scholarly journals Building a Document-Oriented Warehouse Using NoSQL

2021 ◽  
Vol 12 (2) ◽  
pp. 33-54
Author(s):  
Ines Ben Messaoud ◽  
Abdulrahman A. Alshdadi ◽  
Jamel Feki
Keyword(s):  
Response Time ◽  
Data Structures ◽  
Data Warehousing ◽  
Read Request ◽  
Galaxy Model ◽  
Semantic Operators ◽  
Drill Down

The traditional data warehousing approaches should adapt to take into consideration novel needs and data structures. In this context, NoSQL technology is progressively gaining a place in the research and industry domains. This paper proposes an approach for building a NoSQL document-oriented warehouse (DocW). This approach has two methods, namely 1) document warehouse builder and 2) NoSQL-Converter. The first method generates the DocW schema as a galaxy model whereas the second one translates the generated galaxy into a document-oriented NoSQL model. This relies on two types of rules: structure and hierarchical rules. Furthermore, in order to help understanding the textual results of analytical queries on the NoSQL-DocW, the authors define two semantic operators S-Drill-Up and S-Drill-Down to aggregate/expand the terms of query. The implementation of our proposals uses MangoDB and Talend. The experiment uses the medical collection Clef-2007 and two metrics called write request latency and read request latency to evaluate respectively the loading time and the response time to queries.

Ontology-Based Data Warehousing and Mining Approaches in Petroleum Industries

2011 ◽  
pp. 211-236 ◽  
Author(s):  
Shastri L. Nimmagadda ◽  
Heinz Dreher
Keyword(s):  
Data Structures ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Knowledge Mapping ◽  
Financial Decisions ◽  
Fine Grained ◽  
Knowledge Models ◽  
Visualization Of Data ◽  
The One ◽  
Commercial Organizations

Several issues of database organization of petroleum industries have been highlighted. Complex geo-spatial heterogeneous data structures complicate the accessibility and presentation of data in petroleum industries. Objectives of the current research are to integrate the data from different sources and connecting them intelligently. Data warehousing approach supported by ontology, has been described for effective data mining of petroleum data sources. Petroleum ontology framework, narrating the conceptualization of petroleum ontology and methodological architectural views, has been described. Ontology based data warehousing with fine-grained multidimensional data structures, facilitate to mining and visualization of data patterns, trends, and correlations, hidden under massive volumes of data. Data structural designs and implementations deduced, through ontology supportive data warehousing approaches, will enable the researchers in commercial organizations, such as, the one of Western Australian petroleum industries, for knowledge mapping and thus interpret knowledge models for making million dollar financial decisions.

Ontology-Based Data Warehousing and Mining Approaches in Petroleum Industries

2008 ◽  
pp. 1901-1925 ◽  
Author(s):  
Shastri L. Nimmagadda ◽  
Heinz Dreher
Keyword(s):  
Data Structures ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Knowledge Mapping ◽  
Financial Decisions ◽  
Fine Grained ◽  
Knowledge Models ◽  
Visualization Of Data ◽  
The One ◽  
Commercial Organizations

Several issues of database organization of petroleum industries have been highlighted. Complex geo-spatial heterogeneous data structures complicate the accessibility and presentation of data in petroleum industries. Objectives of the current research are to integrate the data from different sources and connecting them intelligently. Data warehousing approach supported by ontology, has been described for effective data mining of petroleum data sources. Petroleum ontology framework, narrating the conceptualization of petroleum ontology and methodological architectural views, has been described. Ontology based data warehousing with fine-grained multidimensional data structures, facilitate to mining and visualization of data patterns, trends, and correlations, hidden under massive volumes of data. Data structural designs and implementations deduced, through ontology supportive data warehousing approaches, will enable the researchers in commercial organizations, such as, the one of Western Australian petroleum industries, for knowledge mapping and thus interpret knowledge models for making million dollar financial decisions.

scholarly journals Cost Models for Selecting Materialized Views in Public Clouds

2014 ◽  
Vol 10 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Romain Perriot ◽  
Jérémy Pfeifer ◽  
Laurent d'Orazio ◽  
Bruno Bachelet ◽  
Sandro Bimonte ◽  
...  
Keyword(s):  
Response Time ◽  
Data Structures ◽  
Performance Optimization ◽  
Optimization Problem ◽  
Materialized Views ◽  
Cost Models ◽  
Public Cloud ◽  
Total Response ◽  
Query Response Time ◽  
Total Response Time

Data warehouse performance is usually achieved through physical data structures such as indexes or materialized views. In this context, cost models can help select a relevant set of such performance optimization structures. Nevertheless, selection becomes more complex in the cloud. The criterion to optimize is indeed at least two-dimensional, with monetary cost balancing overall query response time. This paper introduces new cost models that fit into the pay-as-you-go paradigm of cloud computing. Based on these cost models, an optimization problem is defined to discover, among candidate views, those to be materialized to minimize both the overall cost of using and maintaining the database in a public cloud and the total response time of a given query workload. It experimentally shows that maintaining materialized views is always advantageous, both in terms of performance and cost.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

A Response-Time Investigation of Vehicle Stereotypes

2000 ◽  
Author(s):  
Michael Anthony ◽  
Robert W. Fuhrman
Keyword(s):  
Response Time

A Response Time Model on the Role of Spectral and Duration Cues in Vowel Perception

2014 ◽  
Author(s):  
Gabriel Tillman ◽  
Don van Ravenzwaaij ◽  
Scott Brown ◽  
Titia Benders
Keyword(s):  
Response Time ◽  
Time Model ◽  
Vowel Perception ◽  
Response Time Model
Sign in / Sign up

Export Citation Format

Share Document