Improved Methods for Signature-Tree Construction

Tousidou, Eleni

doi:10.1093/comjnl/43.4.301

Cited by 39 publications

(57 citation statements)

References 31 publications

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“…The root has at least two and at most B children, where other nodes have at least b and at most B children. Unlike B + -tree, b = B 2 , here, 1 ≤ b ≤ B 2 [28]. b = 1 is the "free-at-empty" strategy, i.e., containing as few as one child [17], while b = B 2 is the "merge-at-half" strategy.…”

Section: Deletionmentioning

confidence: 99%

gStore: a graph-based SPARQL query engine

et al. 2013

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We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph-based. We store RDF data as a large graph, and also represent a SPARQL query as a query graph. Thus the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.

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Section: Deletionmentioning

confidence: 99%

gStore: a graph-based SPARQL query engine

et al. 2013

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“…Interesting performance evaluation of signature indexes can be found in [12]. Much effort is put into further improving signature indexes [17]. Signature indexes are well suited to handle large collections of sets but their performance degrades quickly with the increase of the domain size of the indexed attribute.…”

Section: Related Workmentioning

confidence: 99%

“…Internal nodes of the S-tree contain the superimposition of all index key roots contained in the lower level nodes that they point to. An interesting discussion on signature trees construction and improvement can be found in [17]. We believe that it is sufficient to store in the S-tree only the roots of the index keys.…”

Section: Hierarchical Bitmap Indexmentioning

confidence: 99%

Hierarchical Bitmap Index: An Efficient and Scalable Indexing Technique for Set-Valued Attributes

Morzy¹,

Morzy²,

Νανόπουλος³

et al. 2003

Advances in Databases and Information Systems

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Abstract. Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing techniques for indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases. In this paper we present the hierarchical bitmap index-a novel technique for indexing set-valued attributes. Our index permits to index sets of arbitrary length and its performance is not affected by the size of the indexed domain. The hierarchical bitmap index efficiently supports different classes of queries, including subset, superset and similarity queries. Our experiments show that the hierarchical bitmap index outperforms other set indexing techniques significantly.

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“…Moreover, it is a common method to evaluate indexing algorithms by comparing the number of disk accesses, e.g., (Faloutsos & Christodoulakis, 1984) (Helmer & Moerkotte, 1997) (Tousidou, Nanopoulos, & Manolopoulos, 2000).…”

Section: Family Of Sequence Indexing Methodsmentioning

confidence: 99%

“…Implementation details and performance evaluation of different signature indexes can be found in (Helmer, 1997) (Helmer & Moerkotte, 1999). Improved methods for signature tree construction were proposed in (Tousidou, Nanopoulos, & Manolopoulos, 2000).…”

Section: Review Of Existing Indexing Techniquesmentioning

confidence: 99%

Indexing Techniques for Web Access Logs

Manolopoulos¹,

Morzy²,

Morzy³

et al.

Web Information Systems

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Access histories of users visiting a web server are automatically recorded in web access logs.Conceptually, the web-log data can be regarded as a collection of clients' access-sequences where each sequence is a list of pages accessed by a single user in a single session. This chapter presents novel indexing techniques that support efficient processing of so-called pattern queries, which consist in finding all access sequence s that contain a given subsequence. Pattern queries are a key element of advanced analyses of web-log data, especially those concerning typical navigation schemes. In this chapter, we discuss the particularities of efficiently processing user access-sequences with pattern queries, compared to the case of searching unordered sets. Extensive experimental results are given, which examine a variety of factors and illustrate the superiority of the proposed methods over indexing techniques for unordered data adapted to access sequences.

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Improved Methods for Signature-Tree Construction

Cited by 39 publications

References 31 publications

gStore: a graph-based SPARQL query engine

gStore: a graph-based SPARQL query engine

Hierarchical Bitmap Index: An Efficient and Scalable Indexing Technique for Set-Valued Attributes

Indexing Techniques for Web Access Logs

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