Intuitionistic Fuzzy XML Query Matching

XML is undoubtedly becoming the predominant de facto standard for data representation and communication, especially on the web, which in turn is causing XML data repositories to grow rapidly. Current XML Query languages, such as Xquery, have limited capabilities in querying multiple data sources with different structures (schemas) which is inefficient. Therefore, an urgent need has been identified for XML querying techniques that can overcome the rising diversity in XML data schemas. In this work, we propose our approach of using Intuitionistic Fuzzy Trees (IFTr) to achieve approximate XML query matching by considering a novel approach of matching arcs as basic units of data schemas. Additionally, we provide an algorithm for rewriting the original query to be able to retrieve data from local data sources. Our approach was tested using synthetic data sources with high degree of structural diversity, and it proved useful result while pertaining good performance and low memory usage.

show abstract

“…Before presenting our proposal, we present a few basic definitions (More definitions were presented in previous works [26,27].…”

Section: Intuitionistic Fuzzy Treesmentioning

confidence: 99%

Approximate XML query matching and rewriting using Intuitionistic Fuzzy Trees

Alzebdi

Chountas

Atanassov

2012

2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS

Self Cite

View full text Add to dashboard Cite

show abstract

Arc-Based Soft XML Pattern Matching

Alzebdi

Chountas

Atanassov

2013

Flexible Query Answering Systems

View full text Add to dashboard Cite

Integrating flexibility and fuzziness into a question driven query model

Sarhan

Rokne

Alhajj

2018

Information Sciences

View full text Add to dashboard Cite

Data plays an important role in our daily life. Thus, data collection, storage, maintenance and processing continue to attract considerable attention. Data may exist in various formats, ranging from unstructured to structured as the two extremes. Traditionally, researchers and practitioners cooperated and developed various data models which form the main foundation for existing database management systems. The relational data model is still dominating despite the rapid development in the techniques used for data collection, storage and processing. Further, a relational database management system supports a structured query language (SQL) for data processing, and it is not possible to access and retrieve data from a relational database without knowing how to use SQL. However, the wide usage of relational databases motivated researchers to develop more user friendly interfaces which would allow a larger population of users to access relational databases. Such interfaces range from visual to natural language based. This thesis contributes a question driven query model which falls under the natural language based category. The target is to make databases reachable by a larger population, especially after the Internet increased database availability. The proposed model supports fuzziness where every user is given the freedom to define his/her own understanding of fuzzy terms. The developed system absorbs the fuzzy understanding of each user to utilize it while deciding on the result to be communicated back as answer to the raised question. Data mining techniques are employed to guide users in defining their fuzzy understanding. The developed model is intended to help users to retrieve the data they want from a relational database without expecting them to know SQL. In the current version only questions written in English are allowed. The system handles different types of questions, such as (1) simple questions, (2) complex questions with inner joins and where conditions, (3) questions that involves the usage of aggregate functions (e.g., min, max, etc.), and (4) questions with fuzzy i terms. The reported test results demonstrate the effectiveness of the developed system in handling various types of questions raised by a heterogeneous set of users ranging from professionals to naive.

show abstract

Intuitionistic Fuzzy XML Query Matching

Cited by 4 publications

References 16 publications

Approximate XML query matching and rewriting using Intuitionistic Fuzzy Trees

Approximate XML query matching and rewriting using Intuitionistic Fuzzy Trees

Arc-Based Soft XML Pattern Matching

Integrating flexibility and fuzziness into a question driven query model

Contact Info

Product

Resources

About