Warehouse Requirements Research Articles

A novel metric for assessing structural complexity of data warehouse requirements models

Metrics can be used to evaluate the overall structural complexity of the data warehouse (DW) and assess its quality in the early phases of design and development. The requirements data model plays a crucial role in developing a quality-based DW. Several researchers have assessed the quality of the requirements data model using the requirements metrics based on the agent-goal-decision-information (AGDI) model, both formally and empirically. However, no assessment has been implemented on the structural complexities of the requirements data model based on goal, decision, and information hierarchies. Considering this, the study proposes a novel structural complexity metric to assess the overall complexity of the requirements data model at different phases (information, decision and organisation) and to analyze the links between the different levels of granularity in these models. The proposed metric is formally validated by applying the measurement theory-based Briand’s framework for complexity measure (i.e., one of the five notions of measurements in Software Engineering) by confirming that the proposed metric is valid and defined correctly under certain mathematical properties. As a result, we conclude that the proposed structural complexity metric is accurately defined and effectively valid. We also provide a detailed comparison of various requirements metrics commonly used in similar contexts to contextualize our proposed metric within the broader landscape of existing methodologies. Additionally, we have included comprehensive guidelines and practical examples for applying the proposed structural complexity metric in DW development scenarios to enhance its utility among practitioners. The proposed metric can be utilized to ensure the accuracy of the requirements model, thereby improving the overall quality of DW.

ОБОСНОВАНИЕ КОМПОНОВКИ РЕФРЕЖИРАТОРНОГО СКЛАДА

The article is devoted to the issues of justifying the layout of a refrigerated warehouse. This takes into account the peculiarity of the refrigerated warehouse associated with the need to comply with the temperature regime, indicators of the efficiency of technological processes of the warehouse and other requirements necessary for storing perishable goods.

DAREF: MDA framework for modelling data warehouse requirements and deducing the multidimensional schema

Nowadays, the growing importance of modelling in software engineering is without a doubt reinforced by the blossoming of model-driven architecture (MDA). In this trend, MDA could be considered the most convenient approach to integrate the modelling process in data warehousing projects. On the other hand, decision-makers are usually unable to express their business needs in a concise way that allows getting a valid data warehouse (DW), mainly due to the lack of standard methodologies and tools devoted to supporting this situation. This fact might expand the gap between the business world and the IT world and causes troublesome difficulties to interpret and model DW requirements. Moreover, applying MDA for this kind of project requires using new tools to avoid this drawback. In this paper, we provide an MDA framework to design DW requirements and generate afterwards the multidimensional schema. The framework is based on UML profiles and presents to decision-makers a graphical tool for modelling their strategic visions in order to build the system-to-be. Besides, the proposal allows for dealing with data historization and metadata in the generated multidimensional model to perform properly the extract transform load process.

PCN518 ONCOLOGY REAL WORLD DATA (RWD): ENABLING LEARNING HEALTH SYSTEMS

Unprecedented advances in oncology innovation and precision medicine are increasing treatment complexity. Sharing Real-World Data (RWD) on routine practice systematically is critical to help navigate this. This abstract aims to describe how this goal can be realized. It draws lessons from a collaborative European data-sharing platform developed to inform best practice, highlight variations in care, catalyze research and help address financial sustainability challenges. Through a collaborative approach with patients, clinicians, biopharmaceutical companies and cancer organizations, IQVIA, supported by the Collaboration for Oncology Data in Europe (CODE), assessed the infrastructural and data requirements of a data-sharing platform to meet oncology stakeholders’ needs. Data protection authorities were consulted to understand data processing and warehouse requirements. Methodologies to address all requirements were then identified. Pre-requisites identified included: low latency data access, comparability, sufficient clinical depth, support and involvement of, and minimal administrative burden to the clinical community, GDPR alignment, and flexibility to facilitate different payment models. The Oncology Data Network (ODN) was constructed using ‘data protection by design,’ aligning with the EU GDPR. The ODN will provide analytically ready data in <24 hours on a concise set of variables including clinical & demographic characteristics, biomarkers, indication of use, and detailed drug utilization including treatment plan, dispensing and administration for all anti-cancer medicines longitudinally in a patients’ medical pathway across settings of care. 115 European sites in 6 countries have joined, representing approximately 80,000 patients receiving anti-cancer therapy. The mapping of 28,000 distinct anti-cancer regimens enables comparability. RWD from the ODN will enable us to generate timely insights and assess utilization of anti-cancer medicines, prescribing patterns, adherence to guidelines, bench marking data across countries, and potentially develop innovative agreements to ultimately improve access for patients.

Automated or manual storage systems: do throughput and storage capacity matter?

Selecting the appropriate type of capital-intensive storage systems is an important decision for warehouse managers. However, such a decision is complex due to various available storage systems. In addition, warehouse requirements such as storage capacity and throughput influence this decision. This research provides insights that enable managers to select the suitable type of storage system which minimizes the investment and operational costs while the warehouse design requirements, in particular the storage capacity and throughput, are met. To obtain these insights, an Excel®-based decision support system is developed for a set of most common types of manual and crane-based automated storage systems in pallet and case warehouses. The decision support system uses the closed-form formulas from the warehousing literature and also Monte Carlo simulation to approximate the travel time in each storage system. The results show that the choice of automated or manual storage system and the associated costs depend on the required capacity and throughput. When the storage capacity and throughput are low, the manual pallet racks are the preferred storage system and incur the lowest costs. As the storage capacity and throughput increase, there is a need for more compact storage systems that can store more loads in a smaller footprint. Thus, for medium to high capacity levels, double-deep automated storage systems and deep-lane compact storage systems are the ones with the lowest investment and operational costs. The results for the case warehouses show that the investment and operational costs increase rapidly with an increase of the throughput. In particular, the increase is noticeable for operational costs of the shelf rack system and the investment cost of the miniload system where the storage capacity and throughput level are high.

A multifactor approach for elicitation of Information requirements of data warehouses

Whereas requirements engineering for transactional systems aims to discover the functionality of the system-to-be, data warehouse requirements engineering aims to discover the Information contents of the data-warehouse-to-be. Though notions of goals, Decisions, business processes, business events have been used to set the context for Information discovery, the move from these to obtain the relevant Information is largely ad hoc, unguided, and does not provide traceability of Information. We propose four elicitation techniques that are inferred from manager concerns during Decision making and that provide guidance and traceability. These form a suite such that each augments the set of already discovered Information. Consequently, the possibility of missing requirements is reduced, thereby making for more effective requirements engineering.

CIM for data warehouse requirements using an UML profile

Different modelling approaches have been proposed to automate the process of design and development of data warehouse. Some of them use the model driven architecture, however they are more focused on designing platform independent model (PIM) and platform specific model (PSM) and reserved unfortunately few efforts for requirements modelling and design computational independent model (CIM). This situation can be explained essentially by the presence of mature technologies and methods used for the representation of PIM and PSM models. Moreover, there are no standards to build CIM. Within this context, data warehousing projects fail due to not much attention paid on the requirement analysis phase. To overcome this problem, this study provides an approach to build a CIM architecture for modelling data warehouse requirements using an unified modelling language (UML) profile that extends the use cases and business process models, and proposes requirements specifications using SBVR structured English.

Eliciting and structuring business indicators in data warehouse requirements engineering

AbstractTraditionally, data warehouse requirements engineering is oriented towards determining the information contents of the warehouse to be. This has resulted in a de‐emphasis of the functional perspective of data warehouses. Consequently, it is difficult to specify functions needed for computing business indicators. Our approach aims to elicit needed business indicators from organizational decision makers. Thereafter, indicator hierarchies are built. Then we associate functions with business indicators of the hierarchy. These functions are visualized as use case diagrams. To do this, we extend these diagrams to allow for actor aggregation in addition to actor specialization. Further, we introduce the ‘estimated from’ relationship between use cases, in addition to the ‘extend’ and ‘include’ relationships of UML. We illustrate our proposals with an example.

Data warehouse requirements engineering: an emerging discipline

Though the origins of requirements engineering lie in software/information systems engineering, our study shows that requirements engineering in data warehouse systems is beginning to emerge as a stand-out, independent discipline. We show that failure statistics of both kinds of systems point to the common problem of defective requirements. Consequently, a number of models and techniques were developed in software/information systems for engineering the requirements of the system to-be. Initially, data warehouse requirements engineering was highly influenced by these. However, in recent years, new concepts and models specific to data warehouse requirements engineering have been proposed. We are seeing the emergence of a new discipline.

Data warehouse requirements engineering: an emerging discipline

Though the origins of requirements engineering lie in software/information systems engineering, our study shows that requirements engineering in data warehouse systems is beginning to emerge as a stand-out, independent discipline. We show that failure statistics of both kinds of systems point to the common problem of defective requirements. Consequently, a number of models and techniques were developed in software/information systems for engineering the requirements of the system to-be. Initially, data warehouse requirements engineering was highly influenced by these. However, in recent years, new concepts and models specific to data warehouse requirements engineering have been proposed. We are seeing the emergence of a new discipline.

Eliciting Data Warehouse Contents for Policy Enforcement Rules

Data Warehouse requirements engineering has been extensively looked at from the ENDS perspective of the Business Motivation Model, in terms of goals the system to-be wants to achieve. The authors propose that the MEANS perspective of this Model can drive the requirements engineering process. MEANS are organized into business policies and ‘policy enforcement rules'. Starting from policies expressed in a higher order logic, the authors propose an approach to formulate policy enforcement rules. That subset of the set of formulated policy enforcement rules which is most appropriate for the business is to be selected. For this, the information relevant to the rules is to be kept in the Data Warehouse. The authors technique picks up the components of the policy enforcement rule to elicit the information that has a bearing on its selection. The elicited information is represented as an ER diagram. The authors rely on existing methodologies to convert an ER form into star schemas. The authors use the medical domain to illustrate our methodology.

On completeness and traceability metrics for data warehouse requirements engineering

Due to increased complexity of data warehouse (DW), continuous attention must be paid for evaluation of quality throughout its design and development. The quality of models is one of the main factors that influence information quality of DW. In some earlier works, metrics or guidelines to assure the quality of conceptual, logical and physical models for DW were proposed. To the best knowledge of the authors, there is no significant work found in the literature that may assure quality of DW requirements model. In this paper, we present requirements completeness and traceability metrics to monitor the progress of DW requirements engineering (RE) products (i.e., requirements model) while using a particular approach. Here, in our case, our existing approach is being used for capturing DW requirements, in which, we capture requirements in terms of organisation model, decision model and information model. We have successfully applied our proposed metrics to these DW requirements models for a typical banking organisation.

Sistem Rantai Pasok Produk Obat-Obatan

PT. Bayer Indonesia is a global enterprise with core competencies in health, nutrition and high-tech materials. As the core company operations, PT. Bayer Indonesia produces and markets various drugs and over-the-counter, as well as products for the agricultural industry. One of the products produced by this company are Aspirin and Calcium D Redoxon (CDR) Fortos. Material and information flows along the value-added chain becomes more dynamic. Supply Chain Management (SCM) is a method, tool, or approach to management. Demand management of PT. Bayer Indonesia is to capture information related to forecasting (forecasting), order entry, orderpromising, branch warehouse requirements. Forecasting methods used by these companies is a method of MRP (Material Requirement Planning). Supplier selection mechanism is performed by PT Bayer Indonesia is by selecting suppliers, supplier evaluation, supplier optimization, and material ordering and payment.Keywords : PT. Bayer Indonesia, Supply Chain Management, Supplier

Eliciting user requirements when there is no organization: a mixed method for an educational data warehouse project

Steadily, more statistical data about student performance from international large-scale assessment studies becomes publicly available. To facilitate access, the eduDWH project aimed at building a data warehouse integrating three such studies for educational researchers. This target group is hard to access because it is not united through a single organizational context and individual researchers are not stakeholders in the warehouse project. This paper proposes an adapted mix of focus group activities and document analysis and reports about experiences while piloting these activities. It was found to be efficient for eliciting data warehouse requirements even with limited direct target user contact.

Stakeholders Driven Requirements Engineering Approach for Data Warehouse Development

Most of the data warehouse (DW) requirements engineering approaches have not distinguished the early requirements engineering phase from the late requirements engineering phase. There are very few approaches seen in the literature that explicitly model the early & late requirements for a DW. In this paper, we propose an AGDI (Agent-Goal-Decision-Information) model to support the early and late requirements for the development of DWs. Here, the notion of agent refers to the stakeholders of the organization and the dependency among agents refers to the dependencies among stakeholders for fulfilling their organizational goals. The proposed AGDI model also supports three interrelated modeling activities namely, organization modeling, decision modeling and information modeling. Here, early requirements are modeled by performing organization modeling and decision modeling activities, whereas late requirements are modeled by performing information modeling activities. The proposed approach has been illustrated to capture the early and late requirements for the development of a university data warehouse exemplifying our model's ability of supporting its decisional goals by providing decisional information.

Data Warehousing Requirements Collection and Definition

Defining data warehouse requirements is widely recognized as one of the most important steps in the larger data warehouse system development process. This paper examines the potential risks and pitfalls within the data warehouse requirement collection and definition process. A real scenario of a large-scale data warehouse implementation is given, and details of this project, which ultimately failed due to inadequate requirement collection and definition process, are described. The presented case underscores and illustrates the impact of the requirement collection and definition process on the data warehouse implementation, while the case is analyzed within the context of the existing approaches, methodologies, and best practices for prevention and avoidance of typical data warehouse requirement errors and oversights.

A Dynamic Distribution Model with Warehouse and Customer Replenishment Requirements

A Dynamic Distribution Model with Warehouse and Customer Replenishment Requirements

Recent Trends in Warehousing Development

This is a statement of just how much change warehousing has undergone in recent years and how different warehouse requirements are now. Companies which fail to realise these changes and to analyse and evaluate their own warehousing stock are at risk of losing their competitive position.