TY - GEN
T1 - Towards query optimization for the data web
T2 - 1st ACM Jordan Professional Chapter ISWSA Annual - International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'10
AU - Hawash, Ala'
AU - Deik, Anton
AU - Farraj, Bilal
AU - Jarrar, Mustafa
PY - 2010/6/14
Y1 - 2010/6/14
N2 - Companies, Communities, Research Labs, and even Governments are all competing on publishing structured data in the web in many forms such as RDF and XML. Many Datasets are now being published and linked together, including Wikipedia, Yago, DBLP, IEEE, IBM, Flickr, and US and UK government data. Most of these datasets are published in RDF which is a graph-based data model. However, querying RDF graphs is a major problem which has brought the attention of the research community. Among the many approaches proposed to tune up the performance of queries over data graphs, a number of them proposed to summarize RDF graphs for query optimization; instead of querying a dataset, queries are executed over the summary of the dataset. In order to summarize a dataset, two well known algorithms are being used, namely, Trace Equivalence and Bisimilarity. Nevertheless, these are memory based and thus suffer from scalability problems because of the limitations imposed by the memory. In this paper, we propose disk-based versions of those memory-based algorithms and we adapt them to RDF data. Our proposed algorithms are experimented on relatively large datasets and using different sizes of memory to prove that they are indeed disk based.
AB - Companies, Communities, Research Labs, and even Governments are all competing on publishing structured data in the web in many forms such as RDF and XML. Many Datasets are now being published and linked together, including Wikipedia, Yago, DBLP, IEEE, IBM, Flickr, and US and UK government data. Most of these datasets are published in RDF which is a graph-based data model. However, querying RDF graphs is a major problem which has brought the attention of the research community. Among the many approaches proposed to tune up the performance of queries over data graphs, a number of them proposed to summarize RDF graphs for query optimization; instead of querying a dataset, queries are executed over the summary of the dataset. In order to summarize a dataset, two well known algorithms are being used, namely, Trace Equivalence and Bisimilarity. Nevertheless, these are memory based and thus suffer from scalability problems because of the limitations imposed by the memory. In this paper, we propose disk-based versions of those memory-based algorithms and we adapt them to RDF data. Our proposed algorithms are experimented on relatively large datasets and using different sizes of memory to prove that they are indeed disk based.
KW - bisimilarity
KW - query optimization
KW - RDF
KW - scalability
KW - semantic/data web
KW - trace equivalence
KW - WEB 3.0
UR - https://www.scopus.com/pages/publications/78751663663
U2 - 10.1145/1874590.1874607
DO - 10.1145/1874590.1874607
M3 - Conference contribution
AN - SCOPUS:78751663663
SN - 9781450304757
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 1st ACM Jordan Professional Chapter ISWSA Annual - International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'10
Y2 - 14 June 2010 through 16 June 2010
ER -