scholarly journals Electronic demography decision making system

2020 ◽  
pp. 228-236
Author(s):  
G.Ch. Nabibekova ◽  
Keyword(s):  
Data Warehouse ◽  
Decision Makers ◽  
Practical Application ◽  
Data Marts ◽  
Decision Making System ◽  
Demographic Processes ◽  
Analytical Processing ◽  
Using Data ◽  
High Level ◽  
Bus Architecture

The article suggests an approach to the development of an electronic demographic decision support system using data warehouse and interactive analytical processing OLAP. This makes it possible to conduct research on demographic processes at a high level and to support decision makers in the field of demography. Due to the presence of many types of demography and a large number of indicators, proposed in the article, a Data Mart Bus Architecture with Linked Dimensional Data Marts is proposed as a Data Warehouse architecture. The article also shows the practical application of this approach using two Data Marts as an example. Based on these Data Marts, OLAP-cubes are built. OLAP operations provide the ability to view cubes in various slices, as well as provide aggregate data.

Application of OLAP Technology in the Environment of Electronic Demography

2021 ◽  
Vol 27 (10) ◽  
pp. 542-549
Author(s):  
G. Ch. Nabibayova ◽  
Keyword(s):  
Decision Makers ◽  
Practical Application ◽  
Demographic Research ◽  
Computing Model ◽  
Interdisciplinary Field ◽  
Parallel Data ◽  
Data Marts ◽  
Data Volume ◽  
Analytical Processing ◽  
High Level

The article proposes an approach to the development of an electronic demographic decision support system using technologies of Data Warehouse (DW) and Interactive Analytical Processing OLAP. This makes it possible to conduct high-level demographic research and provide support to decision-makers in demographic sphere. The article notes that demography is an interdisciplinary field of research and is defined as a complex science. Each industry of demography has many indicators. A sample list of these indicators is presented. The main characteristics of the DW, which should be taken into account when developing its architecture, are stated. Among these characteristics, one can find the main defining characteristics of Big Data — volume, velocity, variety, veracity, variability, visualization, value etc. For a more rational and efficient use of a large amount of information, taking into account its constant increase, to ensure the speed of execution of requests for a given system, it is proposed to use a Bus of Interconnected Data Marts (DM) as an architecture of DW. One of the advantages of using DM is that their use assumes distributed parallel data processing. This architecture allows for much faster results generation. It is based on the MapReduce distributed computing model and the Hadoop project. In addition, to effectively use large amounts of data, it is also proposed to use OLAP operations such as roll-up and drill-down, as well as fuzzy set theory, based on the technique of computing with words. The article also shows the practical application of interconnected DM. An OLAP cube is built on the basis of these DM. OLAP operations provide the ability to view cubes in different slices and provide aggregate data.

A Novel Approach Using Non-Synonymous Materialized Queries for Data Warehousing

2021 ◽  
Vol 17 (3) ◽  
pp. 22-43
Author(s):  
Sonali Ashish Chakraborty
Keyword(s):  
Data Warehouse ◽  
Relational Database ◽  
Execution Time ◽  
Data Warehousing ◽  
Multiple Sources ◽  
Novel Approach ◽  
Union Operation ◽  
Data Marts ◽  
Using Data

Data from multiple sources are loaded into the organization data warehouse for analysis. Since some OLAP queries are quite frequently fired on the warehouse data, their execution time is reduced by storing the queries and results in a relational database, referred as materialized query database (MQDB). If the tables, fields, functions, and criteria of input query and stored query are the same but the query criteria specified in WHERE or HAVING clause do not match, then they are considered non-synonymous to each other. In the present research, the results of non-synonymous queries are generated by reusing the existing stored results after applying UNION or MINUS operations on them. This will reduce the execution time of non-synonymous queries. For superset criteria values of input query, UNION operation is applied, and for subset values, MINUS operation is applied. Incremental result processing of existing stored results, if required, is performed using Data Marts.

Real-time high-level video understanding using data warehouse

2006 ◽  
Author(s):  
Bruno Lienard ◽  
Xavier Desurmont ◽  
Bertrand Barrie ◽  
Jean-Francois Delaigle
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Video Understanding ◽  
Using Data ◽  
High Level

System Replacement to a New HIS and Data Warehouse

2012 ◽  
Vol 16 (1) ◽  
pp. 38-41
Author(s):  
Masayuki Honda ◽  
◽  
Takehiro Matsumoto
Keyword(s):  
Data Warehouse ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Large Scale ◽  
Best Practice ◽  
Transaction Processing ◽  
Hospital Information Systems ◽  
University Hospital ◽  
Analytical Processing ◽  
High Level

Large-scale hospital information systems (HIS) generally consist of (i) online transaction processing (OLTP) and (ii) online analytical processing (OLAP) systems. Electronic medical records (EMR) are a major OLTP element. The data warehouse (DWH) assumes many important OLAP roles and maintains an institution’s medical care at a high level by providing EMR with the best practice cases available. This article focuses mainly on why OLTP and OLAP are needed and what roles the DWH plays, which means that the DWH has its own utilities and supplementary merits. The background of this discussion is closely related to the HIS at Nagasaki University Hospital introduced before the DWH is discussed.

Multidimensional Model Design using Data Mining

2017 ◽  
Vol 13 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Sandro Bimonte ◽  
Lucile Sautot ◽  
Ludovic Journaux ◽  
Bruno Faivre
Keyword(s):  
Data Mining ◽  
Rapid Prototyping ◽  
Data Warehouse ◽  
Decision Maker ◽  
Decision Makers ◽  
Model Design ◽  
Multidimensional Model ◽  
Mapping Process ◽  
Using Data

Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.

Enhancing the Diamond Document Warehouse Model

2020 ◽  
Vol 16 (4) ◽  
pp. 1-25
Author(s):  
Maha Azabou ◽  
Ameen Banjar ◽  
Jamel Omar Feki
Keyword(s):  
Data Warehouse ◽  
Decision Makers ◽  
Main Concern ◽  
Multidimensional Model ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

The data warehouse community has paid particular attention to the document warehouse (DocW) paradigm during the last two decades. However, some important issues related to the semantics are still pending and therefore need a deep research investigation. Indeed, the semantic exploitation of the DocW is not yet mature despite it representing a main concern for decision-makers. This paper aims to enhancing the multidimensional model called Diamond Document Warehouse Model with semantics aspects; in particular, it suggests semantic OLAP (on-line analytical processing) operators for querying the DocW.

scholarly journals An MDA approach for developing secure OLAP applications: Metamodels and transformations

2015 ◽  
Vol 12 (2) ◽  
pp. 541-565 ◽  
Author(s):  
Carlos Blanco ◽  
Guzmán de ◽  
Eduardo Fernández-Medina ◽  
Juan Trujillo
Keyword(s):  
Decision Makers ◽  
Sql Server ◽  
Enterprise Information ◽  
Logical Models ◽  
Model Driven ◽  
Data Marts ◽  
On Line ◽  
Analytical Processing ◽  
Multidimensional Modelling ◽  
Secure Implementation

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).

scholarly journals On-line Analytical Processing (OLAP) Operation for Outpatient Healthcare

2021 ◽  
pp. 225-231
Author(s):  
Talib M. J. Al Taleb ◽  
Sami Hasan ◽  
Yaqoob Yousif Mahd
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Decision Makers ◽  
Data Repository ◽  
Managerial Decision ◽  
High Quality Information ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Different Sources

This paper presents an architecture for the data warehouse of outpatient healthcare (DWOP) as a data repository collects data from two different sources (Databases of outpatient healthcare and Excel files from hospitals) and provides storage, functionality and responsiveness to queries to meet decision makers requirements.Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. “On-Line Analytical Processing (OLAP) is utilized for decision support to get interesting information” from the data warehouse with a rapid execution time. OLAP is considered one of Business Intelligence tools.

An Approach for Retrieving Faster Query Results From Data Warehouse Using Synonymous Materialized Queries

2021 ◽  
Vol 17 (2) ◽  
pp. 85-105
Author(s):  
Sonali Ashish Chakraborty ◽  
Jyotika Doshi
Keyword(s):  
Data Warehouse ◽  
Strategic Decision ◽  
Maintenance Cost ◽  
Multidimensional Data ◽  
Materialized Views ◽  
Multiple Sources ◽  
Query Result ◽  
Novel Approach ◽  
Data Marts ◽  
Analytical Processing

The enterprise data warehouse stores an enormous amount of data collected from multiple sources for analytical processing and strategic decision making. The analytical processing is done using online analytical processing (OLAP) queries where the performance in terms of result retrieval time is an important factor. The major existing approaches for retrieving results from a data warehouse are multidimensional data cubes and materialized views that incur more storage, processing, and maintenance costs. The present study strives to achieve a simpler and faster query result retrieval approach from data warehouse with reduced storage space and minimal maintenance cost. The execution time of frequent queries is saved in the present approach by storing their results for reuse when the query is fired next time. The executed OLAP queries are stored along with the query results and necessary metadata information in a relational database is referred as materialized query database (MQDB). The tables, fields, functions, relational operators, and criteria used in the input query are matched with those of stored query, and if they are found to be same, then the input query and the stored query are considered as a synonymous query. Further, the stored query is checked for incremental updates, and if no incremental updates are required, then the existing stored results are fetched from MQDB. On the other hand, if the stored query requires an incremental update of results, then the processing of only incremental result is considered from data marts. The performance of MQDB model is evaluated by comparing with the developed novel approach, and it is observed that, using MQDB, a significant reduction in query processing time is achieved as compared to the major existing approaches. The developed model will be useful for the organizations keeping their historical records in the data warehouse.

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