Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
DOCUMENT MANAGEMENT IN HALDOC
Date
1994-01-01
Author
BUYUKKOKTEN, F
ISIKLI, O
KOKSAL, M
Halıcı, Uğur
AYBAY, I
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
61
views
0
downloads
Cite This
URI
https://hdl.handle.net/11511/53042
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Data sharing and access with a corba data distribution service implementation
Dursun, Mustafa; Bilgen, Semih; Department of Electrical and Electronics Engineering (2006)
Data Distribution Service (DDS) specification defines an API for Data-Centric Publish-Subscribe (DCPS) model to achieve efficient data distribution in distributed computing environments. Lack of definition of interoperability architecture in DDS specification obstructs data distribution between different and heterogeneous DDS implementations. In this thesis, DDS is implemented as a CORBA service to achieve interoperability and a QoS policy is proposed for faster data distribution with CORBA features.
Data mining in deductive databases using query flocks
Toroslu, İsmail Hakkı (Elsevier BV, 2005-04-01)
Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deduct...
Multimedia data modeling and semantic analysis by multimodal decision fusion
Güder, Mennan; Çiçekli, Fehime Nihan; Department of Computer Engineering (2015)
In this thesis, we propose a multi-modal event recognition framework based on the integration of event modeling, fusion, deep learning and, association rule mining. Event modeling is achieved through visual concept learning, scene segmentation and association rule mining. Visual concept learning is employed to reveal the semantic gap between the visual content and the textual descriptors of the events. Association rules are discovered by a specialized association rule mining algorithm where the proposed str...
XML Retrieval Using Pruned Element-Index Files
Altıngövde, İsmail Sengör; Ulusoy, Oezguer (2010-01-01)
Data Mining in Deductive Databases Using Query Flocks: Extended Abstract
Toroslu, İsmail Hakkı (2002-12-01)
An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. The query flocks technique, which extends the concept of association rule mining with a "generate-and-test" model for different kind of patterns, can also be applied to deductive databases. In this paper, query flocks technique is extended further, with view definitions including recursive views. We have designed architecture to compile query flocks from datalog...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
F. BUYUKKOKTEN, O. ISIKLI, M. KOKSAL, U. Halıcı, and I. AYBAY, “DOCUMENT MANAGEMENT IN HALDOC,” 1994, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53042.