1. Practice Report and Master thesys Mihailov (Hriplivîi) Tatiana 3rd TecTNet consortium meeting

2. At practice, in Iaşi, Romania, our team had chance  to see how works a center of research and technological transfer by a university  the procedure of registration of an invention in Romania  the state entities coneceted with inovation and technological transfer in Romania and to realize that there are similar entities in Moldova delivering the same functions  scientific-technological park,  an incubator,  National Inventics Institute  Department of information technologies in TUIASI  for me: consult the literature from faculties libraries, at the planned topic for master’s thesys, because this topic is quite new for my university library(Data Mining). Spor la muncă! Andrei Chiciuc 2

3. 3 activities of research and development, including fundamental and applicative sciencifical research experimental development and inovation consulting, projecting, services and tehnological transfer in specific fields of the university. Functions of CCTT Polytech

4.  Evolution of database and information technology.  how to extract information from huge amount of data  How can be increase the quality of service delivered to enterprises and individuals?  How many working places will be in a certain area, in 3 years  How long will it take to find a specific specialist? 4 Introduction to thesys

5. Theoretical study OLAP and data mining advanced analysis methods for vacancies job market Practical side 5 Topic of thesys is -Separately Data mining and OLAP tools and techniques -Data mining and OLAP integrated solutions -Making queries with data mining and OLAP -Set of recommemdation s when is better each technique

6. Making a set of recomendations of when which is the best Analysis of the way Data Mining with OLAP may be applied Analysis of the way Data Mining may be used Analysis of the way OLAP may be used Critical analysis of literature on the topic Objectives 6

7. 7 Chapter 1 analyzing data prediction tendency Data mining Online Analytical Processing multi- dimensional information systems OLAP

8.  classification;  prediction;  clustering;  statistical methods;  tendency detection;  association rules;  sequence analysis;  text mining;  data visualization;  artificial neural networks  decision trees  the nearest-neighbor method.  etc. 8 Some techniques for data mining

9. 9 The main techniques for OLAP Slices of the cube Quering multidimensional data

10. Techniques of prediction, tendency detection, etc. Multidimensional data 10 The main techniques for combining both

11. The concept of solution which i suggest  relational data base +  which questions might be interested in the enterprises and the individuals.  fulfill it with the data from sites with available labour.  data mining queries  multidimensional data base  to query  Build a combined technique  queries with combined/integrated techniques: data mining and OLAP= OLAM  Conclude a set of recomendations:  what thechnique for what question is better. 11 Chapter 2

12. 12 What is done until now

13. 13 Comparison of data mining and OLAP and combined techniques Data miningOLAPcombined Data Mining techniques can operate on any kind of unprocessed or even unstructured information, The final result can be very simple (e.g., frequency tables, descriptive statistics, simple cross- tabulations) With the coupling of these enhancement techniques, OLAP functionality can be improved with res pect to its performance and visualization. they can also be applied to the data views and summaries generated by OLAP to provide more in-depth and often more multidimensional knowledge. or more complex (e.g., they may involve seasonal adjustments, removal of outliers, and other forms of cleaning the data). it integrates the enhanced OLAP with a data mining technique of hierarchical clustering. visualization and can get a higher degree. Could be considered an analytic extension of OLAP. the growing complexity and volumes of the data to be analyzed impose new challenges on OLAP systems

14. 14 Bibliography 1.THOMSEN E.. OLAP Solutions. Building Multidimensional Information Systems. 2nd ed. New York: ed. John Wiley & Sons, Inc., 2002. 661 p. 2.ILEANĂ, I., ROTAR, C., MUNTEAN, M. Inteligenţa artificială. Alba Iulia: ed. Aeternitas, 2009. 298 p 3.Data Mining http://documents.software.dell.com/statistics/textbook/data-mining-techniques#mininghttp://documents.software.dell.com/statistics/textbook/data-mining-techniques#mining 4.MARINOVA, N. Instrumentele data mining – parte componenta a procesului de descoperire a cunostintelor. In: Economica. 2005, nr. 2(50). ISSN 1810-9136. 5.Data Mining 101: Tools and Techniques ​ https://iaonline.theiia.org/data-mining-101-tools-and-techniqueshttps://iaonline.theiia.org/data-mining-101-tools-and-techniques 6.FRAND, J.. Data Mining: What is Data Mining? http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm 7.HAN, J., KAMBER, M. Data Mining: Concepts and Techniques. San Francisco: ed. Elsevier, 2006. 743 p. 8.Data warehousing http://documents.software.dell.com/statistics/textbook/data-mining- techniques#warehousinghttp://documents.software.dell.com/statistics/textbook/data-mining- techniques#warehousing 9.What is Data Mining (Predictive Analytics, Big Data) http://www.statsoft.co.za/textbook/data-mining- techniques/http://www.statsoft.co.za/textbook/data-mining- techniques/ 10.КОЧ, К.. Что такое ERP, In CIO. 2001, 15 november, Перевод Даулета Тынбаева http://www.erp- online.ru/erp/http://www.erp- online.ru/erp/ 11.http://www.heliumv.org/en/opensource-Industry_Solution-3.htmlhttp://www.heliumv.org/en/opensource-Industry_Solution-3.html 12.Data Mining - Query Language http://www.tutorialspoint.com/data_mining/dm_query_language.htmhttp://www.tutorialspoint.com/data_mining/dm_query_language.htm 13.ZHAO, Y.. R and Data Mining: Examples and Case Studies. Amsterdam: ed. Elsevier, 2013. 156 p. 14.Computational Methods, http://www.statsoft.com/Textbook/Classification-Trees#computationhttp://www.statsoft.com/Textbook/Classification-Trees#computation 15.Neural Networks, http://documents.software.dell.com/statistics/textbook/data-mining-techniques#neuralhttp://documents.software.dell.com/statistics/textbook/data-mining-techniques#neural 16.How is OLAP Technology Used? http://olap.com/olap-definition/http://olap.com/olap-definition/ 17.BELLAACHIA, A.. Data Warehousing and OLAP Technology http://www.seas.gwu.edu/~bell/csci243/lectures/data_warehousing.pdf http://www.seas.gwu.edu/~bell/csci243/lectures/data_warehousing.pdf 18.OLAP and Data Mining http://docs.oracle.com/cd/B28359_01/server.111/b28313/bi.htmhttp://docs.oracle.com/cd/B28359_01/server.111/b28313/bi.htm 19.On-Line Analytic Processing (OLAP) http://documents.software.dell.com/statistics/textbook/data-mining- techniques#olaphttp://documents.software.dell.com/statistics/textbook/data-mining- techniques#olap 20.INMON, W. H.. Building the Data Warehouse 3rd ed.