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
Data Mining in Deductive Databases Using Query Flocks: Extended Abstract
Date
2002-12-01
Author
Toroslu, İsmail Hakkı
Metadata
Show full item record
Item Usage Stats
132
views
0
downloads
Cite This
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 into SQL in order to be able to use commercially available DBMS's as an underlying engine. Since recursive datalog views (IDB's) cannot be converted directly into SQL statements, they are materialized before the final compilation operation.
Subject Keywords
Information retrieval
,
Knowledge based systems
,
Program compilers
,
Query languages
,
Relational database systems
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=1642409373&origin=inward
https://hdl.handle.net/11511/71280
Conference Name
Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Improving the performance of Hadoop/Hive by sharing scan and computation tasks
Özal, Serkan; Toroslu, İsmail Hakkı; Doğaç, Asuman; Department of Computer Engineering (2013)
MapReduce is a popular model of executing time-consuming analytical queries as a batch of tasks on large scale data. During simultaneous execution of multiple queries, many oppor- tunities can arise for sharing scan and/or computation tasks. Executing common tasks only once can reduce the total execution time of all queries remarkably. Therefore, we propose to use Multiple Query Optimization (MQO) techniques to improve the overall performance of Hadoop Hive, an open source SQL-based distributed warehouse sy...
Geotechnical Considerations for Mining Method Selection of a Potential Underground Iron Ore Mine in Mideastern, Turkey
Karpuz, Celal (null; 2013-10-01)
Geotechnical analysis plays an important role in determining mining method selection. This study presents the geotechnical design analysis of a potential U/G iron ore Mentes Mine at Yahyali province of Kayseri district in Turkey. Iron ore body is initially planned to be mined by using long hole mining method. Detailed geotechnical site investigation and laboratory work are carried out to assess the applicability of the selected mining method. Diamond-drilled borehole cores are logged and geotechnical charac...
Concurrency control for distributed multiversion databases through time intervals
Halıcı, Uğur (1991-04-01)
© 1991 ACM.Multiversion Schedulers are now a widely accepted method for enhancing performance of the concurrency control component of a database. When the read and write sets of transactions are known in advance, the amount of concurrency provided by the Multiversion Schedulers can further be improved. In this paper, a new concurrency control technique, which uses multiversion data in conjunction with predeclared read-write sets and the Time Interval technique is suggested. With the proposed method, a trans...
Efficient computation of strong partial transitive-closures
Toroslu, İsmail Hakkı (null; 1993-01-01)
The development of efficient algorithms to process the different forms of the transitive-closure (TC) queries within the context of large database systems has recently attracted a large volume of research efforts. In this paper, we present a new algorithm suitable for processing one of these forms, the so called strong partially-instantiated, in which one of the query's argument is instantiated to a set of constants and the processing of which yields a set of tuples that draw their values form both of the q...
WIP - SKOD: A Framework for Situational Knowledge on Demand
Palacios, Servio; Solaiman, K.M.A.; Angın, Pelin; Nesen, Alina; Bhargava, Bharat; Collins, Zachary; Sipser, Aaron; Stonebraker, Michael; Macdonald, James (2019-01-01)
Extracting relevant patterns from heterogeneous data streams poses significant computational and analytical challenges. Further, identifying such patterns and pushing analogous content to interested parties according to mission needs in real-time is a difficult problem. This paper presents the design of SKOD, a novel Situational Knowledge Query Engine that continuously builds a multi-modal relational knowledge base using SQL queries; SKOD pushes dynamic content to relevant users through triggers based on mo...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
İ. H. Toroslu, “Data Mining in Deductive Databases Using Query Flocks: Extended Abstract,” NC, Amerika Birleşik Devletleri, 2002, vol. 6, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=1642409373&origin=inward.