A Technique For Timeliness Measurement In Information Manufacturing System (IMS)

Timeliness is one of the major dimensions in the field of data quality. Freshness or obsoleteness of data is determined by timeliness data quality dimension. Generally, timeliness is calculated by currency and volatility. Currency is calculated by age, delivery time and input time. On the other side, volatility of data is the duration of the validity of data. Currency and volatility of IMS depend on the factors like refreshment period, waiting period of data in the system, expiry time of the data and the query response time for query requests. Therefore, development a technique for measuring the timeliness of data in IMS is the purpose of this paper

Container-Managed ETL Applications for Integrating Data in Near Real-Time

As the analytical capabilities and applications of e-business systems expand, providing real-time access to critical business performance indicators to improve the speed and effectiveness of business operations has become crucial. The monitoring of business activities requires focused, yet incremental enterprise application integration (EAI) efforts and balancing information requirements in real-time with historical perspectives. The decision-making process in traditional data warehouse environments is often delayed because data cannot be propagated from the source system to the data warehouse in a timely manner. In this paper, we present an architecture for a container-based ETL (extraction, transformation, loading) environment, which supports a continual near real-time data integration with the aim of decreasing the time it takes to make business decisions and to attain minimized latency between the cause and effect of a business decision. Instead of using vendor proprietary ETL solutions, we use an ETL container for managing ETLets (pronounced “et-lets”) for the ETL processing tasks. The architecture takes full advantage of existing J2EE (Java 2 Platform, Enterprise Edition) technology and enables the implementation of a distributed, scalable, near real-time ETL environment. We have fully implemented the proposed architecture. Furthermore, we compare the ETL container to alternative continuous data integration approaches

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

Striving towards Near Real-Time Data Integration for Data Warehouses

Abstract. The amount of information available to large-scale enterprises is growing rapidly. While operational systems are designed to meet well-specified (short) response time requirements, the focus of data warehouses is generally the strategic analysis of business data integrated from heterogeneous source systems. The decision making process in traditional data warehouse environments is often delayed because data cannot be propagated from the source system to the data warehouse in time. A real-time data warehouse aims at decreasing the time it takes to make business decisions and tries to attain zero latency between the cause and effect of a business decision. In this paper we present an architecture of an ETL environment for real-time data warehouses, which supports a continual near real-time data propagation. The architecture takes full advantage of existing J2EE (Java 2 Platform, Enterprise Edition) technology and enables the implementation of a distributed, scalable, near real-time ETL environment. Instead of using vendor proprietary ETL (extraction, transformation, loading) solutions, which are often hard to scale and often do not support an optimization of allocated time frames for data extracts, we propose in our approach ETLets (spoken “et-lets”) and Enterprise Java Beans (EJB) for the ETL processing tasks. 1

O processo de refrescamento nos sistemas de data warehouse: guião de modelação conceptual da tarefa de extracção de dados

Nos últimos anos, os Sistemas de Data Warehouse (SDW) têm sido os sistemas de apoio à decisão mais utilizados nas organizações, integrando dados de diferentes fontes nos Repositórios de Data Warehouse (RDW). Com o decorrer do tempo de funcionamento do sistema, coloca-se o problema do refrescamento, entendido como o problema de assegurar que os conteúdos dos RDW são periodicamente refrescados, de modo a reflectirem as alterações que ocorrem nos dados das fontes que lhes servem de base. Esta dissertação propõe uma abordagem que tem como objectivos principais tornar explícito e documentar o problema do refrescamento e apresentar um guião de modelação conceptual da tarefa de extracção de dados que possa enriquecer as fases subsequentes de desenho para a especificação formal do processo de refrescamento. São dois os contributos desta dissertação. Primeiro, providencia um quadro detalhado sobre o problema do refrescamento que inclui os conceitos e questões fundamentais que permitem caracterizar os SDW, na perspectiva das funcionalidades no apoio à decisão, das abordagens de integração de fontes de dados e dos componentes da arquitectura, os constrangimentos e tarefas que compreendem o processo de refrescamento, as principais abordagens disponíveis na literatura. Segundo, propõe um guião de apoio à modelação conceptual da tarefa de extracção de dados, com base na UML, apresentando os passos que devem ser seguidos pelo designer e disponibilizando as construções que permitem representar os dados que se extraem das fontes, de acordo com as regras que permitem isolar e extrair os dados relevantes para a tomada de decisão.Data Warehouse Systems (DWS) have become very popular in the last years for decision making, by integrating data from internal and external sources into data warehouse stores. As times advances and the sources from which warehouse data is integrated change, the data warehouse contents must be regularly refreshed, such that warehouse data reflect the state of the underlying data sources. This dissertation proposes an approach which main goals are to explicit and document the data warehouse refreshment problem and to present a guidelines for the conceptual modelling of data extraction in order to enrich the subsequent design steps for the formal specification of the refreshment process. The contributions of our approach are twofold. First, it provides a detailed outline of data warehouse refreshment problem, including the main concepts and issues that characterise the general domain of the DWS, such as decision making functionalities, data sources integration approaches and architecture and, the refreshment tasks and constraints as well as the main approaches. Second, it proposes a guidelines for an UML conceptual modelling of data extraction, by giving the sequence of steps for a designer to follow, the modelling constructs for the definition of extracting data, according to the rules that must be accomplished for extracting relevant data

A Goal and Ontology Based Approach for Generating ETL Process Specifications

Data warehouse (DW) systems development involves several tasks such as defining requirements, designing DW schemas, and specifying data transformation operations. Indeed, the success of DW systems is very much dependent on the proper design of the extracting, transforming, and loading (ETL) processes. However, the common design-related problems in the ETL processes such as defining user requirements and data transformation specifications are far from being resolved. These problems are due to data heterogeneity in data sources, ambiguity of user requirements, and the complexity of data transformation activities. Current approaches have limitations on the reconciliation of DW requirement semantics towards designing the ETL processes. As a result, this has prolonged the process of the ETL processes specifications generation. The semantic framework of DW systems established from this study is used to develop the requirement analysis method for designing the ETL processes (RAMEPs) from the different perspectives of organization, decision-maker, and developer by using goal and ontology approaches. The correctness of RAMEPs approach was validated by using modified and newly developed compliant tools. The RAMEPs was evaluated in three real case studies, i.e., Student Affairs System, Gas Utility System, and Graduate Entrepreneur System. These case studies were used to illustrate how the RAMEPs approach can be implemented for designing and generating the ETL processes specifications. Moreover, the RAMEPs approach was reviewed by the DW experts for assessing the strengths and weaknesses of this method, and the new approach is accepted. The RAMEPs method proves that the ETL processes specifications can be derived from the early phases of DW systems development by using the goal-ontology approach