scholarly journals Model of Textual Data Linking and Clustering in Relational Databases

2016 ◽  
Vol 9 (1) ◽  
pp. 7-17
Author(s):  
Wael M.S. Yafoo
Keyword(s):  
Relational Databases ◽  
Textual Data

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.

Humanities Data Warehousing

2011 ◽  
pp. 987-992
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 (in say Wal-Mart’s data warehouse (Westerman, 2000)) and astronomical data (for example SKICAT) in scientific research, with textual data providing a descriptive rather than a central analytic 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.

Humanitites Data Warehousing

2011 ◽  
pp. 570-574
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.

Modeling longitudinal dynamics in textual data

2003 ◽  
Author(s):  
Kevin Dooley ◽  
Steven Corman
Keyword(s):  
Longitudinal Dynamics ◽  
Textual Data

Patients’ Perceptions of Caring and Uncaring Nursing Encounters in Inpatient Rehabilitation Settings

2019 ◽  
Vol 21 (2) ◽  
Author(s):  
Joan C Cheruiyot ◽  
Petra Brysiewicz
Keyword(s):  
South Africa ◽  
Content Analysis ◽  
Inpatient Rehabilitation ◽  
Structured Interview ◽  
The Self ◽  
Personal Relationships ◽  
Individual Interviews ◽  
Textual Data ◽  
Interview Guide ◽  
Being There

This study explores and describes caring and uncaring nursing encounters from the perspective of the patients admitted to inpatient rehabilitation settings in South Africa. The researchers used an exploratory descriptive design. A semi-structured interview guide was used to collect data through individual interviews with 17 rehabilitation patients. Content analysis allowed for the analysis of textual data. Five categories of nursing encounters emerged from the analysis: noticing and acting, and being there for you emerged as categories of caring nursing encounters, and being ignored, being a burden, and deliberate punishment emerged as categories of uncaring nursing encounters. Caring nursing encounters make patients feel important and that they are not alone in the rehabilitation journey, while uncaring nursing encounters makes the patients feel unimportant and troublesome to the nurses. Caring nursing encounters give nurses an opportunity to notice and acknowledge the existence of vulnerability in the patients and encourage them to be present at that moment, leading to empowerment. Uncaring nursing encounters result in patients feeling devalued and depersonalised, leading to discouragement. It is recommended that nurses strive to develop personal relationships that promote successful nursing encounters. Further, nurses must strive to minimise the patients’ feelings of guilt and suffering, and to make use of tools, for example the self-perceived scale, to measure this. Nurses must also perform role plays on how to handle difficult patients such as confused, demanding and rude patients in the rehabilitation settings.

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