Semantic warehousing

In data management, semantic warehousing is a methodology of digitalized text data using similar functions to Data warehousing (DW), such as ETL(Extract, transform, load), ODS(Operational data store), and MODEL. Key value operation is less useful for the digitalized text. Semantic warehousing is different from DW in that it utilizes semantic information from text data.

Semantic warehousing is different from a search engine in that semantic information from text data is stored in the database (DBMS).

Though data is the most important word in the computing era, it cannot explain human knowledge well yet. Data (numeric data) is a key element of computing systems for certain organizations (especially companies, enterprises), but no performance-oriented organization needs something to gather and use knowledge or human feeling. Semantic warehousing will be equally or more important than data warehousing in the future.

Definition

Semantic warehousing is a conceptual and functional term meaning to gather from a source, semantically define, and provide information from digitalized text type data.^{[citation needed]}

Background

Data warehousing (DW) is popular these days. Gathering data from systems that generate transactions, data warehouses become a base of information. The key to a data warehouse is a model (called datamart) and that model is made up of dimensions (key) and measures (value). Users get information from the models by doing certain operations. Online analytical processing (OLAP) is the most important operation for users to get information from the DW models. Handling dimensions with pivoting, drilling, slice & dice operations, users get numeric values like sales amounts, growth rates, etc. Various areas of this world have defined and appeared on the World Wide Web (Internet), eager to present their contents in a semantic way. Briefly speaking, semantic warehousing has a data warehousing body and a search head, and ontology features.

Data warehousing contributed to companies’ business values and many solutions and tools are commercially successful. Analysis of internal data delivers a certain level of business values, in contrast to this, the Semantic warehousing environment has not yet matured. The capacity of social data is increasing rapidly and various efforts of finding value from that data are widely known as Big data, etc. Semantic warehousing can be the mainstream of treating data and intelligence of the social world in the future, though it is defined with other keywords.

At the Big data era, semantic processing is going to become a major IT process. Semantic warehousing is the digital infrastructure of Intelligence.^{[citation needed]}

Practices

▣ Medical area (Clinical Information)

Some hospitals implement semantic warehousing for clinical information (SWCI). Medical information is now at a knowledge network level. UMLS defines a semantic knowledge network of medical language. Currently, medical information is stored in databases and not fully used for clinical purposes. Semantic warehousing is the next stage of digitalized medical information.

SWCI is a name of a conceptual system of clinical information.
Named by Juhan Kim (SNUH, Seoul National University Hospital) and Bohyon Hwang, YongChan Keum in 2008.

Defined architecture on SWCI:

Semantic-oriented cleansing
Semantic-oriented meta management
Clinical (Medical) knowledge basement
Semantic-oriented user intelligence

▣ Intelligence Area

At the point of Big data usage, intelligence reporting can be valuable results.

Source information
Manage intelligence & Semantic data
Intelligence service & use

http://www.globalintelligence.kr/gibigdata/ Archived 2018-09-26 at the Wayback Machine

Connected area

- Big data
- Semantic web
- Ontology
- Knowledge
- Medical and healthcare : EMR (Electronic Medical Record), EHR (Electronic Health Record)
- Data warehouse
- AI (artificial intelligence)

References

BI Laboratory of Seoul National University Hospital
Smith, Barry Kumar, Anand and Schulze-Kremer, Steffen (2004) Revising the UMLS Semantic Network, in M. Fieschi, et al. (eds.), Medinfo 2004, Amsterdam: IOS Press, 1700.
Foundations of Data Warehouse Quality:

 Data Quality article mentioning that semantically rich DW.
 [1]

An Integrative and Uniform Model for Metadata Management in Data Warehousing Environment.

 Semantic metadata and technical metadata.
 http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-19/paper12.pdf

Effective Query Expansion using Condensed UMLS Metathesaurus for Medical Information Retrieval

http://www.e-hir.org/journal/view.html?uid=201&start=&sort=&scale=&key=all&oper=&key_word=UMLS&year1=&year2=&Vol=&Num=&PG=&book=&mod=vol&sflag=&sub_box=Y&aut_box=Y&sos_box=&pub_box=Y&key_box=&abs_box=&year=

A Study of Effective Unified Medical Language System Concept Indexing in Radiology Reports

http://www.e-hir.org/journal/view.html?uid=226&start=&sort=&scale=&key=all&oper=&key_word=UMLS&year1=&year2=&Vol=&Num=&PG=&book=&mod=vol&sflag=&sub_box=Y&aut_box=Y&sos_box=&pub_box=Y&key_box=&abs_box=&year=

Developing a Reference Terminology Model for Health Care Using an Object-Oriented Approach

http://www.e-hir.org/journal/view.html?uid=311&start=&sort=&scale=&key=all&oper=&key_word=UMLS&year1=&year2=&Vol=&Num=&PG=&book=&mod=vol&sflag=&sub_box=Y&aut_box=Y&sos_box=&pub_box=Y&key_box=&abs_box=&year=

UMLS(Unified Medical Language System)의 증상용어와 국내의무기록에서 사용되는 증상용어와의 비교연구

http://www.e-hir.org/journal/view.html?uid=922&start=&sort=&scale=&key=all&oper=&key_word=UMLS&year1=&year2=&Vol=&Num=&PG=&book=&mod=vol&sflag=&sub_box=Y&aut_box=Y&sos_box=&pub_box=Y&key_box=&abs_box=&year=

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