scholarly journals Character-level analysis of semi-structured documents for set expansion

2009 ◽  
Author(s):  
Richard C. Wang ◽  
William W. Cohen
Keyword(s):  
Structured Documents ◽  
Level Analysis

Marking Up is Not Enough

1993 ◽  
Vol 32 (04) ◽  
pp. 272-273 ◽  
Author(s):  
A. L. Rector
Keyword(s):  
Health Care ◽  
Intelligent Processing ◽  
Structured Documents ◽  
Electronic Health ◽  
Health Care Records

Response to: Essin DJ. Intelligent processing of loosely structured documents as a strategy for organizing electronic health care records. Meth Inform Med 1993; 32: 265.

Intelligent Processing of Loosely Structured Documents as a Strategy for Organizing Electronic Health Care Records

1993 ◽  
Vol 32 (04) ◽  
pp. 265-268 ◽  
Author(s):  
D. J. Essin
Keyword(s):  
Health Care ◽  
Health Care Organization ◽  
Database Systems ◽  
Relevant Information ◽  
Full Potential ◽  
Care Organization ◽  
Intelligent Processing ◽  
Data Interchange ◽  
Structured Documents ◽  
Health Care Records

AbstractLoosely structured documents can capture more relevant information about medical events than is possible using today’s popular databases. In order to realize the full potential of this increased information content, techniques will be required that go beyond the static mapping of stored data into a single, rigid data model. Through intelligent processing, loosely structured documents can become a rich source of detailed data about actual events that can support the wide variety of applications needed to run a health-care organization, document medical care or conduct research. Abstraction and indirection are the means by which dynamic data models and intelligent processing are introduced into database systems. A system designed around loosely structured documents can evolve gracefully while preserving the integrity of the stored data. The ability to identify and locate the information contained within documents offers new opportunities to exchange data that can replace more rigid standards of data interchange.

İstanbul İlçelerinin Nüfus Değişimi ve Kütük Bilgilerinin Analizi

2019 ◽  
Vol 6 (1) ◽  
pp. 37-72
Author(s):  
Ulaş Sunata ◽  
Dila Ergül
Keyword(s):  
Population Growth ◽  
Population Growth Rate ◽  
Population Change ◽  
Urban Migration ◽  
Specific Population ◽  
Related Factors ◽  
Internal Migrant ◽  
Level Analysis ◽  
Study District ◽  
Demographic Features

39 ilçesiyle Türkiye’nin en büyük nüfusuna sahip ili İstanbul aynı zamanda Türkiye’nin en çok iç göç alan şehridir. Özellikle kırdan kente göç bağlamında sosyo-ekonomik ve demografik özellikleriyle birçok araştırmaya konu olmuştur. Fakat İstanbul yerleşik nüfusunun Türkiye’nin diğer şehirlerine kayıtlı olma yoğunluğu da önemlidir. Bu çalışmanın amacı 2012 ve 2017 yıllarındaki nüfus değişimini göz önünde bulundurarak İstanbul ilçelerinin ayrıntılı nüfus yoğunluğu ve büyüme analizini yapmak, ilgili faktörleri değerlendirmek, hemşehri ağlarını okumak adına yerleşik nüfus kütük bilgileri bakımından inceleyerek elde edilen örüntüler doğrultusunda ilçe tipolojileri oluşturmaktır. Çalışmanın birinci bölümünde ilgili beş yıllık nüfus değişimlerine göre İstanbul ilçe nüfusları analiz edilmiştir. Ardından her bir ilçe için nüfusa kayıtlı olunan kente göre nüfus büyüme hızlarına bakılarak ilçelerin ağırlıklı olarak barındırdığı hemşehri ağları belirlenmiştir. İkinci bölümde ise ilçeler nüfus değişim özelliklerine göre belirli kategorilere ayrılmış ve bu kategoriler doğrultusunda ilçe tipolojileri oluşturulmuştur..ABSTRACT IN ENGLISHA District Level Analysis of Istanbul’s Population Change (2012-2017)Istanbul having the largest population of Turkey with its 39 districts is the most internal-migrant-receiving city in Turkey. Particularly in the context of rural-to-urban migration, Istanbul has been became a subject of various researches with its socio-economic and demographic features. However, the density of Istanbul’s settled population who registered other cities of Turkey is important. The main aim of this study is to analyse population growth of all districts considering the population change between 2012 and 2017, to evaluate the related factors and to develop a district typology by using the data of settled population according to their family registration in the name of reading the current countryman networks. In the first section of the study, district populations of Istanbul are examined regarding the related five-year change. Afterwards, most repeated countryman networks of all Istanbul’s districts are specified regarding the population growth rate of the registered cities. In the latter section of the study, districts were divided into categories regarding the specific population change features which help to create district typology.

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