scholarly journals Designing a Framework to Standardize Data Warehouse Development Process for Effective Data Warehousing Practices

2016 ◽  
Vol 8 (4) ◽  
pp. 15-32
Author(s):  
Deepak Asrani ◽  
Renu Jain
Keyword(s):  
Data Warehouse ◽  
Development Process ◽  
Data Warehousing

Universal Data Warehousing Based on a Meta-Data Modeling Approach

2003 ◽  
Vol 12 (03) ◽  
pp. 325-363 ◽  
Author(s):  
Joseph Fong ◽  
Qing Li ◽  
Shi-Ming Huang
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Object Oriented ◽  
Data Models ◽  
Heterogeneous Databases ◽  
Materialized Views ◽  
Prototype System ◽  
Relational View ◽  
Metadata Model ◽  
Star Schema

Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements based on the integrated schema, catalogued in the metadata, which stores the schema of relational database (RDB) and object-oriented database (OODB). Data materialization between RDB and OODB is achieved by unloading source database into sequential file and reloading into target database, through which an object relational view can be defined so as to allow the users to obtain the same warehouse view in different data models simultaneously. We describe our procedures of building the relational view of star schema by multidimensional SQL query, and the object oriented view of the data warehouse by Online Analytical Processing (OLAP) through method call, derived from the integrated schema. To validate our work, an application prototype system has been developed in a product sales data warehousing domain based on this approach.

Optimizing ETL by a Two-Level Data Staging Method

2016 ◽  
Vol 12 (3) ◽  
pp. 32-50
Author(s):  
Xiufeng Liu ◽  
Nadeem Iftikhar ◽  
Huan Huo ◽  
Per Sieverts Nielsen
Keyword(s):  
Data Warehouse ◽  
Data Warehousing ◽  
Data Extraction ◽  
Data Staging ◽  
Staging Area ◽  
Level Data ◽  
Different Types ◽  
Operational Systems ◽  
Early Late ◽  
Central Data

In data warehousing, the data from source systems are populated into a central data warehouse (DW) through extraction, transformation and loading (ETL). The standard ETL approach usually uses sequential jobs to process the data with dependencies, such as dimension and fact data. It is a non-trivial task to process the so-called early-/late-arriving data, which arrive out of order. This paper proposes a two-level data staging area method to optimize ETL. The proposed method is an all-in-one solution that supports processing different types of data from operational systems, including early-/late-arriving data, and fast-/slowly-changing data. The introduced additional staging area decouples loading process from data extraction and transformation, which improves ETL flexibility and minimizes intervention to the data warehouse. This paper evaluates the proposed method empirically, which shows that it is more efficient and less intrusive than the standard ETL method.

AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

2001 ◽  
Vol 10 (03) ◽  
pp. 377-397 ◽  
Author(s):  
LUCA CABIBBO ◽  
RICCARDO TORLONE
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Multidimensional Data ◽  
Multidimensional Databases ◽  
Level Of Aggregation ◽  
High Level ◽  
Data Independence ◽  
Logical Architecture

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.

Data warehousing organization: Infrastructural experimentation with educational governance

Organization ◽  
2018 ◽  
Vol 26 (4) ◽  
pp. 537-552 ◽  
Author(s):  
Helene Ratner ◽  
Christopher Gad
Keyword(s):  
Data Warehouse ◽  
Science And Technology Studies ◽  
Data Warehousing ◽  
Ethnographic Study ◽  
Educational Governance ◽  
Unfinished Business ◽  
Organizational Effects ◽  
Organizational Relations ◽  
Partial Connection ◽  
Governance Infrastructure

Organization is increasingly entwined with databased governance infrastructures. Developing the idea of ‘infrastructure as partial connection’ with inspiration from Marilyn Strathern and Science and Technology Studies, this article proposes that database infrastructures are intrinsic to processes of organizing intra- and inter-organizational relations. Seeing infrastructure as partial connection brings our attention to the ontological experimentation with knowing organizations through work of establishing and cutting relations. We illustrate this claim through a multi-sited ethnographic study of ‘The Data Warehouse’. ‘The Data Warehouse’ is an important infrastructural component in the current reorganization of Danish educational governance which makes schools’ performance public and comparable. We suggest that ‘The Data Warehouse’ materializes different, but overlapping, infrastructural experiments with governing education at different organizational sites enacting a governmental hierarchy. Each site can be seen as belonging to the same governance infrastructure but also as constituting ‘centres’ in its own right. ‘The Data Warehouse’ participates in the always-unfinished business of organizational world making and is made to (partially) relate to different organizational concerns and practices. This argument has implications for how we analyze the organizational effects of pervasive databased governance infrastructures and invites exploring their multiple organizing effects.

scholarly journals Issues and Handy Solutions Addressed at Every Stage in Real Time Data Warehousing, I.E. ETL (Extraction, Transformation & Loading)

2019 ◽  
Vol 8 (5S3) ◽  
pp. 344-348
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Time Data ◽  
Processing Load ◽  
Real Time Data ◽  
Data Source

In the standard ETL (Extract Processing Load), the data warehouse refreshment must be performed outside of peak hours. i It implies i that the i functioning and i analysis has stopped in their iall actions. iIt causes the iamount of icleanness of i data from the idata Warehouse which iisn't suggesting ithe latest i operational transections. This i issue is i known as i data i latency. The data warehousing is iemployed to ibe a iremedy for ithis iissue. It updates the idata warehouse iat a inear real-time iFashion, instantly after data found from the data source. Therefore, data i latency could i be reduced. Hence the near real time data warehousing was having issues which was not identified in traditional ETL. This paper claims to communicate the issues and accessible options at every point iin the i near real-time i data warehousing, i.e. i The i issues and Available alternatives iare based ion ia literature ireview by additional iStudy that ifocus ion near real-time data iwarehousing issue

Data Warehousing Requirements Collection and Definition

2012 ◽  
pp. 261-271
Author(s):  
Nenad Jukic ◽  
Miguel Velasco
Keyword(s):  
Data Warehouse ◽  
Large Scale ◽  
Data Warehousing ◽  
System Development ◽  
Large Scale Data ◽  
Typical Data ◽  
Potential Risks ◽  
Real Scenario ◽  
The Impact ◽  
Scale Data

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.

Multidimensional Design Methods for Data Warehousing

2011 ◽  
pp. 78-105 ◽  
Author(s):  
Oscar Romero ◽  
Alberto Abelló
Keyword(s):  
Data Warehouse ◽  
Design Process ◽  
Data Warehousing ◽  
Design Methods ◽  
Data Sources ◽  
Data Driven ◽  
Main Research ◽  
Multidimensional Modeling ◽  
Warehouse Design ◽  
Warehousing Systems

In the last years, data warehousing systems have gained relevance to support decision making within organizations. The core component of these systems is the data warehouse and nowadays it is widely assumed that the data warehouse design must follow the multidimensional paradigm. Thus, many methods have been presented to support the multidimensional design of the data warehouse.The first methods introduced were requirement-driven but the semantics of the data warehouse (since the data warehouse is the result of homogenizing and integrating relevant data of the organization in a single, detailed view of the organization business) require to also consider the data sources during the design process. Considering the data sources gave rise to several data-driven methods that automate the data warehouse design process, mainly, from relational data sources. Currently, research on multidimensional modeling is still a hot topic and we have two main research lines. On the one hand, new hybrid automatic methods have been introduced proposing to combine data-driven and requirement-driven approaches. These methods focus on automating the whole process and improving the feedback retrieved by each approach to produce better results. On the other hand, some new approaches focus on considering alternative scenarios than relational sources. These methods also consider (semi)-structured data sources, such as ontologies or XML, that have gained relevance in the last years. Thus, they introduce innovative solutions for overcoming the heterogeneity of the data sources. All in all, we discuss the current scenario of multidimensional modeling by carrying out a survey of multidimensional design methods. We present the most relevant methods introduced in the literature and a detailed comparison showing the main features of each approach.

Humanitites Data Warehousing

2008 ◽  
pp. 2364-2370
Author(s):  
Janet Delve
Keyword(s):  
Data Warehouse ◽  
Relational Databases ◽  
Data Warehousing ◽  
Numerical Data ◽  
Complex Nature ◽  
Data Warehouses ◽  
Textual Data ◽  
Numeric Data ◽  
First Time ◽  
And Linguistics

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (e.g. Wal-Mart’s data warehouse) and astronomical data (e.g. SKICAT) in scientific research, with textual data providing a descriptive rather than a central role. The lack of ability of data warehouses to cope with mainly non-numeric data is particularly problematic for humanities1 research utilizing material such as memoirs and trade directories. Recent innovations have opened up possibilities for non-numeric data warehouses, making them widely accessible to humanities research for the first time. Due to its irregular and complex nature, humanities research data is often difficult to model and manipulating time shifts in a relational database is problematic as is fitting such data into a normalized data model. History and linguistics are exemplars of areas where relational databases are cumbersome and which would benefit from the greater freedom afforded by data warehouse dimensional modeling.

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