A Study on Fuzzy Query with Weights Based on Fuzzy Logic

Natural Language Processing (NLP) is the electronic tactic to analyzing text that is depends on both a set of ideas and a set of technologies. Natural Language Processing (NLP) is a subfield of artificial intelligence and etymology it thinks about the issues of computerized era and comprehension of regular human dialects. Common dialect era frameworks change over data from PC databases into ordinary sounding human dialect, and normal dialect understanding frameworks change over specimens of human dialect into more formal representations that are less demanding for PC projects to control. The Fuzzy logic-based approach provides another alternative for effective natural language analysis. It is commonly recognized that many phenomena in natural language lend themselves to descriptions by Fuzzy mathematics, including Fuzzy sets, Fuzzy relations and Fuzzy logic. By defining a Fuzzy logic system and acquiring proper rules, we hope that difficulties in analysis of speech can be alleviated. The goal of NLP is to enable communication between people and computers without resorting to memorization of complex commands and procedures.

Fuzzy querying with SQL: Fuzzy view-based approach

Nowadays several works have been proposed that allow users to perform fuzzy queries on relational databases. But most of these systems based on an additional software layer to translate a fuzzy query and a supplementary layer of a classic database management system (DBMS) to evaluate fuzzy predicates, which induces an important overhead. They are not also easy to implement by a non-expert user. Here we have proposed a simple and intelligent approach to extend the SQL language to allow us to write flexible conditions in our queries without the need for translation. The main idea is to use a view to manipulate the satisfaction degrees related to user-defined fuzzy predicates, instead of calculating them at runtime employing user functions embedded in the query. Consequently, the response time of executing a fuzzy query statement will be reduced. This approach allows us to easily integrate most fuzzy request characters such as fuzzy modifiers, fuzzy quantifiers, fuzzy joins, etc. Moreover, we present a user-friendly interface to make it easy to use fuzzy linguistic values in all clauses of a select statement. The main contribution of this paper is to accelerate the execution of fuzzy query statements.

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

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.

IMPLEMENTASI METODE FUZZY QUERY DATABASE MODEL TAHANI SEBAGAI INFERENCE ENGINE PEMBERI REKOMENDASI OBJEK WISATA DI KABUPATEN BOYOLALI

Saat ini berwisata telah menjadi bagian dari kebutuhan masyarakat. Hal ini dinilai sebagai salah satu investasi yang besar bagi pemasukan daerah yang menjadi tujuan wisata termasuk Kabupaten Boyolali. Namun banyaknya objek wisata di Kabupaten Boyolali sering kali menyulitkan masyarakat untuk mendapatkan objek wisata yang sesuai dengan keinginan atau kebutuhannya. Dari permasalahan tersebut, muncul gagasan untuk membangun sebuah sistem pendukung keputusan yang memudahkan masyarakat dalam pemilihan objek wisata di Kabupaten Boyolali. Sistem pendukung keputusan tersebut akan menggunakan metode Fuzzy Query Database Model Tahani. Untuk mendapatkan informasi yang diperlukan digunakan teknik observasi, wawancara dan studi literatur. Bahasa pemrograman yang akan digunakan adalah PHP. MySQL sebagai DBMS dan Dreamweaver sebagai software pembangunnya. Dengan adanya sistem pendukung keputusan ini diharapkan masyarakat akan lebih mudah untuk mendapatkan objek wisata yang sesuai dengan keinginan atau kebutuhannya.