Towards Flexible Similarity Analysis of XML Data

The problem of supporting

similarity analysis of XML data

is a major problem in the

data fusion

research area. Several approaches have been proposed in literature, but

lack of flexibility

represents a hard challenge to be faced-off, especially in modern

Cloud Computing

environments. Inspired by this motivation, we propose

SemSynX

,

a novel technique for supporting similarity analysis of XML data via semantic and syntactic heterogeneity/homogeneity detection

.

SemSynX

retrieves several

similarity scores

over input XML documents, thus enabling flexible management and “customization” of similarity tools over XML data. In particular, the proposed technique is highly

customizable

, and it permits the specification of

thresholds

for the requested

degree of similarity

for paths and values as well as for the

degree of relevance

for path and value matching. Also,

selection of paths

and

semantics-based comparison of label content

are supported. It thus makes possible to “adjust” the similarity analysis depending on the nature of the input XML documents.