Warehouse Model Research Articles

An Inventory Model with Time-Dependent Demand and Limited Storage Facility under Inflation

The main objective of this paper is to develop a two-warehouse inventory model with partial backordering and Weibull distribution deterioration. We consider inflation and apply the discounted cash flow in problem analysis. The discounted cash flow (DCF) and optimization framework are presented to derive the optimal replenishment policy that minimizes the total present value cost per unit time. When only rented or own warehouse model is considered, the present value of the total relevant cost is higher than the case when two-warehouse is considered. The results have been validated with the help of a numerical example. Sensitivity analysis with respect to various parameters is also performed. From the sensitivity analysis, we show that the total cost of the system is influenced by the deterioration rate, the inflation rate, and the backordering ratio.

Extending UML for trajectory data warehouses conceptual modelling

The new positioning and information capture technologies are able to treat data related to moving objects taking place in targeted phenomena. This gave birth to a new data source type called trajectory data (TD) which handle information related to moving objects. Trajectory Data must be integrated in a new data warehouse type called trajectory data warehouse (TDW) that is essential to model and to implement in order to analyze and understand the nature and the behavior of movements of objects in various contexts. However, classical conceptual modeling does not incorporate the specificity of trajectory data due to the complexity of their components that are spatial, temporal and thematic (semantic). For this reason, we focus in this paper on presenting the conceptual modeling of the trajectory data warehouse by defining a new profile using the StarUML extensibility mechanism. Keywords—Trajectory data; trajectory data warehouse; UML extension; trajectory UML profile.

PERSISTENT STAGING AREA MODELS FOR DATA WAREHOUSES

The paper’s scope is conceptual modeling of the Data Warehouse (DW) staging area seen as a permanent system of records of interest to regulatory/auditing compliance. The context is design of enterprise data warehouses compliant with the next generation of DW architecture that is DW 2.0. We address temporal DW aspect and review relevant alternative models i.e. compliant with DW 2.0, namely the Data Vault (DV) and the Anchor Model (AM). The principal novelty is a Conceptual DV Model and its comparison with the Anchor Model.

Transforming of Construction Contract Administration: Data Warehouse Modeling

Construction stakeholders always use standard form of contract to regulate their contractual obligations and expectations during contract administration process. They need to refer a vast of contract provisions in the process. It always leads misunderstanding and interpretation errors as most of construction practitioners are without a proper legal background. Hence the research aims to apply data warehouse technology to assist end-users for better and/or right decision making. A conceptual model of data warehouse for contract administration is developed and verified by twelve reputed experts in the construction industry. The results show that the data warehouse modeling is feasible and practical to the construction practitioners. It would render a more effective and proactive approach in contract administration towards dispute resolution or prevention.

A practical application of our MDD approach for modeling secure XML data warehouses

Data warehouses are systems that provide useful information to support the decision making process, thus improving organizations' business processes. These systems integrate heterogeneous sources which are not only limited to their internal business data but also include data from the Web, the latter of which have become increasingly more important in the decision making process in recent years. This has motivated the extensive use of XML in the implementation of data warehouses, in a manner which facilitates data and metadata interchange among the heterogeneous data sources from the Web and the data warehouse. However, the business information that data warehouses manage is crucial and highly sensitive, and must be carefully protected. Security is thus a key issue in the design of data warehouses, regardless of the implementation technology used. It is important to note that the data available on the Web requires particular security considerations which have been specifically tailored to these systems in order to permit their particularities to be captured correctly. Unfortunately, although security issues have been considered in the development of traditional data warehouses, current research lacks approaches with which to consider security when the target platform is based on XML technology.In order to deal with this situation, in this paper we propose a methodological approach for the model driven development of secure XML data warehouses. We also specify a set of transformation rules that are able to automatically generate not only the corresponding XML structure of the data warehouse from secure conceptual data warehouse models, but also the security rules specified within the data warehouse XML structure, thus allowing both aspects to be implemented simultaneously. We additionally introduce our secure XML DW development approach, in which the secure conceptual DW data model, the PIM, is transformed into a secure XML DW, as a PSM, by applying a set of transformation rules. Our proposal is validated through the practical application of our model driven development approach for Modeling Secure XML Data Warehouses to a case study, which is based on a central Airport DW. We first describe the transformation rules defined, then use a step by step illustration to show how they will be applied to the secure conceptual model of the case study to obtain the Secure XML Data Warehouse, thus demonstrating the benefits of our proposal, and finally we analyze how to achieve the secure implementation into commercial database management systems, providing details of the secure implementation in Oracle XML DB 11g.

InfoNetOLAPer

To support efficient graph OLAP operations on information networks, we propose two significant intermediate infrastructures: InfoNetWarehouse and InfoNetCube. InfoNetWarehouse is designed with novelty to be the warehouse model for information networks, which provides topic-oriented, integrated, and multi-dimensional organizational solutions for Information networks. InfoNetCube is our proposed datacube implemention that serves the OLAP of information networks. We further integrate the two infrastructures with InfoNetOLAP module into a prototype called InfoNetOLAPer, which has the following noteworthy features: (1) The basic InfoNetWarehouse schema is well implemented based on SQL Server 2000, (2) InfoNetCube improves the efficiency of InfoNetOLAP by the pre-computation of InfoNetLattice, and (3) InfoNetOLAPer supports efficient I-OLAP and T-OLAP operations.

Building a Diabetes Screening Population Data Repository Using Electronic Medical Records

There has been a rapid advancement of information technology in the area of clinical and population health data management since 2000. However, with the fast growth of electronic medical records (EMRs) and the increasing complexity of information systems, it has become challenging for researchers to effectively access, locate, extract, and analyze information critical to their research. This article introduces an outpatient encounter data framework designed to construct an EMR-based population data repository for diabetes screening research. The outpatient encounter data framework is developed on a hybrid data structure of entity-attribute-value models, dimensional models, and relational models. This design preserves a small number of subject-specific tables essential to key clinical constructs in the data repository. It enables atomic information to be maintained in a transparent and meaningful way to researchers and health care practitioners who need to access data and still achieve the same performance level as conventional data warehouse models. A six-layer information processing strategy is developed to extract and transform EMRs to the research data repository. The data structure also complies with both Health Insurance Portability and Accountability Act regulations and the institutional review board's requirements. Although developed for diabetes screening research, the design of the outpatient encounter data framework is suitable for other types of health service research. It may also provide organizations a tool to improve health care quality and efficiency, consistent with the "meaningful use" objectives of the Health Information Technology for Economic and Clinical Health Act.

Comparing the understandability of alternative data warehouse schemas: An empirical study

An easily understood data warehouse model enables users to better identify and retrieve its data. It also makes it easier for users to suggest changes to its structure and content. Through an exploratory, empirical study, we compared the understandability of the star and traditional relational schemas. The results of our experiment contradict previous findings and show schema type did not lead to significant performance differences for a content identification task. Further, the relational schema actually led to slightly better results for a schema augmentation task. We discuss the implications of these findings for data warehouse design and future research.

Modeling, Design and Implementation of Graph OLAPing

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Automatic warehouse modelling and simulation

The Newspaper Production/Distribution Problem (NDP) involves a perishable good. Newspaper publishers cannot print the news sections of newspapers in advance, as they must be up to date. Additionally, delivery must be completed before morning commuting starts in metropolitan areas. Therefore, delivery delays at the printing plant are not acceptable, as they cause significant disturbances to the distribution chain. In this paper, we analyse the impact of using a second transelevator on the performance of a printing plant's automatic warehouse with the aim to avoid delivery delays to the printing press.

Assessment of quality of data warehouse multidimensional model

Data warehouses are large repositories designed to enable the knowledge workers to take better and faster decisions. Due to its significance in strategic decisions, there is a need to assure data warehouse quality. One of the factors affecting the data warehouse quality is multidimensional model quality. Although there are some useful guidelines for designing good multidimensional data models, but objective indicators, i.e., metrics are needed to help designers to develop quality multidimensional models. Few researchers have proposed quality metrics for multidimensional models for data warehouse. These metrics need to be theoretically as well as empirically validated in order to prove their practical utility. In this paper, empirical validation using controlled experiment is carried out. We not only evaluate the effect of individual metric but also evaluate the effect of various combinations of metrics on data warehouse model quality specifically understandability, in order to best explain the variance of dependent variable due to independent variables. The results show that these metrics may be used as objective indicators for understandability. Finally, accuracy of our model in predicting the multidimensional model quality is also evaluated.

‘Nothing is really safe’: a focus group study on the processes of anonymizing and sharing of health data for research purposes

The availability of anonymized data is a keystone of medical research, yet little is known about lay views towards the process of anonymization or on the way that anonymized medical data are transferred to researchers. During May and June 2009, as part of a wider consultation on methods for releasing data to researchers, three focus groups (n = 19) were conducted exploring lay attitudes towards the traditional 'warehouse' model commonly used in medical research for delivering anonymized National Health Service (NHS) data to researchers. The focus groups explored different processes such as the copying of data, use of programmers for linkage and anonymization, the transfer of data and governance. The recognition of the positive aspects of medical research and desire to support it formed the context for discussions. Nonetheless, individuals varied in their attitudes to the use of anonymized data extracts for research from their health records (without consent); although some appeared positive wanted to be asked to consent for this use. Furthermore, participants were acutely aware of security breaches of NHS information nevertheless, they continued to display a high level of trust in NHS staff. Participants were concerned about the practicalities of the warehouse model and relied on their own life experiences to make sense of the model (using analogies with 'banks' or 'libraries'). The general attitude towards the processes underlying the warehouse model might best be captured by the term 'ambivalence'. This research (1) offers unique insights into views of anonymization of health data extracts, how it is undertaken and data are transferred and (2) adds to an increasing body of work that demonstrates that a minority of individuals are concerned about consent, even when data are anonymized although (3) those concerned about anonymization do not necessarily seek resolution through gaining consent.

A Multidimensional Model for Data Warehouses of Simulation Results

This paper examines the multidimensional modeling of a data warehouse for simulation results. Environmental dynamics modeling is used to study complex scenarios like urbanization, climate change and deforestation while allowing decision makers to understand and predict the evolution of the environment in response to potential value changes in a large number of influence variables. In this context, exploring simulation models produces a huge volume of data, which must often be studied extensively at different levels of aggregation due to there being a great need to define tools and methodologies specifically adapted for the storage and analysis of such complex data. Data warehousing systems provide technologies for managing simulation results from different sources. Moreover, OLAP technologies allow one to analyze and compare these results and their corresponding models. In this paper, the authors propose a generic multidimensional schema to analyze the results of a simulation model, which can guide modelers in designing specific data warehouses, and an adaptation of an OLAP client tool to provide an adequate visualization of data. As an example, a data warehouse for the analysis of results produced from a savanna simulation model is implemented using a Relational OLAP architecture.

센서를 이용한 웨어하우스 관리 온톨로지 모델 구축

유비쿼터스 기술개발에 따라 농산물을 저장 및 관리를 주 기능으로 하는 창고시스템의 관리는 효과적으로 이루어지고 있다. 이에 본 논문에서는 유비쿼터스 센서를 사용하여 농산물을 저장하고 관리하기 위한 목적으로 온톨로지 기반 상황 인지 시스템 모델을 제안하여 정보를 공유하고 분산하도록 한다. 오늘날 우리 생활속에 유비쿼터스 기술이 확산되어짐에 따라 정보에 대한 중요성은 점진적으로 증가하고 있다. 뿐만 아니라, 한 도메인에서 온톨로지의 중요성 또한 중요해지고 있다. 그러므로 본 논문에서는 센서와 같은 유비쿼터스 장치로부터 데이터를 획득하는데 초점을 두고 기술하고 있으며, 온톨로지 기반 농수산물 창고 모델을 설계하고 구축한것을 보인다. According to the ubiquitous technology development, managing of the warehouse system which stores and manages agriculture products is realized effectively. This paper proposes an ontology-based context aware system model for the purpose of storing and managing agricultural products using ubiquitous sensors to share and distribute information. In these days, owing to penetrating ubiquitous technologies into our way of life, the importance of information is increasing gradually. The importance of ontology in a domain is getting as well. Therefore, this paper focuses on describing to get data from ubiquitous devices such as sensors, and designs and build an ontology-based agricultural product warehouse model.

Fragmenting very large XML data warehouses via K-means clustering algorithm

XML data sources are more and more gaining popularity in the context of a wide family of Business Intelligence (BI) and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing semi-structured and complex multidimensional data. As a consequence, many XML data warehouse models have been proposed during past years in order to handle hetero-geneity and complexity of multidimensional data in a way traditional relational data warehouse approaches fail to achieve. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore , recent research efforts are focusing the attention on fragmentation techniques, which are able to overcome the limitations above. Derived horizontal fragmentation is already used in relational data warehouses, and can definitely be adapted to the XML context. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses, and, with more emphasis, distributed data warehouse architectures. Inspired by this research challenge, in this paper we propose the use of K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses, and, at the same time, completely controlling and determining the number of originated fragments via adequately setting the parameter K. We complete our analytical contribution by means of a comprehensive experimental assessment where we compare the efficiency of our proposed XML data warehouse fragmentation technique against those of classical derived horizontal fragmentation algorithms adapted to XML data warehouses.

Ohio Regional Depositories: Moving from Warehousing Separate Collections to Servicing Shared Collections

ABSTRACT In Ohio, the space needs of campus libraries are changing just as book depositories are filling up. In response to this situation, the university libraries are re-examining the depositories to see if the space can be used even more efficiently, the funding distributed more predictably, and the services standardized and expanded. The Northeastern Ohio Cooperative Regional Library Depository has a plan to rethink the space in the depository, redefine the collection housed there in terms of ownership and access, and to repurpose the depository from a warehouse model to a service-centered model. The Northeastern Ohio Regional Library Depository plan and its anticipated role in changing the way that contributing libraries statewide assess depository collections and provide for access to those collections is the focus of this paper.

An Adaptive Approach to Schema Classification for Data Warehouse Modeling

Data warehouse (DW) modeling is a complicated task, involving both knowledge of business processes and familiarity with operational information systems structure and behavior. Existing DW modeling techniques suffer from the following major drawbacks -- data-driven approach requires high levels of expertise and neglects the requirements of end users, while demand-driven approach lacks enterprise-wide vision and is regardless of existing models of underlying operational systems. In order to make up for those shortcomings, a method of classification of schema elements for DW modeling is proposed in this paper. We first put forward the vector space models for subjects and schema elements, then present an adaptive approach with self-tuning theory to construct context vectors of subjects, and finally classify the source schema elements into different subjects of the DW automatically. Benefited from the result of the schema elements classification, designers can model and construct a DW more easily.

Web Data Warehousing Convergence

This article proposes a data warehouse integration technique that combines data and documents from different underlying documents and database design approaches. The well-defined and structured data such as relational, object-oriented and object relational data, semi-structured data such as XML, and unstructured data such as HTML documents are integrated into a Web data warehouse system. The user specified requirements and data sources are combined to assist with the definitions of the hierarchical structures, which serve specific requirements and represent a certain type of data semantics using object-oriented features including inheritance, aggregation, association, and collection. A conceptual integrated data warehouse model is then specified based on a combination of user requirements and data source structure, which creates the need for a logical integrated data warehouse model. A case study is then developed into a prototype in a Web-based environment that enables the evaluation. The evaluation of the proposed integration Web data warehouse methodology includes the verification of correctness of the integrated data, and the overall benefits of utilizing this proposed integration technique.

Referenzmodellierung im Data-Warehousing — State-of-the-Art und konfigurative Ansätze für die Fachkonzeption

Reference models increase the efficiency of data warehouse projects by providing construction patterns. This paper presents an overview of existing applications of reference models for data warehousing which shows that there is only insufficient support of model alternatives during requirements definition. Especially configurable reference models provide an adequate solution for creating project-specific models. Therefore, we suggest an extension of data warehouse modeling techniques by configuration rules. The configuration of reference models is embedded in the data warehouse development process. Furthermore, supplementary operational instructions for reference model designers are outlined.

Validating metrics for data warehouses

Organisations are adopting data warehouses to manage information efficiently as the main organisational asset. This success of data warehouses (DW) can be explained because a data warehouse is a set of data and technologies aimed at enabling the executives, managers and analysts to make better and faster decisions. Due to the principal role of data warehouses in taking strategic decisions, quality is fundamental. One of the most important factors that affects the quality of the final system is its design. Although in recent years different authors have proposed some useful guidelines to design a data warehouse, more objective indicators are needed to help designers and managers to develop quality data warehouses. A set of metrics for data warehouse models is presented, and an empirical validation is carried out in order to prove practically their usefulness as quality indicators.