scholarly journals Star Schema Advantages on Data Warehouse: Using Bitmap Index and Partitioned Fact Tables

2016 ◽  
Vol 134 (13) ◽  
pp. 11-13 ◽  
Author(s):  
Emany Sidi ◽  
Mohamed El ◽  
El Amin
Keyword(s):  
Data Warehouse ◽  
Bitmap Index ◽  
Star Schema

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.

scholarly journals Dimensional Modeling Using Star Schema for Data Warehouse Creation

2017 ◽  
Vol 10 (04) ◽  
pp. 745-754
Author(s):  
Mudasir M Kirmani
Keyword(s):  
Data Warehouse ◽  
Transformation Model ◽  
Data Warehouses ◽  
Design Technique ◽  
End User ◽  
Conversion Model ◽  
Know How ◽  
Dimensional Modeling ◽  
Star Schema ◽  
Logical Design

Data Warehouse design requires a radical rebuilding of tremendous measures of information, frequently of questionable or conflicting quality, drawn from various heterogeneous sources. Data Warehouse configuration assimilates business learning and innovation know-how. The outline of theData Warehouse requires a profound comprehension of the business forms in detail. The principle point of this exploration paper is to contemplate and investigate the transformation model to change over the E-R outlines to Star Schema for developing Data Warehouses. The Dimensional modelling is a logical design technique used for data warehouses. This research paper addresses various potential differences between the two techniques and highlights the advantages of using dimensional modelling along with disadvantages as well. Dimensional Modelling is one of the popular techniques for databases that are designed keeping in mind the queries from end-user in a data warehouse. In this paper the focus has been on Star Schema, which basically comprises of Fact table and Dimension tables. Each fact table further comprises of foreign keys of various dimensions and measures and degenerate dimensions if any. We also discuss the possibilities of deployment and acceptance of Conversion Model (CM) to provide the details of fact table and dimension tables according to the local needs. It will also highlight to why dimensional modelling is preferred over E-R modelling when creating data warehouse.

An Encryption Methodology for Enabling the Use of Data Warehouses on the Cloud

2021 ◽  
pp. 528-559
Author(s):  
Claudivan Cruz Lopes ◽  
Valéria Cesário-Times ◽  
Stan Matwin ◽  
Cristina Dutra de Aguiar Ciferri ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Data Warehouse ◽  
Homomorphic Encryption ◽  
Distributed Database ◽  
Pareto Optimal ◽  
Symmetric Encryption ◽  
Data Confidentiality ◽  
Cloud Data ◽  
Use Of Data ◽  
Star Schema ◽  
Definition Of

A cloud data warehouse (cloud DW) is a subject-oriented, integrated, time-variant, voluminous, nonvolatile and multidimensional distributed database that is hosted in a cloud. A solution to ensure data confidentiality for a cloud DW is cryptography. In this article, the authors propose an encryption methodology for a cloud DW stored according to the star schema, considering both the data confidentiality maintenance of the DW and the capability of processing analytical queries directly over the encrypted DW. The proposed encryption methodology comprises an encryption strategy for DW called MV-HO (MultiValued and HOmomorphic) for the definition of how the different types of DW's attributes must be encrypted. The proposed MV-HO encryption strategy was compared with encryption strategies based on symmetric encryption, order preserving symmetric encryption and homomorphic encryption. Results indicated that MV-HO is the best solution found, as MV-HO is pareto-optimal with respect to other strategies investigated.

Analyses and Evaluation of Responses to Slowly Changing Dimensions in Data Warehouses

2011 ◽  
pp. 324-337
Author(s):  
Lars Frank ◽  
Christian Frank
Keyword(s):  
Data Warehouse ◽  
Concurrency Control ◽  
The Other ◽  
Data Warehouses ◽  
Other Hand ◽  
Star Schema ◽  
Dynamic Dimension ◽  
Special Case

A Star Schema Data Warehouse looks like a star with a central, so-called fact table, in the middle, surrounded by so-called dimension tables with one-to-many relationships to the central fact table. Dimensions are defined as dynamic or slowly changing if the attributes or relationships of a dimension can be updated. Aggregations of fact data to the level of the related dynamic dimensions might be misleading if the fact data are aggregated without considering the changes of the dimensions. In this chapter, we will first prove that the problems of SCD (Slowly Changing Dimensions) in a datawarehouse may be viewed as a special case of the read skew anomaly that may occur when different transactions access and update records without concurrency control. That is, we prove that aggregating fact data to the levels of a dynamic dimension should not make sense. On the other hand, we will also illustrate, by examples, that in some situations it does make sense that fact data is aggregated to the levels of a dynamic dimension. That is, it is the semantics of the data that determine whether historical dimension data should be preserved or destroyed. Even worse, we also illustrate that for some applications, we need a history preserving response, while for other applications at the same time need a history destroying response. Kimball et al., (2002), have described three classic solutions/responses to handling the aggregation problems caused by slowly changing dimensions. In this chapter, we will describe and evaluate four more responses of which one are new. This is important because all the responses have very different properties, and it is not possible to select a best solution without knowing the semantics of the data.

scholarly journals Analisis Dan Perancangan Data Warehouse Pada PT Gajah Tunggal Prakarsa

2010 ◽  
Vol 1 (2) ◽  
pp. 437
Author(s):  
Choirul Huda ◽  
Bram Pangestu ◽  
Jimmy Lai ◽  
Riantoro Teja
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Data Warehouse ◽  
Research Methodology ◽  
Analysis Study ◽  
Library Research ◽  
Star Schema ◽  
Current Systems

The purpose of this helpful in making decisions more quickly and precisely. Research methodology includes analysis study was to analyze the data base support in helping decisions making, identifying needs and designing a data warehouse. With the support of data warehouse, company leaders can be more of current systems, library research, designing a data warehouse using star schema. The result of this research is the availability of a data warehouse that can generate information quickly and precisely, thus helping the company in making decisions. The conclusion of this research is the application of data warehouse can be a media aide related parties on PT. Gajah Tunggal initiative in decision making. 

scholarly journals An Improved Framework for Bitmap Indexes and their Use in Data Warehouse Optimization

2021 ◽  
Vol 12 (2) ◽  
pp. 1513-1520
Author(s):  
Dr. J. Preetha, Et. al.
Keyword(s):  
Data Warehouse ◽  
Bitmap Index ◽  
Compression Technique ◽  
Data Manipulation ◽  
Disk Space ◽  
Index Compression ◽  
Storage Area ◽  
Table Data ◽  
Manipulation Language ◽  
Bitmap Indexes

Compression technique is basically used to compress the size of table or reduce the storage area. Oracle already gives this feature for the table compression as well as for the index compression. when index is created on particular column of a table then it contain some space, which require some storage or disk space by this technique we can save our disk space because in industry the company have to purchase the disk space  according to the size of the their data and pay according to their disk space. To utilize this disk space for useful records data rather than wasting it. In this paper used the data pump utility for the compression of Bitmap index and table. Data pump utility performed for the logical backups in database.in this paper implemented data pump for compression, to release the space and change the index pointing location. It will not release the space even after deletion of records. This is of special interest for the case to compress the bitmap index and table space along with the’S (Data Manipulation Language).

scholarly journals Designing a Star Schema for Optimizing the Total Sales of Motorcycles

2021 ◽  
pp. 105-109
Author(s):  
Eka Praja Wiyata Mandala ◽  
Randy Permana ◽  
Dewi Eka Putri
Keyword(s):  
Data Warehouse ◽  
Business Processes ◽  
Requirements Analysis ◽  
Complex Information ◽  
Star Schema ◽  
Optimized Model ◽  
Total Sales

Motorcycle sales have increased significantly, motorcycle manufacturers are competing to produce the latest models which are then sold to consumers. As a result, motorcycle dealers are overwhelmed with more and more data, not knowing what to do with it. Motorcycle dealers also have difficulty calculating the total sales of motorcycles. We try to provide solutions to deal with data overflow. We propose designing a star schema as the basis for creating a data warehouse. To create a star schema, we propose a four-step sequence in creating an effective star schema, starting from requirements analysis and reporting, understanding business processes, connecting and matching business processes with suitable entities and determining the dimensions of the business processes. We get a star schema with 1 fact table, motorcycle_sales and 11 of dimension tables, such as brand, color, customer, customer_contract, distributor, district, motorcycle, repair_workshop, sell_location, type and time.  The star schema is an optimized model that provides the best performance in presenting more complex information

scholarly journals Perancangan Data Warehouse pada Laporan PDPT di Kopertis Wilayah V

2016 ◽  
Vol 3 (3) ◽  
pp. 255
Author(s):  
Sukarsono Windu Kumoro ◽  
Abidarin Rosidi ◽  
Armadyah Amborowati
Keyword(s):  
Higher Education ◽  
Data Warehouse ◽  
Teaching And Learning ◽  
Private Higher Education ◽  
Faculty Teaching ◽  
Star Schema ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Academic Year

Evaluasi terhadap Program Studi pada Perguruan Tinggi Swasta (PTS) yang memperoleh Ijin Penyelenggaraan dari Dirjen Dikti dibutuhkan oleh Koordinator Kopertis Wilayah V. Laporan PDPT telah terkumpul sejak tahun akademik 2002 semester ganjil (2002-1) sampai dengan tahun akademik 2013 semester genap (2013-2) yang terdiri dari data transaksi yang terkait dengan proses belajar mengajar di PTS. Laporan PDPT dari PTS dikerjakan atas dasar “Culture Trust”. Untuk mengatasi permasalahan tersebut dibangun sebuah data warehouse di Kopertis Wilayah V DIY. Data warehouse ini dikembangkan dengan menggunakan Foxpro dan Clipper dikarenakan data yang dilaporkan menggunakan file berekstendi DBF. Foxpro dan Clipper adalah sebuah paket basisdata dan dapat didistribusikan.Dalam pengerjaan pembangunan data warehouse ini akan melalui proses ETL dan pembuatan Star Schema (Skema Bintang) berupa dimensi-dimensi yang terhubung dengan tabel fakta berupa tabel aktifitas perkuliahan mahasiswa, evaluasi program studi dan aktifitas dosen mengajar di seluruh program studi pada PTS yang menjadi binaan Kopertis Wilayah V. Kemudian hasil data warehouse akan dianalisa melalui proses OLAP (On-line Analytical Processing).The evaluation of the Program on Private Higher Education (PTS) which derive from the Operating Licence required by the Coordinator General of Higher Education Kopertis Region V. PDPT reports have been collected since 2002 semester of the academic year (2002-1) until the second semester of academic year 2013 (2013-2), which consists of transaction data associated with the teaching and learning process in the PTS. PDPT reports of PTS is done on the basis of "Culture Trust".To overcome these problems built a data warehouse in Kopertis Region V DIY. The data warehouse was developed using FoxPro and Clipper because the data reported using a DBF file extension. FoxPro and Clipper is a package database and can be distributed. In the execution of data warehouse development is going through the ETL process and the making of Star Schema (Star Schema) in the form of dimensions that are connected with the fact table in the form of table activity lecturing students, evaluation of courses and activities throughout the faculty teaching courses at private universities being built Kopertis region V. Then the results will be analyzed data warehouse through a process of OLAP (On-line Analytical Processing).

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