Session details: Tools, infrastructures, architectures and techniques for deploying pervasive applications in assistive environments I

The progress in mobile devices and wireless networks technologies has considerably contributed to integrate pervasive computing expertise in many domains with the aim of improving the quality of services and users' mobility. However, in many situations, users may face difficult situations, needing faster decisions, where classical systems impose the submission of classic queries in which crisp conditions must be carefully fixed. This inconvenience limits the potential of pervasive applications accessed by users having few times to make the right decisions. To introduce the contributions of this paper, we choose the medical domain as example. We considered a pervasive healthcare application under which physicians haven't enough time to fix carefully their queries in some emergency cases. Therefore, they are allowed to flexibly express their preferences using conjunctive fuzzy queries and to quickly receive best answers anywhere and anytime while treating patients in the shortest time and consequently free resources for eventually other urgent requests. In this work, we consider, in general, the problem of efficiently finding the top-K answers for a conjunctive fuzzy query from the top-N conjunctive query rewritings of the query. In particular, we propose an efficient algorithm called the Top-N rewritings algorithm for finding the top-N query rewritings of a medical conjunctive fuzzy query using a set of conjunctive crisp views. At the best of our knowledge, this algorithm is the first to generate, without computing all possible rewritings, the N best ones ordered according to their satisfaction degrees and that are likely to return the best K-answers for the user fuzzy query. The relevance of a query rewriting is estimated using a second algorithm called the Query-satisfaction computing algorithm proposed to estimate, through the histograms maintained to approximate the distribution of set of values returned by the rewriting and to which fuzzy predicates are related, the pertinence of a conjunctive fuzzy query rewriting rather than accessing the database relations.

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Efficient Algorithm for Answering Fuzzy Medical Requests in Pervasive Healthcare Information Systems

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.2017040103 ◽

2017 ◽

Vol 12 (2) ◽

pp. 46-64

Author(s):

Wissem Labbadi ◽

Jalel Akaichi

Keyword(s):

Efficient Algorithm ◽

Query Rewriting ◽

Pervasive Healthcare ◽

Classical Systems ◽

Fuzzy Query ◽

Healthcare Application ◽

The Right ◽

Difficult Situations ◽

Pervasive Applications

The progress in mobile devices and wireless networks technologies has considerably contributed to integrate pervasive computing expertise in many domains with the aim of improving the quality of services and users' mobility. However, in many situations, users may face difficult situations, needing faster decisions, where classical systems impose the submission of classic queries in which crisp conditions must be carefully fixed. This inconvenience limits the potential of pervasive applications accessed by users having few times to make the right decisions. To introduce the contributions of this paper, we choose the medical domain as example. We considered a pervasive healthcare application under which physicians haven't enough time to fix carefully their queries in some emergency cases. Therefore, they are allowed to flexibly express their preferences using conjunctive fuzzy queries and to quickly receive best answers anywhere and anytime while treating patients in the shortest time and consequently free resources for eventually other urgent requests. In this work, we consider, in general, the problem of efficiently finding the top-K answers for a conjunctive fuzzy query from the top-N conjunctive query rewritings of the query. In particular, we propose an efficient algorithm called the Top-N rewritings algorithm for finding the top-N query rewritings of a medical conjunctive fuzzy query using a set of conjunctive crisp views. At the best of our knowledge, this algorithm is the first to generate, without computing all possible rewritings, the N best ones ordered according to their satisfaction degrees and that are likely to return the best K-answers for the user fuzzy query. The relevance of a query rewriting is estimated using a second algorithm called the Query-satisfaction computing algorithm proposed to estimate, through the histograms maintained to approximate the distribution of set of values returned by the rewriting and to which fuzzy predicates are related, the pertinence of a conjunctive fuzzy query rewriting rather than accessing the database relations.

Download Full-text