Διαχείριση Εξέλιξης σε Δεδομένα Ιστού με τη Χρήση Σύνθετων Αλλαγών

Γαλάνη, Θεοδώρα; Galani, Theodora

dc.contributor.author	Γαλάνη, Θεοδώρα
dc.contributor.author	Galani, Theodora
dc.date.accessioned	2021-12-20T08:14:31Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/54194
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.21892
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	change modeling	en
dc.subject	change definition language	en
dc.subject	change detection	en
dc.subject	querying data evolution	en
dc.subject	RDF(S)	en
dc.subject	μοντελοποίηση αλλαγών	el
dc.subject	γλώσσα ορισμού αλλαγών	el
dc.subject	εντοπισμός αλλαγών	el
dc.subject	επερώτηση εξελισσόμενων δεδομένων	el
dc.subject	RDF(S)	el
dc.subject	XML	el
dc.subject	XML	en
dc.title	Διαχείριση Εξέλιξης σε Δεδομένα Ιστού με τη Χρήση Σύνθετων Αλλαγών	el
dc.title	Managing Evolution in Web Data through Complex Changes	en
heal.type	doctoralThesis
heal.classification	Computer Science	en
heal.classification	Επιστήμη Υπολογιστών	el
heal.dateAvailable	2022-12-19T22:00:00Z
heal.language	en
heal.access	embargo
heal.recordProvider	ntua	el
heal.publicationDate	2021-11-02
heal.abstract	The increasing amount of information published on the web poses new challenges for data management. A central issue concerns evolution management. Data published on the web frequently change, as errors may need to be fixed or new knowledge has to be incorporated. Data consumers need to know what changed among versions, as well as how and why. Revisiting past data snapshots and versions may not be enough for tracking and understanding the semantics of data evolution. Such an activity may require a search that moves backwards and forwards in time, spread across disparate parts of a database, and perform complex queries on the semantics of the changes that modified the data, a task which may be even more intensive for large datasets. In our view, for understanding data evolution changes should be treated as first-class-citizens. This means that human-readable, semantically rich changes are supported, along with any relations between them. Treating changes as first-class-citizens poses several challenges regarding modeling, defining, detecting and querying changes. In this thesis, we study these directions and work upon two basic standards for web data: RDF and XML. First, we propose our approach on modeling, defining and detecting changes in the context of RDF(S) knowledge bases. Overall, the proposed approach offers expressiveness and flexibility in terms of evolution interpretation. The proposed complex changes provide additional information for interpreting past data, via capturing relations between changes and allowing interpreting evolution in multiple ways. Specifically, we proposed modeling and supporting simple and complex changes, as well as any relations among them, for interpreting evolution on RDF(S) knowledge-bases. Simple changes are fine-grained and application/data-agnostic changes, while complex changes are coarse-grained and application/data-specific changes. Furthermore, we formally defined an intuitive, user-friendly language, based on change semantics for defining complex changes. We formally defined the language syntax, via EBNF specification, as well as the language semantics. Moreover, we presented a detection algorithm for the proposed complex change definition language. The dynamics model followed is to detect changes between dataset versions. Therefore, the ultimate goal of defining complex changes is identifying complex change instances between dataset versions, via the complex change detection process. Also, the correctness of the proposed implementation with respect to the language semantics is presented. Finally, we extensively evaluated the proposed approach both qualitatively and experimentally. The qualitative evaluation showed the added value of our approach compared to related works. The experimental evaluation showed the complex change language expressiveness and the detection performance. The proposed language is proven to be adequate in expressing useful changes and facilitating user in analyzing evolution. The response time of the detection process is examined in terms of increasing dataset size. The experimental evaluation is performed over both artificial and real data, proving the effectiveness of our approach. Second, we propose a query language for querying both data versions and change structures in the context of semistructured XML data. This work builds upon evo-graph, a model that captures evolving data along with changes, and evoXML, an XML representation of evo-graph. Specifically, we formally defined evo-path, an XPath extension for performing time-aware and change-aware queries on evo-graph. Evo-path allows querying both data history and change structure in a uniform way, supporting temporal, evolution and causality queries. We presented the evo-path syntax, we defined evo-path formal semantics and we presented an implementation based on a formal translation of evo-path into equivalent XPath expressions over evoXML. Also, we implemented and experimentaly evaluated the basic concepts of evo-graph in the C2D framework, using XML technologies. The space efficiency of evoXML is examined for various configurations, as well as the performance of the reduction process, the process for generating a snapshot holding under a specific time instance from evo-graph.	en
heal.advisorName	Βασιλείου, Ιωάννης
heal.committeeMemberName	Βασιλείου, Ιωάννης
heal.committeeMemberName	Καντερέ, Βασιλική
heal.committeeMemberName	Σταύρακας, Ιωάννης
heal.committeeMemberName	Τσουμάκος, Δημήτριος
heal.committeeMemberName	Βασιλειάδης, Παναγιώτης
heal.committeeMemberName	Παπαστεφανάτος, Γεώργιος
heal.committeeMemberName	Στάμου, Γεώργιος
heal.academicPublisher	Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.fullTextAvailability	false
heal.fullTextAvailability	false