27,215 research outputs found
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
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
Integration of decision support systems to improve decision support performance
Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes
Automated schema matching techniques: an exploratory study
Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made
On-Demand Big Data Integration: A Hybrid ETL Approach for Reproducible Scientific Research
Scientific research requires access, analysis, and sharing of data that is
distributed across various heterogeneous data sources at the scale of the
Internet. An eager ETL process constructs an integrated data repository as its
first step, integrating and loading data in its entirety from the data sources.
The bootstrapping of this process is not efficient for scientific research that
requires access to data from very large and typically numerous distributed data
sources. a lazy ETL process loads only the metadata, but still eagerly. Lazy
ETL is faster in bootstrapping. However, queries on the integrated data
repository of eager ETL perform faster, due to the availability of the entire
data beforehand.
In this paper, we propose a novel ETL approach for scientific data
integration, as a hybrid of eager and lazy ETL approaches, and applied both to
data as well as metadata. This way, Hybrid ETL supports incremental integration
and loading of metadata and data from the data sources. We incorporate a
human-in-the-loop approach, to enhance the hybrid ETL, with selective data
integration driven by the user queries and sharing of integrated data between
users. We implement our hybrid ETL approach in a prototype platform, Obidos,
and evaluate it in the context of data sharing for medical research. Obidos
outperforms both the eager ETL and lazy ETL approaches, for scientific research
data integration and sharing, through its selective loading of data and
metadata, while storing the integrated data in a scalable integrated data
repository.Comment: Pre-print Submitted to the DMAH Special Issue of the Springer DAPD
Journa
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
Knowledge and Metadata Integration for Warehousing Complex Data
With the ever-growing availability of so-called complex data, especially on
the Web, decision-support systems such as data warehouses must store and
process data that are not only numerical or symbolic. Warehousing and analyzing
such data requires the joint exploitation of metadata and domain-related
knowledge, which must thereby be integrated. In this paper, we survey the types
of knowledge and metadata that are needed for managing complex data, discuss
the issue of knowledge and metadata integration, and propose a CWM-compliant
integration solution that we incorporate into an XML complex data warehousing
framework we previously designed.Comment: 6th International Conference on Information Systems Technology and
its Applications (ISTA 07), Kharkiv : Ukraine (2007
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