Clustering of XML documents based on structure and aggregated content

Rezk, Nermeen Gamal; Sarhan, Amany M.; Algergawy, Alsayed

doi:10.1109/icces.2016.7821981

Cited by 4 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, it is appropriate for big datasets. After that, Rezk et al [31] proposed another considerable clustering XML document based on CAS features which improved the quality of clusters by considering more features from source schemas. The authors of [31] used the XEdge algorithm [32] for structural similarity, and in order to get content similarity, aggregate three similarity measures; Cosine, Jaccard, and Jensen-Shannon divergence.…”

Section: Xml Document Clustering Based On Content and Structurementioning

confidence: 99%

“…After that, Rezk et al [31] proposed another considerable clustering XML document based on CAS features which improved the quality of clusters by considering more features from source schemas. The authors of [31] used the XEdge algorithm [32] for structural similarity, and in order to get content similarity, aggregate three similarity measures; Cosine, Jaccard, and Jensen-Shannon divergence. The proposed approach was compared with another XML document clustering approach with the same structural algorithm, while its content similarity was computed by the Jaccard measure.…”

Section: Xml Document Clustering Based On Content and Structurementioning

confidence: 99%

See 1 more Smart Citation

XML Clustering Framework Based on Document Content and Structure in a Heterogeneous Digital Library

Samadi,

(Corresponding Author)

2023

MJCS

View full text Add to dashboard Cite

As textually published information is increasing in digital libraries, efficient retrieval methods are required. Textual documents in a digital library are available in various structures and contents. It is possible to represent these documents with hierarchical levels of granularity when these are organized in XML structure to improve precision by focused retrieval. By this means, contextual elements of each document can be retrieved from a known structure. One solution for retrieving these elements is clustering from a combination of Content and Structural similarities. To achieve this, a novel two-level clustering framework based on Content and Structure is proposed. The framework decomposes a document into meaningful structural units and analyzes all its rich text in its own structure. The quality of the proposed framework was experimented on a heterogeneous XML document collection, having varieties of data sources, structures, and content, be represented as a sample of a real digital library. This collection was made with capabilities to test all of our objectives. The clustering results were evaluated by the Entropy criterion. Finally, the Content and Structure clustering was compared with the usual clustering based on the Content Only to prove the efficacy of considering structural features against the existing Content Only methods in the retrieval process. The total Entropy results of the two-level Content and Structural clustering are almost twice better than the Content Only clustering approach. Consequently, the proposed framework has the ability to improve Information Retrieval systems from two points of view: i) considering the structural aspect of text-rich documents in the retrieval process, and ii) replacing the document-level retrieval with the element-level retrieval.

show abstract

Section: Xml Document Clustering Based On Content and Structurementioning

confidence: 99%

Section: Xml Document Clustering Based On Content and Structurementioning

confidence: 99%

XML Clustering Framework Based on Document Content and Structure in a Heterogeneous Digital Library

Samadi,

(Corresponding Author)

2023

MJCS

View full text Add to dashboard Cite

show abstract

“…This fact has driven researchers to create solutions that minimize loads on networks and increase the speed at which services may be provided. One of the newest suggested improvements is a web-based messages aggregator, which works to combine several messages into a single message by removing duplicate content [20], [21]. However, the aggregation's efficacy depends on the degree of similarity between aggregated messages.…”

mentioning

confidence: 99%

Dynamic Hilbert clustering based on convex set for web services aggregation

Al-Musawi,

Al-Shammary

2023

IJECE

View full text Add to dashboard Cite

<div align=""><span lang="EN-US">In recent years, web services run by big corporations and different application-specific data centers have all been embraced by several companies worldwide. Web services provide several benefits when compared to other communication technologies. However, it still suffers from congestion and bottlenecks as well as a significant delay due to the tremendous load caused by a large number of web service requests from end users. Clustering and then aggregating similar web services as one compressed message can potentially achieve network traffic reduction. This paper proposes a dynamic Hilbert clustering as a new model for clustering web services based on convex set similarity. Mathematically, the suggested models compute the degree of similarity between simple object access protocol (SOAP) messages and then cluster them into groups with high similarity. Next, each cluster is aggregated as a compact message that is finally encoded by fixed-length or Huffman. The experiment results have shown the suggested model performs better than the conventional clustering techniques in terms of compression ratio. The suggested model has produced the best results, reaching up to 15 with fixed-length and up to 20 with Huffman</span></div>

show abstract

“…ISSN: 2502-4752  aggregated SOAP messages [17], [18]. Moreover, it would improve performance by reducing the size of the transmitted data.…”

mentioning

confidence: 99%

Static Hilbert convex set clustering for web services aggregation

Al-Musawi,

Al-Shammary

2023

IJEECS

View full text Add to dashboard Cite

<span>Web services' high levels of duplicate textual structures have caused network bottlenecks and congestion. Clustering and then aggregating similar web services as one compressed message can potentially achieve network traffic reduction. In this paper, a static Hilbert clustering as new model for clustering web services based on convex set similarity is proposed. Mathematically, the proposed model calculates similarity among simple object access protocol (SOAP) messages and then cluster them based on higher similarity values. Next, each cluster is aggregated as a compact message. The experiments have explained the proposed model performance as it has outperformed the convention clustering strategies in both compression ratio and clustering time. The best results have been achievable by the proposed model has reached up to (15) with fixed-length and up to (21) with Huffman.</span>

show abstract

Clustering of XML documents based on structure and aggregated content

Cited by 4 publications

References 45 publications

XML Clustering Framework Based on Document Content and Structure in a Heterogeneous Digital Library

XML Clustering Framework Based on Document Content and Structure in a Heterogeneous Digital Library

Dynamic Hilbert clustering based on convex set for web services aggregation

Static Hilbert convex set clustering for web services aggregation

Contact Info

Product

Resources

About