• Aucun résultat trouvé

Fast Multimedia Stream Data Mining

N/A
N/A
Info
Télécharger
Protected

Academic year: 2022

Partager "Fast Multimedia Stream Data Mining"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Fast Multimedia Stream Data Mining

Thomas Seidl

RWTH Aachen,

Templergraben 55, 52056 Aachen, Germany seidl@informatik.rwth-aachen.de http://dme.rwth-aachen.de/de/team/seidl

Abstract. In our days, huge and still increasing amounts of data are collected from scientific experiments, sensor and communication net- works, technical processes, business operations and many other domains.

Database and data mining techniques aim at efficiently analyzing these large and complex data to support new insights and decision making based on the extraction of regular or irregular patterns hidden in the data. Current research trends in data analytics are driven by the high volume, velocity, and variety of Big Data.

The talk discusses some challenges in the field. In recent developments for dynamic stream data mining, anytime algorithms play an important role.

Novel hierarchical, statistical indexing structures including BayesTree and ClusTree allow for obtaining high quality results at any time while adapting themselves to varying stream velocities. Particular challenges occur when supervised and unsupervised mining tasks are faced with multimodal streams of complex multimedia objects.

Copyright c 2015 by the papers authors. Copying permitted only for private and academic purposes. In: R. Bergmann, S. G¨org, G. M¨uller (Eds.): Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. Trier, Germany, 7.-9.

October 2015, published at http://ceur-ws.org

7

Références

Télécharger maintenant ( PDF - 1 Page - 116.52 KB )

Documents relatifs

Data Mining Patterns: New Methods and Applications

Therefore, it proposes a method where both the spatial information derived from a complex document image analysis process (layout analysis), and the information extracted from the

Intelligence and Data Mining

The data from the warehouse can be combined with other sources of data, and mined using data mining techniques to generate new insights.. The insights need to be visualized

Data Analysis and Data Mining

In these two environments, we faced the rapid development of a field connected with data analysis according to at least two features: the size of available data sets, as both number

DATA MINING AND ANALYSIS

Finally, on a more personal front, MJZ dedicates the book to his wife, Amina, for her love, patience and support over all these years, and to his children, Abrar and Afsah, and

Data Mining: Concepts and Techniques

Data warehouse technology includes data cleaning, data integration, and on-line analytical processing (OLAP), that is, analysis techniques with functionalities such as summa-

Data Warehousing and Data Mining Techniques for Cyber Security

When applying correlation algorithms, we cor- related each target alert with all other non-background alerts (i.e., the back- ground alerts identified by the Ljung-Box test

Sequential Pattern Mining on Multimedia Data

As pattern mining algorithms deal with symbolic sequences, we present rst how to transform time series related to audio data into symbolic sequences.. Then we show how to use

Data Mining to Support Engineering Design Decision

The goal of our work can thus be summarized as follows: To feedback and har- vest knowledge gained from the aftermarket operations documents to help (a) op- erations engineers