Editing Online analytical processing (section)

===Other types===
The following acronyms are also sometimes used, although they are not as widespread as the ones above:

* '''WOLAP''' – Web-based OLAP
* '''DOLAP''' – [[Desktop computer|Desktop]] OLAP
* '''RTOLAP''' – Real-time OLAP
* '''GOLAP''' – Graph OLAP<ref>{{Cite news|url=https://www.datanami.com/2016/12/07/week-graph-entity-analytics/|title=This Week in Graph and Entity Analytics|date=2016-12-07|work=Datanami|access-date=2018-03-08|language=en-US}}</ref><ref>{{Cite news|url=http://www.dbta.com/Editorial/News-Flashes/Cambridge-Semantics-Announces-AnzoGraph-Support-for-Amazon-Neptune-and-Graph-Databases-123280.aspx|title=Cambridge Semantics Announces AnzoGraph Support for Amazon Neptune and Graph Databases|date=2018-02-15|work=Database Trends and Applications|access-date=2018-03-08|language=en-US}}</ref>
* '''CaseOLAP''' – Context-aware Semantic OLAP,<ref name = "textcubes">{{cite web |title=Multi-Dimensional, Phrase-Based Summarization in Text Cubes |url=http://sites.computer.org/debull/A16sept/p74.pdf |last1=Tao|first1=Fangbo | last2=Zhuang|first2=Honglei | last3=Yu|first3=Chi Wang| first4=Qi|last4=Wang | first5=Taylor|last5=Cassidy | first6=Lance|last6=Kaplan | first7=Clare|last7=Voss| last8=Han | first8=Jiawei | date=2016}}</ref> developed for biomedical applications.<ref>{{Cite journal|last1=Liem|first1=David A.|last2=Murali|first2=Sanjana|last3=Sigdel|first3=Dibakar|last4=Shi|first4=Yu|last5=Wang|first5=Xuan|last6=Shen|first6=Jiaming|last7=Choi|first7=Howard|last8=Caufield|first8=John H.|last9=Wang|first9=Wei|last10=Ping|first10=Peipei|last11=Han|first11=Jiawei|date=2018-10-01|title=Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease|journal=American Journal of Physiology. Heart and Circulatory Physiology|volume=315|issue=4|pages=H910–H924|doi=10.1152/ajpheart.00175.2018|issn=1522-1539|pmid=29775406|pmc=6230912}}</ref> The CaseOLAP platform includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube,<ref>{{cite book |last1=Lee |first1=S. |last2=Kim |first2=N. |last3=Kim |first3=J. |title=2014 IEEE Fourth International Conference on Big Data and Cloud Computing |chapter=A Multi-dimensional Analysis and Data Cube for Unstructured Text and Social Media |date=2014 |pages=761–764 |doi=10.1109/BDCloud.2014.117|isbn=978-1-4799-6719-3 |s2cid=229585 }}</ref><ref>{{cite journal |last1=Ding |first1=B. |last2= Lin|first2= X.C.|last3=Han|first3=J.|last4=Zhai| first4=C.|last5=Srivastava|first5= A.|last6=Oza|first6= N.C.|title=Efficient Keyword-Based Search for Top-K Cells in Text Cube |journal=IEEE Transactions on Knowledge and Data Engineering |date=December 2011 |volume=23 |issue=12 |pages=1795–1810 |doi=10.1109/TKDE.2011.34|s2cid=13960227 }}</ref><ref>{{cite book |last1=Ding |first1=B. |last2=Zhao |first2=B. |last3=Lin |first3=C.X. |last4=Han |first4=J. |last5=Zhai |first5=C. |title=2010 IEEE 26th International Conference on Data Engineering (ICDE 2010) |chapter=TopCells: Keyword-based search of top-k aggregated documents in text cube |date=2010 |pages=381–384 |doi=10.1109/ICDE.2010.5447838|isbn=978-1-4244-5445-7 |citeseerx=10.1.1.215.7504 |s2cid=14649087 }}</ref><ref>{{cite book |last1=Lin |first1=C.X. |last2=Ding |first2=B. |last3=Han |first3=K. |last4=Zhu |first4=F. |last5=Zhao |first5=B.  |title=2008 Eighth IEEE International Conference on Data Mining |chapter=Text Cube: Computing IR Measures for Multidimensional Text Database Analysis |date=2008 |pages=905–910 |doi=10.1109/icdm.2008.135|isbn=978-0-7695-3502-9 |s2cid=1522480 |url=https://ink.library.smu.edu.sg/sis_research/1008 }}</ref><ref>{{cite book |last1=Liu |first1=X. |last2=Tang |first2=K. |last3=Hancock |first3=J. |last4=Han |first4=J. |last5=Song |first5=M. |last6=Xu |first6=R. |last7=Pokorny |first7=B. |editor1-last=Greenberg |editor1-first=A.M. |editor2-last=Kennedy |editor2-first=W.G. |editor3-last=Bos |editor3-first=N.D. |title= Social Computing, Behavioral-Cultural Modeling and Prediction: 6th International Conference, SBP 2013, Washington, DC, USA, April 2-5, 2013, Proceedings|publisher=Springer |location=Berlin, Heidelberg |isbn=978-3-642-37209-4 |pages=321–330 |edition=7812 |chapter= |date=2013-03-21 }}</ref> and quantifying user-defined phrase-category relationships using the core CaseOLAP algorithm.