Selection of a Green Logical Data Warehouse Schema by Anti-monotonicity Constraint

2020 ◽  
pp. 350-361
Author(s):  
Issam Ghabri ◽  
Ladjel Bellatreche ◽  
Sadok Ben Yahia
Keyword(s):  
Data Warehouse ◽  
Monotonicity Constraint ◽  
Logical Data ◽  
Selection Of

A Hybrid Approach for Data Warehouse View Selection

2008 ◽  
pp. 3085-3115
Author(s):  
Biren Shah ◽  
Karthik Ramachandran ◽  
Vijay Raghavan
Keyword(s):  
Data Warehouse ◽  
Hybrid Approach ◽  
Greedy Heuristic ◽  
Materialized Views ◽  
View Selection ◽  
Materialized View Selection ◽  
Optimal Efficiency ◽  
Access Patterns ◽  
Multiple Query ◽  
Selection Of

Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Static selection of views may materialize certain views that are not beneficial as the data and usage trends change over time. On the contrary, dynamic selection of views works better only for queries demanding a high degree of aggregation. These facts point to the need for a technique that combines the improved response time of the static approach and the automated tuning capability of the dynamic approach. In this article, we propose a hybrid approach for the selection of materialized views. The idea is to partition the collection of all views into a static and dynamic set such that views selected for materialization from the static set are persistent over multiple query (and maintenance) windows, whereas views selected from the dynamic set can be queried and/or replaced on the fly. Highly aggregated views are selected on the fly based on the query access patterns of users, whereas the more detailed static set of views plays a significant role in the efficient maintenance of the dynamic set of views and in answering certain detailed view queries. We prove that our proposed strategy satisfies the monotonicity requirements, which is essential in order for the greedy heuristic to deliver competitive solutions. Experimental results show that our approach outperforms Dynamat, a well-known dynamic view management system that is known to outperform optimal static view selection.

Materialized View Selection using Marriage in Honey Bees Optimization

2015 ◽  
Vol 5 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Biri Arun ◽  
T.V. Vijay Kumar
Keyword(s):  
Data Warehouse ◽  
Honey Bees ◽  
Response Times ◽  
Large Data ◽  
Strategic Decision ◽  
Selection Algorithm ◽  
View Selection ◽  
Materialized View Selection ◽  
On Line ◽  
Selection Of

Data warehouse was designed to cater to the strategic decision making needs of an organization. Most queries posed on them are on-line analytical queries, which are complex and computation intensive in nature and have high query response times when processed against a large data warehouse. This time can be substantially reduced by materializing pre-computed summarized views and storing them in a data warehouse. All possible views cannot be materialized due to storage space constraints. Also, an optimal selection of subsets of views is shown to be an NP-Complete problem. This problem of view selection has been addressed in this paper by selecting a beneficial set of views, from amongst all possible views, using the swarm intelligence technique Marriage in Honey Bees Optimization (MBO). An MBO based view selection algorithm (MBOVSA), which aims to select views that incur the minimum total cost of evaluating all the views (TVEC), is proposed. In MBOVSA, the search has been intensified by incorporating the royal jelly feeding phase into MBO. MBOVSA, when compared with the most fundamental greedy based view selection algorithm HRUA, is able to select comparatively better quality views.

The Data Swarm

2016 ◽  
Vol 6 (1) ◽  
pp. 52-64
Author(s):  
Jeffrey Smith ◽  
Manjeet Rege
Keyword(s):  
Swarm Intelligence ◽  
Data Warehouse ◽  
Data Availability ◽  
Retail Stores ◽  
A Chain ◽  
Data Analytic ◽  
Logical Data ◽  
Distributed Analytics

The traditional data warehouse model is no longer able to keep up with the evolution and changing requirements of the data analytic world. As we see the concept of a logical data warehouse gain momentum, there's a resulting need to drive a portion of the analytics closer to where the data is actually created and used. This paper uses the concept of swarm intelligence as a basis for simple, distributed analytics architecture to help address this need. It illustrates this with an example based on a chain of retail stores and demonstrates how this model could simplify the architecture and, at the same time, and increase data availability while decreasing cost.

Logical Data Warehouse Design

2014 ◽  
pp. 121-178
Author(s):  
Alejandro Vaisman ◽  
Esteban Zimányi
Keyword(s):  
Data Warehouse ◽  
Warehouse Design ◽  
Logical Data

scholarly journals Implementasi Pengembangan Sistem Model Water Fall Untuk Data Warehouse Akademik

2017 ◽  
Vol 1 (2) ◽  
pp. 108
Author(s):  
Arik Sofan Tohir Sofan Tohir ◽  
Kusrini Kusrini ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Data Warehouse ◽  
Flow System ◽  
Step Method ◽  
Good Data ◽  
Analysis Process ◽  
Warehouse Design ◽  
Waterfall Model ◽  
Transactional Data ◽  
Model Water ◽  
Selection Of

  Data warehouse is a concept and a technology to store transactional data from several sources that have been through the process of filtering and selection of data. By using the Ectract, Transform and Load (ETL) process in the data warehouse, OLTP data is processed to produce good data and ready for use for the analysis process. For the design of this warehouse data will be built by using the Nine Step Method from Kimbal, so that the resulting warhouse data can be as expected. For the development of life flow system (SDLC) with waterfall model. By using the wate fall model will be built a prototype to implement the data warehouse design results.

scholarly journals Designing Data Warehouse at the Impatient and Outpatient (Case Study: Sanglah Hospital)

1970 ◽  
Vol 2 (1) ◽  
pp. 40
Author(s):  
Putu Veda Andreyana ◽  
Putu Angelina Widya ◽  
Made Suartika
Keyword(s):  
Data Processing ◽  
Data Warehouse ◽  
Patient Data ◽  
Design Methods ◽  
Process Selection ◽  
Electoral Process ◽  
Number Of Patients ◽  
Query Model ◽  
Selection Of

The growth of patient data increasing the hospital resulted in even harder to compile data and analyze the data manually, so it takes a data warehouse that can perform this task automatically. The design of the data warehouse Sanglah hospital aims to build a data warehouse that can store data in structured and easier to analyze the data to make a decision. Design methods Fact Constellation Schema and method used is a Nine-step methodology consisting of nine stages, namely the Electoral Process, Selection of Grain, identification and adjustment of the dimensions, the Electoral Facts, Storage pre-calculation in the fact table, Ensuring the dimension tables, the Electoral duration database, Track changes of dimensions is slowly, prioritization and query model. Designing Data Warehouse Sanglah Hospital helpful enough for data processing in large enough quantities, so expect the needs and information about the patient can be met. Data warehouse Sanglah Hospital can be used to analyze patient data in order to get information on the number of patients of various dimensions

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