* SIGKDD: Knowledge Discovery and Data Mining

The database research area has several notable dedicated academic journals ( e. g., ACM Transactions on Database Systems-TODS, Data and Knowledge Engineering-DKE, and more ) and annual conferences ( e. g., ACM SIGMOD, ACM PODS, VLDB, IEEE ICDE, and more ), as well as an active and quite heterogeneous ( subject-wise ) research community all over the world.

Data & Knowledge Engineering 5: 129-137.

Data mining ( the analysis step of the " Knowledge Discovery in Databases " process, or KDD ), is a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large data sets.

Other terms used include Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, etc.

Currently, Data Mining and Knowledge Discovery are used interchangeably.

The premier professional body in the field is the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining ( SIGKDD ).

* DMKD Conference – Research Issues on Data Mining and Knowledge Discovery

* KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining

* PAKDD Conference – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining

* Burges, Christopher J. C .; A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery 2: 121 – 167, 1998

* Data, Knowledge and Services ( DCS ) department: The DCS department is organized around five research teams involving 44 faculty members ( 11 professors, 33 associate professors ).

It covers the following areas: Knowledge discovery ( data mining, complex systems modeling, knowledge engineering ) Data and services engineering ( security and confidentiality, modeling, integration and querying, service composition )

The laboratory leads research on fundamental issues in these areas ( image, and Data, Knowledge and services ).

* Data mining ( which is the analysis step of Knowledge Discovery in Databases ) focuses on the discovery of ( previously ) unknown properties on the data

* Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ( KDD-06 ), 2006.

* Julien B, Philippe C, Pascal C and Pierre C ( 2008 ) " Safeguarding, Integrating and Disseminating Knowledge on Exploited Marine Ecosystems: The Ecoscope " International Marine Data and Information Systems, IMDIS-2008.

* Sänn, A .; Baier, D. ( 2012 ): Lead User Identification Based in Conjoint Analysis Based Product Design, in: Studies in Classification, Data Analysis and Knowledge Organization, Vol.

" In: IEEE Transactions on Knowledge and Data Engineering vol.

Rudi Studer a '*, V. Richard Benjamins b ' c, Dieter Fensel " Knowledge Engineering: Principles and methods ", Data & Knowledge Engineering 25 ( 1998 ) 161-197

* Data & Knowledge Engineering – Elsevier Journal

* IEEE Transactions on Knowledge and Data Engineering

* Guide to the Software Engineering Body of Knowledge

The Software Engineering Body of Knowledge ( SWEBOK ) is a product of the Software Engineering Coordinating Committee sponsored by the IEEE Computer Society.

* An Assessment of Software Engineering Body of Knowledge Efforts — an ACM report critical of the SWEBOK

de: Software Engineering Body of Knowledge

pt: Software Engineering Body of Knowledge

Third-party developers provided additional programming languages ( like OPS5 ) and development tools ( like the Knowledge Engineering Environment, KEE, from Intellicorp ).

* KEE ( Knowledge Engineering Environment ), an expert system shell, from IntelliCorp

ETS University and UQAM were mandated by IEEE to develop the SoftWare Engineering BOdy of Knowledge SWEBOK, which has become an ISO standard describing the body of knowledge covered by a software engineer.

In: Guide to Software Engineering Body of Knowledge.

* Knowledge Engineering and Discovery Research Institute ( KEDRI )

* Civil Engineering Body of Knowledge

Knowledge Engineering Review, 11 ( 3 ): 1 – 40, September 1996.

* Knowledge Engineering is the process of eliciting Knowledge for any purpose be it Expert system or AI development

Some of the trends in Knowledge Engineering in the last few years are discussed in this section. The text below is a brief overview of paper " Knowledge Engineering: Principles and methods " authored by Rudi Studer, V. Richard Benjamins and Dieter Fensel.

Student groups on the campus include STIFKI ( Student Teacher Interaction Forum for Knowledge and Innovation ), IMG ( Information Management Group ), SDSLabs ( Software Development Section Labs ), GIL ( Group for Interactive Learning ), EDC ( Entrepreneurship Development Cell ), HEC ( Himalayan Explorers ' Club ), Literary Society ( Active involvement in debating and quizzing ), a local chapter of ShARE, Spic Macay in addition to student chapters of technical societies such as ASME ( American Society of Mechanical Engineers, IIT Roorkee Student Section ), SAE, IEEE, etc.

Adomavicius, G. and Tuzhilin, A. IEEE Transactions on Knowledge and Data Engineering 06. 2005

-Towards an Introduction to Computational Semiotics-Proceedings of the 2005 IEEE International Conference on Integration of Knowledge Intensive Multi-Agent Systems-KIMAS ' 05, 18 – 21 April 2005, Waltham, MA, USA, pp. 393 – 398. IEEExplore

* IEEE: SWEBOK: Guide to the Software Engineering Body of Knowledge

IEEE Transactions on Knowledge and Data Engineering, 10 ( 1 ): 76 – 90.

", IEEE Transactions on Knowledge and Data Engineering, 24 :( 4 ), p. 619-633, 2012.

", IEEE Transactions on Knowledge and Data Engineering, 22 :( 5 ), p. 730-742, 2010.

Ziv is the recipient of the 1997 Claude E. Shannon Award from the IEEE Information Theory Society and the 2008 Information and Communication Technologies Award of the BBVA Foundation Frontiers of Knowledge Awards.