An auto-indexing technique for databases based on clustering

Zaman, Musharraf; Surabattula, J.; Gruenwald, Le

doi:10.1109/dexa.2004.1333569

Cited by 6 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data Clustering [5,7,9,10] is one data mining technique, which is used in many fields, including pattern recognition, bioinformatics and image analysis. It divides a data set into clusters by using some defined distance measure, which determines the similarity or proximity of two data elements.…”

Section: Data Clusteringmentioning

confidence: 99%

Using Data Clustering to Optimize Scatter Bitmap Index for Membership Queries

Weahama

Vanichayobon

Manfuekphan

2009

2009 International Conference on Computer and Automation Engineering

View full text Add to dashboard Cite

Decision support systems that retrieve information from a data warehouse environment are usually designed to process complex and ad hoc queries. Indexing techniques based on bitmap representations can be used to improve the efficiency of information retrieval. Scatter Bitmap Index uses less space and is more CPU-efficient than other bitmap indexing techniques. It is simple to represent, and improves query processing time by utilizing low-cost Boolean operations and multiple index scans. The Scatter Bitmap Index technique performs simple predicate conditions on the index level before going to the primary data source. Furthermore, Scatter Bitmap Index can be optimized by applying K-mode Clustering, which finds relationships among attribute values in queries. In this paper, we show that Data Clustering with Scatter Bitmap Index can improve query processing time for membership queries.

show abstract

Section: Data Clusteringmentioning

confidence: 99%

Using Data Clustering to Optimize Scatter Bitmap Index for Membership Queries

Weahama

Vanichayobon

Manfuekphan

2009

2009 International Conference on Computer and Automation Engineering

View full text Add to dashboard Cite

show abstract

“…Completing the two main steps indeed brought us to perform choices, but other options would be easy to consider. For instance, the data mining technique we selected for building a candidate index configuration is frequent itemset mining, but another study explored clustering instead (Zaman, Surabattula, & Gruenwald, 2004). Besides, we have also used clustering for materialized view selection.…”

Section: General Principle Of Our Approachmentioning

confidence: 99%

Data mining-based materialized view and index selection in data warehouses

Aouiche¹,

Darmont²

2009

J Intell Inf Syst

View full text Add to dashboard Cite

Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.

show abstract

“…Although Data Mining has already proved to be extremely useful to select physical data structures that enhance performance, such as indexes or materialized views [1, 8,9,83], few fragmentation approaches that exploit Data Mining exist in literature. Therefore, it is reasonable to claim that the latter is a relatively-novel area of research, and a promising direction for future efforts in data warehouse and database fragmentation techniques.…”

Section: Data-mining-based Fragmentationmentioning

confidence: 99%

Fragmenting very large XML data warehouses via K-means clustering algorithm

Cuzzocrea

Darmont

Mahboubi

2009

IJBIDM

View full text Add to dashboard Cite

XML data sources are gaining popularity in the context of Business Intelligence and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing complex and heterogeneous data. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore, recent research efforts are focusing on horizontal fragmentation techniques, which are able to overcome the above limitations. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses. In this paper, we propose the use of the K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses. We complement our analytical contribution with a comprehensive experimental assessment where we compare the efficiency of our proposal against existing fragmentation algorithms.

show abstract

An auto-indexing technique for databases based on clustering

Cited by 6 publications

References 6 publications

Using Data Clustering to Optimize Scatter Bitmap Index for Membership Queries

Using Data Clustering to Optimize Scatter Bitmap Index for Membership Queries

Data mining-based materialized view and index selection in data warehouses

Fragmenting very large XML data warehouses via K-means clustering algorithm

Contact Info

Product

Resources

About