain environments

Visual Analytics (VA) has shown to be of significant importance for Supply Chain (SC) analytics. However, SC partners still have challenges incorporating it into their data-driven decision-making activities. A conceptual framework for the development and deployment of a VA system provides an abstract, platform-independent model for the whole process of VA, covering requirement specification, data collection and pre-processing, visualization recommendation, visualization specification and implementation, and evaluations. In this paper, we propose such a framework based on three main aspects: 1) Business view, 2) Asset view, and 3) Technology view. Each of these views covers a set of steps to facilitate the development and maintenance of the system in its context. The framework follows a consistent process structure that comprises activities, tasks, and people. The final output of the whole process is the VA as a deliverable. This facilitates the alignment of VA activities with business processes and decision-making activities. We presented the framework\u27s applicability using an actual usage scenario and left the implementation of the system for future work

A Critical Study of requirement gathering and testing techniques for datawarehousing

ABSTRACT In light of high cost and higher rate of failure of Datawarehousing projects, it becomes imperative to study software processes being followed for Datawarehousing. In this paper we present a survey of literature for Datawarehousing requirement gathering and testing. This paper has analyzed drawbacks of traditional techniques for requirement gathering and testing of Datawarehouse. We have reported areas where more research needs to be focused. Using text analytics technique called "word cloud", we have analyzed main areas being researched and shown areas that need more focus. This paper can give a direction to future research in the areas of Datawarehouse requirement gathering and testing