Database Management Research
http://learnpythondatasciencenyc.site/
http://bigdatascienceblockchainnyc.site/
http://ebscorp.us/
Universities with top ranking and the research they are doing:
http://www.eecs.berkeley.edu/Research/Areas/DBMS/
Research on database management predictive modeling
http://www.dba-oracle.com/oracle_tips_predictive_modeling_10g.htm
http://www.isogov.com/services/data-management/
http://www.igi-global.com/journals/
Journals:
http://airccse.org/journal/ijdms/ijdms.html
http://www.igi-global.com/journal/journal-database-management-jdm/1072
http://old.library.georgetown.edu/newjour/j/msg04736.html
http://www.springer.com/computer/ai/book/978-3-642-20038-0
Research topics in MIS:
Topics include conceptual data modeling, data security and integrity, distributed data management, recovery strategies, and overall database administration. Students learn the SQL language—an industry standard for relational databases—and design their own database applications using an available database management system such as Microsoft Access or Oracle.
Tutorials:
http://www.scribd.com/doc/36879334/MIS-Database-Management-Systems
http://pages.cs.wisc.edu/~anhai/courses/784-sp09-anhai/
From: www.stern.nyu.edu/UC/CurrentStudents/Academics/MajorsAndMinors/CON_021959#data_mining_for_business_intelligence
Data Mining for Business Intelligence
INFO-UB 57 3 units.
This course teaches students how to structure and solve business problems using data-driven analysis and modeling. The course has three closely related goals:
1. To introduce students to state-of-the-art data-mining methods that support decision making by extracting useful knowledge from the increasingly large volume of data that organizations collect.
2. To provide an analytical framework within which students can apply these data-mining techniques successfully to data-rich business problems.
3. To give students hands-on experience with using the techniques to extract knowledge from data.
The emphasis is on creative problem formulation and analysis. The course assumes prior knowledge of Microsoft Excel and the fundamentals of finance, marketing, and operations at the level of the core courses at Stern. Prior experience with a programming language or with data mining is useful but not necessary.
Intelligent database systems:
http://en.wikipedia.org/wiki/Intelligent_database
http://books.google.co.in/books?id=8v956y7Lvt0C&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-12089-3
http://phys.org/journals/international-journal-of-intelligent-information-and-database-systems/
http://www.ingentaconnect.com/content/ind/ijiids
https://www.inderscience.com/www/IJIIDS_leaflet.pdf
http://bigdatascienceblockchainnyc.site/
http://ebscorp.us/
Universities with top ranking and the research they are doing:
http://www.eecs.berkeley.edu/Research/Areas/DBMS/
Research on database management predictive modeling
http://www.dba-oracle.com/oracle_tips_predictive_modeling_10g.htm
http://www.isogov.com/services/data-management/
http://www.igi-global.com/journals/
Journals:
http://airccse.org/journal/ijdms/ijdms.html
http://www.igi-global.com/journal/journal-database-management-jdm/1072
http://old.library.georgetown.edu/newjour/j/msg04736.html
http://www.springer.com/computer/ai/book/978-3-642-20038-0
Research topics in MIS:
Topics include conceptual data modeling, data security and integrity, distributed data management, recovery strategies, and overall database administration. Students learn the SQL language—an industry standard for relational databases—and design their own database applications using an available database management system such as Microsoft Access or Oracle.
Tutorials:
http://www.scribd.com/doc/36879334/MIS-Database-Management-Systems
http://pages.cs.wisc.edu/~anhai/courses/784-sp09-anhai/
From: www.stern.nyu.edu/UC/CurrentStudents/Academics/MajorsAndMinors/CON_021959#data_mining_for_business_intelligence
Data Mining for Business Intelligence
INFO-UB 57 3 units.
This course teaches students how to structure and solve business problems using data-driven analysis and modeling. The course has three closely related goals:
1. To introduce students to state-of-the-art data-mining methods that support decision making by extracting useful knowledge from the increasingly large volume of data that organizations collect.
2. To provide an analytical framework within which students can apply these data-mining techniques successfully to data-rich business problems.
3. To give students hands-on experience with using the techniques to extract knowledge from data.
The emphasis is on creative problem formulation and analysis. The course assumes prior knowledge of Microsoft Excel and the fundamentals of finance, marketing, and operations at the level of the core courses at Stern. Prior experience with a programming language or with data mining is useful but not necessary.
Intelligent database systems:
http://en.wikipedia.org/wiki/Intelligent_database
http://books.google.co.in/books?id=8v956y7Lvt0C&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-12089-3
http://phys.org/journals/international-journal-of-intelligent-information-and-database-systems/
http://www.ingentaconnect.com/content/ind/ijiids
https://www.inderscience.com/www/IJIIDS_leaflet.pdf
