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Fuzzy Querying Based Tool for Building Courses Evaluation Tests Fuzzy Querying based Tool for Building Courses Evaluation Tests Livia Borjas1, Josué Ramírez1, Rosseline Rodríguez2 and Leonid Tineo2 1 Departamento de Informática, IUT Federico Rivero Palacios, Caracas, Venezuela 2 Departamento de Computación, Universidad Simón Bolívar, Caracas, Venezuela Keywords: Computer Aided Education, e-Learning, Fuzzy Querying. Abstract: In this paper, we present a tool intended for helping in exam configuration based on the reutilization of questions according to user preferences. It is a real life application of fuzzy querying that fulfils an actual need of academic personal at a high studies institution of Venezuela. This application uses the fuzzy querying language SQLf, on top of an existing relational DBMS by means a logic layer named SQLfi. Final users of our application are professors of any area without knowledge of fuzzy sets and databases. We use criteria for the test preparation that support fuzzy terms. These terms can be adjusted to user preferences. Graphic user interfaces are provided in order to perform such adjusts as well as exam configuration and any other fuzzy querying operation. We present here the Database Design, the process test construction and the management of preferences. 1 INTRODUCTION previous experiences. Consequently, a database storing this information is required. The use of these Professors throughout their carriers have to measure data might require the definition of searches based the performance of their students basically through on preferences including fuzzy terms. the application of written tests. The design of these Another important factor is the consideration of tests might become sometimes a discouraging the grades or results obtained in previous tests (for process given its repetitive nature. Furthermore, example: outstanding, excellent, good, regular and some professors, due to their work as researchers poor), which allows professors to project the and because of the administrative burden, do not expected performance of their students in a test. have enough time to dedicate to the preparation of This might be considered through the generation tests using all the creativity and awareness required. of statistics based on non-accurate gradual By virtue thereof, we propose the creation of a requirements defined by professors. It is also software tool for supporting tests’ preparation important for professors to have a scenario providing process. In connection therewith, the needs of potential different tests based on established criteria, professors will be considered the following criteria allowing for making the decision of choosing which for the preparation of a test or question: difficulty tests better fit to the needs of the students being (for example: low, moderate/low, moderate, high evaluated at a given time. and very high), time to solve it (for example: long, We present here a fuzzy querying based moderate and short), correction time (for example: application for support professor’s work of long, moderate and short). The aforementioned preparing evaluation tests. This application is made criteria are not accurate; therefore the definition of in the frame of an effort of the Venezuelan IUT fuzzy terms is required. These terms are subjective Federico Rivero Palacio of providing professors since, for each term, each professor will differently some computer aided support tools for academic conceive the range of values. Also, these terms do work. show the present imprecision at this application, as This paper has the following structure: Section 2 well as the feasibility of the fuzzy logic use in its presents Fuzziness in Databases. Section 3 briefly queries. introduces the Fuzzy Querying Language SQLf that Additionally, the preparation of tests is is used in this development research. Section 4 commonly based on existent tests to benefit from describes main System Features such as the Database Design, the functionality of Examination Borjas L., Ramírez J., Rodríguez R. and Tineo L.. 343 Fuzzy Querying based Tool for Building Courses Evaluation Tests. DOI: 10.5220/0004151003430349 In Proceedings of the 4th International Joint Conference on Computational Intelligence (FCTA-2012), pages 343-349 ISBN: 978-989-8565-33-4 Copyright c 2012 SCITEPRESS (Science and Technology Publications, Lda.) IJCCI2012-InternationalJointConferenceonComputationalIntelligence Tests Construction with fuzzy queries expressing considering other results that approximate in certain user desires and the Management of Preferences. degree to the response; these could be presented to Section 5 is devoted to giving some Concluding user as alternate results. Search criteria in the Remarks and Future works. systems based on classic DBMS are accurate, while Fuzzy DBMS allow for defining more flexible criteria that better adjust to human thought. Classical 2 FUZZINESS IN DATABASES databases use all the results obtained, while Fuzzy Querying Systems only use those results that meet the minimum level of satisfaction defined by users. The growing ambitious use of information systems in the amount of data volume and data recovery Classical queries limit their results since are based mechanisms has fostered the accelerated on the use of Boolean Logic, where a proposition only accepts two values: true or false. development of technology. In this regard, query specification mechanisms that resembling natural Systems based on fuzzy databases are closer to language expression and human thought is sought. human through. The use of fuzzy logic better adapts to real world and it even is able to understand and Therefore, it would be convenient to provide flexible query capabilities allowing users to express deal with linguistic terms or expressions in natural requirements involving preferences. language, such as "it is really easy", "he is not too long", “he is too short”, etc. A powerful tool used in the management of databases for the purpose of making queries in a more flexible way has been the fuzzy sets (Zadeh, 1965; Cox, 1995; Galindo, 2008; Pivert and Bosc, 3 FUZZY QUERYING 2012), which vests traditional databases with a more LANGUAGE intuitive approach closed to the natural language when retrieving information from data sources. This Different fuzzy query languages have been proposed type of sets has been applied to several knowledge (Bosc and Pivert, 1995a; Galindo, 2005; Kacpryzyk areas, such as Control Systems, Decision-making and Zadrozny, 1995; Nakajima et al, 1995). On of Systems, and Expert Systems. In (Ma and Yan, the most remarkable efforts is SQLf (Bosc and 2010), it is stated that the theory of fuzzy sets has Pivert, 1995a). It is an extended SQL with the been widely applied to extend several models and application of the Fuzzy Set Theory aimed at management systems database, which has resulted in expressing flexible queries on Relational Databases. many contributions. In this regard, different research This query language has been updated with different projects have been conducted worldwide focused on versions of SQL (Goncalves et al, 2009). SQLf including fuzziness in databases (Bosc and Pivert, allows the use of different fuzzy elements in 1995a; Kacpryzyk and Zadrozny, 1995; Goncalves constructions of SQL:2003 where a condition is and Tineo, 2008; Galindo, 2008; Yager, 2010; Ma involved. and Ya, 2010; Pivert and Bosc, 2012). Nevertheless, For the purpose of defining the preferences of there are few known developed applications that each user, SQLf counts on a series of sentences enjoy the benefits of fuzzy databases (Galindo et al, belonging to the DDL (Data Definition Language) 2006; Goncalves and Tineo, 2008; Fernandez et al, for fuzzy components. In this (SQLf - DDL) terms 2008; Delgado et al, 2008). such as predicates, modifiers, quantifiers and We spoke of imprecision in database when the comparators are involved, which have a meaning information is incomplete or uncertain. Thus, an that is specific to a group of users or individuals. approximate value or a term describing it is stored Fuzzy predicates are the atomic components of instead of an exact value. In general, we could be fuzzy logic. These correspond to the class of terms talking about data inaccuracies or imprecision in known as "linguistic labels". In SQLf, fuzzy queries, i.e., imprecise terms may be stored as data predicates are defined through the following values or could be used in the query criteria. Thus sentence: there are mainly two different approaches that address the vagueness of the databases. Those CREATE FUZZY PREDICATE <name> ON <domain> AS <fuzzyset> focused on the data (Galindo, 2008) and those focused on queries (Pivert and Bosc, 2012). Where <domain> is a range of scale characters The results produced by an application system, defined by the user, <fuzzyset> is a specification based on conventional DBMS, strictly adjust to the of the fuzzy set, which membership function be compliance with the search criteria. But, they are not defined by 344 FuzzyQueryingbasedToolforBuildingCoursesEvaluationTests . a trapezium with the syntax: are made on independent crisp criteria. On the other (<value1>,<value2>,<value3>,<value4>) hand, in a fuzzy query, we establish flexible using the “infinite” special value, the selection criteria. Such criteria are based on fuzzy semantics of which is infinite; logic involving linguistic terms. They express user . extension with the syntax: {<d1>/<v1>, ... , preferences. Fuzzy criteria are combined giving a <dn>/<vn>} where <di> is a degree and <vi> global satisfaction
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