Cortez in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science has been examined and is recommended for acceptance. Accepted and approved as partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science. Mathematical and Computer Sciences Unit Department of Physical Sciences Department of Physical Sciences and Mathematics.
Data modeling has been a complicated task for students taking a database course as well as novice developers. However, data modeling should not be ignored by people in a database related course or profession as it is required to develop a well structured database. Although data modeling, in particular ER modeling, is a complex task, it is not well provided with tool-based support [7].
Various model development tools exist but learning tools for data modeling are very few in number. This study aims to develop a computer-aided instruction (CAI) for the development of entity-relationship diagrams and normalization.
Background of the Study
The existing data modeling applications do not care about citizens' knowledge of creating models and databases.
Statement of the Problem
Objectives of the Study
Significance of the Project
Scope and Limitations
The system can only translate database problem specifications into a normalized ERD up to third normal form (3NF). The system does not include instructions for other data models such as network model, UML, DFD and similar. The ERD constructs used in the system are limited to entities, relationships, cardinality, attributes, generalization/specialization, completeness constraints, and disconnection constraints.
Words used in the input that are not found in the system dictionary (simple txt file or words) cause an error. The inclusion of the keyword unique or distinct indicates whether the attributes should be primary key or not. Because of the keyword also, the subtype must be only one of the objects, thus indicating incoherence.
Because of the connector or the subtype must be only one, more, or all objects, indicating overlap.
Assumptions
Review of Related Literature 8
The tool provides a step-by-step evaluation of the input, showing the users a partially normalized database at each step. Using a graphical format can improve communication of the design between the designer and the user, and the designer and the people who will implement it. The cardinality defines the number of instances of one entity for a single occurrence of the related entity.
The disjoint constraint indicates that an instance of a supertype can only be one of a subtype. The overlap restriction indicates that an instance of a supertype can be more than one subtype. The completeness constraint indicates whether an instance of a supertype must be a member of at least one subtype.
Total specialization states that an instance of the supertype must belong to at least one of the subtypes. Partial specialization indicates that an instance of the supertype may or may not belong to any subtype.
Normalization
Repeated attributes on the right side must be identified in all functional dependencies; one copy of the redundant attributes should be kept and the others deleted. Attributes B, C, and D in functional dependency FD3 are removed, because attributes B, C, and D appear in FD1 and FD1 has only one attribute on the left. Attributes C and D are removed from functional dependency FD1, because FD1 has more attributes on the right side than functional dependency FD2.
Computer-Aided Instruction
Data Modelling
Database Problem Specification
ACME-DB integrates a UML diagramming tool with an automated assessment for its students, automatic correction of exercises and automated feedback from the system. Students' answers to the problems are evaluated using the domain knowledge of the system, represented as a set of constraints. KERMIT is more of a problem-solving environment where the system provides feedback and assistance to its users in solving a problem.
Users will input a set of database specifications in English sentences, and the system will generate the corresponding ER model. An entity relationship diagram (ERD) is an important data model that helps organize data into entities and defines relationships between entities. An identifier (or key) is used to uniquely identify one and only one instance of an entity.
A selection constraint indicates that each occurrence of an entity may or may not be associated with an instance of another entity. A mandatory constraint indicates that each occurrence of an entity must be associated with at least one or more instances of another entity.
Natural Language Processing
Stanford Parser
In view of the input: "The heaviest rain ever recorded in India shut down Mumbai's financial center, snapped communication lines, closed airports and forced thousands of people to sleep in their offices or go home overnight, officials said in day." can perform the following functions. Part-of-speech tagging is the process of assigning words to their corresponding part of speech in a sentence. This function can be performed by a POS tagger, a software or program that tags or tags each word in a sentence to its corresponding part of speech.
Part of speech is a term in traditional grammar for the eight categories into which words are classified according to their function in a sentence, such as noun, verb, adjective, pronoun, etc. In modern linguistics, the term part of speech has generally been discarded. in favor of a word class or syntactic category marker. The tags or tags that follow each word indicate its classification in the parts of speech.
The output of the given input as generated by Stanford Parser is as follows Den/DT strongest/JJS rain/NN ever/RB recorded/VBN in/IN. Using the same input as used in the part-of-speech markup, Stanford would generate the following dependencies entered.
Typed Dependencies of the Stanford Parser
Heuristics Rules
These rules use the Stanford parser's typed dependency parsing results to extract data such as entities and relationships. A tuple is always an ordered triple, indicating a relationship and the two entities of the relationship. The left side of the rules are the conditionally entered dependencies required to trigger the rule.
The binary relation nsubj holds the verb and the subject of the sentence, and the binary relation dobj holds the verb and the object of the sentence. By applying the rule of these conditions, the tuple hteaches,instructor,coursei can be extracted from the example sentence. By applying the rule of these conditions, the tuple hteach,instructor,departmenti can be extracted from this example.
Cardinalities between entities of a relationship can also be extracted from the Stanford Parser using rules. By applying the rule under these conditions, the tuple hzero or many,instructor,coursesi can be extracted from this example.
Design and Implementation 27
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