Multimedia Data Mining - Past, Present and Future

• Autorzy: Janusz Świerzowicz
• Języki publikacji: EN

Abstrakty

EN

The paper presents a short overview of data mining goals and methods. The main aim of multimedia data mining is to gain an interesting knowledge and understand an object recognition in multimedia data that consists of combined images, audio and video media as well as text. There is also a description of supervised and not supervised classification showing an interesting decision rules and schemes. The author presents some advantages offered by multimedia data mining and discusses future trends.

Artykuł stanowi przegląd dziedzin i praktycznych zastosowań multimedialnych baz danych, celów i metod wykorzystywanych w zdobywaniu wiedzy. Głównym celem multimedialnych baz danych jest pozyskiwanie interesującej wiedzy i rozumienie znaczeń utrwalonych w danych multimedialnych, które zawierają współzależne obrazy, przekazy audio i video oraz tekst. Artykuł pokrótce opisuje nadzorowaną i nienadzorowaną klasyfikację, pokazując m.in. interesujące reguły i schematy decyzyjne. Przedstawia główne korzyści multimedialnych baz danych oraz dyskutuje pewne kwestie i przyszłe trendy. (abstrakt oryginalny)

Słowa kluczowe

EN

Knowledge management; Databases; Knowledge based systems; Information systems technology; Literature review

PL

Zarządzanie wiedzą; Bazy danych; Systemy z bazą wiedzy; Technologia systemów informacyjnych; Przegląd literatury

• Czasopismo: Prace Naukowe Akademii Ekonomicznej we Wrocławiu
• Rocznik: 2005
• Numer: nr 1064 Pozyskiwanie wiedzy i zarządzanie wiedzą
• Strony: 280--289

Twórcy

autor

Janusz Świerzowicz

Bibliografia

• Bock H., The Goal of Classification, [in:] Handbook of Data Mining and Knowledge Discovery, Klosgen W., Zytkow J.M. (ed.), Oxford University Press, 2002, 254-258.
• Chen S.C., Shyu M.L., Chen M., Chengcui Z., A Decision Tree-based Multimodal Data Mining Framework for Soccer Goal Detection, IEEE International Conference on Multimedia and Expo (ICME 2004), Taipei, Taiwan.
• Ciaccia P., Patella M., Searching in Metric Spaces with User-Defined and Approximate Distances, ACM Transactions on Database Systems, 2002, 27(4), 398-437.
• Detyniecki M., Marsala C., Fuzzy Multimedia Mining Applied to Video News, IPMU 2002, July 1- 5,2002, Annency, France, 1001-1008.
• Doorn M., de Vries A., The Psychology of Multimedia Databases, Proceedings of the fifth ACM Conference on Digital Libraries, 2000, 1-9.
• Fagin R., Wimmers E., Incorporating User Preferences in Multimedia Queries, Proceedings of the International Conference on Database Theory (ICDT), Volume 1186 of Lecture Notes in Computer Science (LNCS) Springer-Verlag, 1997, 247-261.
• Frischholz R.W., Werner A., Avoiding Replay-Attacs in a Face Recognition System using Head-Pose Estimation, Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG'03), 2003, 1-2.
• Han J., Kamber M., Data Mining. Concepts and Techniques, San Mateo, CA. Morgan Kaufmann., 2001.
• Herena V., Paquet E., le Roux G., Cooperative Learning and Virtual Reality-Based Visualization for Data Mining, Data Mining. Opportunities and Challenges, J. Wang (ed.), IGP, 2003, Hershey, PA, 55-79.
• Hsu J., Critical and Future Trends in Data Mining, A Review of Key Data Mining Technologies/Applications. Data Mining Opportunities and Challenges, J. Wang (ed.), IGP, 2003, Hershey, PA, 437-452.
• Jain A.K., Ross A., Multibiometric Systems, CACM, 2004, 47(1), 34-40.
• Jain A.K., Duin R.P., Mao J., Statistical Pattern Recognition, A Review, MSU-CSE-00-5, 2000.
• Jefferies P., Multimedia, Cyberspace & Ethics, Proceedings of the IEEE International Conference on Information Visualization (IV'00), London, 2000, 99-104.
• Jesorsky O., Kirchberg K.J., Frischholz R.W., Robust Face Detection Using the Hausdorff Distance, Proc. Third International Conference on Audio- and Video-based Biometric Person Authentication. Volume 2091 of Lecture Notes in Computer Science (LNCS) Springer-Verlag, 2001. 90-95.
• Kantardzic M., Data Mining. Concepts, Models, Methods, and Algorithms, John Wiley & Sons, 2003.
• Kossmann D., The State of the An in Distributed Query Processing, ACM Computing Surveys, 2000, 32(4), 422-469.
• Mannila H., Association Rules, [in:] Handbook of Data Mining and Knowledge Discovery, Klosgen W., Zytkow J.M. (ed.), Oxford University Press, 2002, 344-348.
• Marsala C, Fuzzy Decision Trees to Help Flexible Querying, Kybernetika, 2000, 36(6), 689-705.
• Mazurkiewicz A., Krawczyk H., A Parallel Environment for Image Data Mining, Proceedings of the International Conference on Parallel Computing in Electrical Engineering (PARELEC'02), 2002.
• Melton J., Eisenberg A., SQL Multimedia and Application Packages (SQL/MM), SIGMOD Record, 2001, 30(4), 97-102.
• Noirhomme-Fraiture M., Multimedia Support for Complex Multidimensional Data Mining, Procceedings of the First International Workshop on Multimedia Data Mining (MDM/KDD1 2000), in conjunction with Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2000, Boston, MA. ACM Press.
• Oliviera S.R.M., Zaiane O.R., Toward Standardization In Privacy-Preserving Data Mining, Proceedings of the 2nd International Workshop on Data Mining Standards, Services and Platforms, Grossman R.(ed.),KDD 2004, Seattle, 7-17.
• Quinlan J.R., Introduction of decision trees. Machine Learning, 1986,1(1), 86-106.
• Rowe L.A., Jain R., ACM SIGMM Retreat Report on Future Directions In Multimedia Research, ACM SIGMM, 2004, 1-12.
• Skowron A., Swinarski R.W., Information Granulation and Pattern Recognition, [in:] Rough-Neural Computing, Pal S.K., Polkowski L., Skowron A. (eds.), Springer-Verlag, 2004, 599-636.
• Świerzowicz J., Decision Support System for Data and Web Mining Tools Selection. Issues and Trends of Information Technology Management in Contemporary Organizations, Khosrow-Pour M. (ed). IGP, Hershey, London, 2002, 1118-1120.
• Świerzowicz J., Multimedia Data Mining Concept: an Overview, to Appear in Encyclopedia of Multimedia Technology and Networking, Pagani M. (ed). IGP, Hershey, London, 2005.
• Thuraisingham B., XML Databases and the Semantic Web, Auerbach Publications, 2002.
• Weiss G.M., Mining with Rarity, A Unifying Framework, SIGKDD Explorations, 2004,6(1), 7-19.
• Wijesekera D., Barbara D., Multimedia applications, [in:] Handbook of Data Mining and Knowledge Discovery, Klosgen W., Zytkow J.M. (ed.), Oxford University Press, 2002, 758-769.
• Zaiane O.R., Han J., Li Z., Hou J., Mining MultiMedia Data, In Proceedings of Meeting of Minds, CASCON'98, Toronto, Canada, 1-18.
• Zaiane O.R., Han J., Zhu H., Mining Recurrent Items in Multimedia with Progressive Resolution Refinement, [in:] Proceedings of the International Conference on Data Engineering ICDE'00, San Diego, CA, IEEE, 15-28.
• Zhang J., Hsu W., Li Lee M., Image Mining. Issues, Frameworks and Techniques, Proceedings of the Second International Workshop on Multimedia Data Mining (MDM/KDD' 2001) in conjunction with Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2001, Zaiane O.,R, Simoff S.J.(eds.) San Francisco, USA, ACM Press, 13-21.
• Zhang J., Hsu W., Li Lee M., An Information driven Framework for Image Mining, Proceedings of 12th International Conference on Database and Expert Systems Applications (DEXA), Munich, Germany, 2001.