Unsere Vison zum tele-TASK Projekt

Im Zentrum der Forschungsarbeit von Prof. Dr. Christoph Meinel und seinen Team auf dem Gebiet des Knowledge Engineering und der Web-University steht das tele-TASK Projekt (Tele-Teaching Anywhere Solution Kit). Es wurde vor mehr als 15 Jahren gestartet, als wir zu erforschen begannen, wie die Internet und Web-Technologien genutzt werden können, um das Lernen (-> E-Learning) und Lehren (-> Tele-Teaching) zu unterstützen und befördern. Unsere Vision was es, ein mobile System zu entwickeln, mit welchen Vorlesungen und Präsentation sehr leicht vollständig aufgezeichnet und über das Internet übertragen werden können, und dann mit dem so gewonnenen Material einerseits neue Lehr- und Lernkonzepte und andererseits innovative Portal- und Navigationstechniken entwickeln und praktisch ausprobieren zu können.

Demonstration des tele-TASK Systems

Unsere Forschungfragen

Über die Jahre hat sich der im tele-TASK Projekt verfolgte Ansatz als sehr fruchtbar erwiesen. Er hat es uns zum einen ermöglicht, wertvolle praktische Erfahrungen und ein fundiertes Verständnis von den Möglichkeiten und Begrenzungen des Tele-Teaching und E-Learning zu gewinnen, und zum anderen wurden wir immer wieder angeregt, ganz neuen gerade aufkommende Techniken im Bereich des Web3.0 - Semantic, Social, und Service Web - für den Bereich der Web-University zu erschließen und zu evaluieren (Details). 

Einige Links zum tele-TASK Portal

 

Hier sind ein paar interessant Links zum tele-TASK Portal mit seinen inzwischen mehr als 4.000 Vorlesungsaufzeichnungen und 30 Mio. Besuchern:

Kauf, Leasing und Vermietung von tele-TASK

Wenn Sie unser Vorlesungen oder Präsentationen aufzeichnen und über das Internet online oder offline streamen wollen, dann können auch Sie unser tele-TASK Aufnahmesystem nutzen. Wie können es käuflich erwerben, leasen oder mieten ... 

Unser tele-TASK Team

  • Prof. Dr. Christoph Meinel (Leiter)
  • Dipl.-Inf. Matthias Bauer
  • Martin Malchow, M.Sc.
  • Xiaoyin CHE
  • Sheng LUO
  • Dr. Haojin Yang
  • Dipl-Inf. Frank Priester (Technischer Support)
  • Frühere Teammitglieder: Dr. Franka Grünewald, Volker Schillings, Tongbo Chen, Mingchao Ma, Mathias Kutzner, Bert Baumann, Long Wang, Andreas Groß, Maria Siebert, ...

tele-TASK Symposia

Wissenschaftliche Publikationen über tele-TASK

Enhance Lecture Archive Search with OCR Slide Detection and In-Memory Database Technology

Martin Malchow, Matthias Bauer, Christoph Meinel
In 2015 IEEE 18th International Conference on Computational Science and Engineering (CSE), pages 176-183, 10 2015 IEEE.

DOI: 10.1109/CSE.2015.19

Abstract:

On the Web there are a lot of frequently used video lecture archives which have grown up fast during the last couple of years. This fact led to a lot of lecture recordings which include knowledge for a variety of subjects. The typical way of searching these videos is by title and description. Unfortunately, not all important keywords and facts are mentioned in the title or description if they are available. Furthermore, there is no possibility to analyze how important those detected keywords are for the whole video. Another lecture archive specific virtue is that every regular university lecture is repeated yearly. Normally this will lead to duplicate lecture recordings. In search results doubling is disturbing for students when they want to watch the most recent lectures from the search result. This paper deals with the idea to resolve these problems by analyzing the recorded lecture slides with Optical Character Recognition (OCR). In addition to the name and description the OCR data will be used for a full text analysis to create an index for the lecture archive search. Furthermore, a fuzzy search is introduced. This will solve the issue of misspelled search requests and OCR detection defects. Additionally, this paper deals with the performance issues of a full text search with an in-memory database, issues in OCR detection, handling duplicate recordings of lectures repeated every year. Finally, an evaluation of the search performance in comparison with other database ideas besides the in-memory database is performed. Additionally, a user acceptability survey for the search results to increase the learning experience on lecture archives was performed. As a result, this paper shows how to handle the big amount of OCR data for a full text live search performed on an in-memory database in reasonable time. During this search a fuzzy search is performed additionally to resolve spelling mistakes and OCR detection problems. In conclusion this paper shows a solution for an enhanced video lecture archive search that supports students in online research processes and enhances their learning experience.

Keywords:

Teleteaching;Tele-Lecturing;Distance Learning;E-Learning;OCR Search;Fuzzy Search;In-Memory Database

BibTeX file

@inproceedings{Martin2015a,
author = { Martin Malchow, Matthias Bauer, Christoph Meinel },
title = { Enhance Lecture Archive Search with OCR Slide Detection and In-Memory Database Technology },
year = { 2015 },
pages = { 176-183 },
month = { 10 },
abstract = { On the Web there are a lot of frequently used video lecture archives which have grown up fast during the last couple of years. This fact led to a lot of lecture recordings which include knowledge for a variety of subjects. The typical way of searching these videos is by title and description. Unfortunately, not all important keywords and facts are mentioned in the title or description if they are available. Furthermore, there is no possibility to analyze how important those detected keywords are for the whole video. Another lecture archive specific virtue is that every regular university lecture is repeated yearly. Normally this will lead to duplicate lecture recordings. In search results doubling is disturbing for students when they want to watch the most recent lectures from the search result. This paper deals with the idea to resolve these problems by analyzing the recorded lecture slides with Optical Character Recognition (OCR). In addition to the name and description the OCR data will be used for a full text analysis to create an index for the lecture archive search. Furthermore, a fuzzy search is introduced. This will solve the issue of misspelled search requests and OCR detection defects. Additionally, this paper deals with the performance issues of a full text search with an in-memory database, issues in OCR detection, handling duplicate recordings of lectures repeated every year. Finally, an evaluation of the search performance in comparison with other database ideas besides the in-memory database is performed. Additionally, a user acceptability survey for the search results to increase the learning experience on lecture archives was performed. As a result, this paper shows how to handle the big amount of OCR data for a full text live search performed on an in-memory database in reasonable time. During this search a fuzzy search is performed additionally to resolve spelling mistakes and OCR detection problems. In conclusion this paper shows a solution for an enhanced video lecture archive search that supports students in online research processes and enhances their learning experience. },
keywords = { Teleteaching;Tele-Lecturing;Distance Learning;E-Learning;OCR Search;Fuzzy Search;In-Memory Database },
publisher = { IEEE },
booktitle = { 2015 IEEE 18th International Conference on Computational Science and Engineering (CSE) },
isbn = { 978-1-4673-8297-7 },
priority = { 0 }
}

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last change: Mon, 26 Oct 2015 10:22:57 +0100

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