Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

Why should nulls in a relation be avoided as far as possible? Discuss the problem of spurious tuples and how we may prevent it.

Short Answer

Expert verified
Nulls in a relation should be avoided as they can lead to uncertainty in comparison, data integrity issues and can even influence system performance. Spurious tuples, occurring due to improper join operations causing additional rows that did not exist in original relations, can produce inaccurate results. They can be prevented by proper join conditions, including all necessary attributes, and database schema normalization.

Step by step solution

01

Defining Nulls in a Relation

Null in a relational database is a value that is undefined or unknown. It is not equivalent to zero or blank, but rather signifies the absence of a value. Here we will explain why these null values should be carefully managed or avoided altogether.
02

Explaining the Problems caused by Nulls

Nulls can lead to several issues in databases. They make comparison uncertain and cause problems with data integrity and database operations like Select, Insert, Update and Delete. For some database systems, Nulls can have an impact on how the system processes information and can affect the performance of the system. Additionally, null values can lead to inaccurate results when performing calculations or data analysis.
03

Understanding Spurious Tuples

Spurious tuples are 'extra' tuples (rows of data in a table) that appear when two relations are joined improperly. They are called so because they do not exist in the original relations but yet appear in the result of an incorrect join operation. This can cause confusion as well as inaccurate analysis or reports.
04

Preventing Spurious Tuples

To prevent spurious tuples, we must ensure that we properly join tables. This often means including all the necessary attributes in the join condition and ensuring that these attributes match appropriately. Normalization of database schemas can also prevent spurious tuples by reducing redundancy and properly structuring the database.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Understanding Null Values
Null values in databases represent unknown or missing data. Unlike zero or a blank space, nulls indicate that the value in a field is not available. This can lead to unclear comparisons, resulting in errors during queries or calculations. For instance, when a null is involved in a calculation, the entire result might become unknown. Therefore, deciding when to use nulls is crucial, and it is often advised to avoid them where possible. One way to mitigate issues with nulls is to use default values in the database. Values that represent "unknown" or "not applicable" can be better managed using specific flags or comments.
Ensuring Data Integrity
Data integrity refers to the accuracy and consistency of data within a database. Null values can threaten data integrity because they introduce uncertainty. This means comparisons become inaccurate as the system can't ascertain whether data really matches. Data integrity can be maintained through accurate data entry, using constraints like foreign keys, and regular audits of the database. Implementing referential integrity constraints ensures that relationships between tables remain consistent, preventing orphaned records. Constant vigilance and proper practices help keep the database reliable and the information it contains accurate.
Decoding Spurious Tuples
When tables in a relational database are joined improperly, spurious tuples can emerge. These are extra rows that do not exist in the original tables and appear only due to poor join conditions. This can disrupt the accuracy of database operations and produce misleading data analyses. Spurious tuples often arise from incorrect assumptions about data relationships. To prevent them, join conditions must be properly defined using key attributes that reliably connect tables. Validating these relationships and ensuring the integrity of joined data is key to avoiding spurious tuples.
Database Normalization Simplified
Database normalization is a process that organizes tables in such a way that reduces redundancy and improves data integrity. This involves dividing a database into two or more tables and defining relationships between the tables. Normalization follows various "normal forms," each with specific rules for structuring data.
  • First Normal Form (1NF): Ensures that each table column holds unique, atomic values only.
  • Second Normal Form (2NF): Builds on 1NF by ensuring that each column is fully functionally dependent on the primary key.
  • Third Normal Form (3NF): Further refines by removing transitive dependencies which aren't part of the primary key.
Conducting normalization decreases the chance of anomalies, such as spurious tuples, and ensures that the integrity of the database is maintained during updates and deletions.

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Why can we not infer a functional dependency automatically from a particular relation state?

What is meant by the closure of a set of functional dependencies? Illustrate with an example.

Suppose that we have the following requirements for a university database that is used to keep track of students' transcripts: a. The university keeps track of each student's name (SNAME), student number (SNUM), social security number (SSN), current address (SCADDR) and phone \((\mathrm{SCPHONE}),\) permanent address \((\mathrm{SPADDR})\) and phone \((\mathrm{SPPHONE}),\) birth date \((\mathrm{BDATE})\) \(\operatorname{sex}(\operatorname{sex}), \text { class (cLass) (freshman, sophomore, } \ldots, \text { graduate }),\) major depart ment (MAJORCODE), minor department (MINORCODE) (if any), and degree program \(\left(p_{R O C}\right)(B, A,, B, S, \ldots, P H, D,) .\) Both sss \(N\) and student number have unique val. ues for each student. b. Each department is described by a name (DNAME), department code (DCOOE), office number (DOFFICE), office phone (DPHONE), and college (DCOLLECE). Both name and code have unique values for each department. c. Each course has a course name (cNAME), description (cDESC), course number (CNUM), number of semester hours (cREDIT), level (LEVEL), and offering depart. ment (coept). The course number is unique for each course. d. Each section has an instructor (INAME), semester (SEMESTER), year (YEAR), course (seccourse), and section number (secwum). The section number distinguishes different sections of the same course that are taught during the same semester/ year; its values are \(1,2,3, \ldots,\) up to the total number of sections taught during each semester. e. \(A\) grade record refers to a student \((\operatorname{ss} N),\) a particular section, and a grade \((\mathrm{CRADE})\) Design a relational database schema for this database application. First show all the functional dependencies that should hold among the attributes. Then design relation schemas for the database that are each in \(3 \mathrm{NF}\) or BCNF. Specify the key attributes of each relation. Note any unspecified requirements, and make appropriate assumptions to render the specification complete.

, Odate, Cust#, Total_amount) ORDER- ITEM(O#, I#, Qty_order… # Consider the following relations for an order-processing application database at \(\mathrm{ABC},\) Inc. ORDER (O#, Odate, Cust#, Total_amount) ORDER-ITEM(O#, I#, Qty_ordered, Total_price, Discount\%) Assume that each item has a different discount. The Total_PRICE refers to one item, OOATE is the date on which the order was placed, and the Total_AMOUNT is the amount of the order. If we apply a natural join on the relations ORDER-ITEM and ORDER in this database, what does the resulting relation schema look like? What will be its key? Show the FDs in this resulting relation. Is it in \(2 \mathrm{NF}\) ? Is it in \(3 \mathrm{NF}\) ? Why or why not? (State assumptions, if you make any.)

What role do Armstrong's inference rules- -the three inference rules IR 1 through IR3-play in the development of the theory of relational design?

See all solutions

Recommended explanations on Computer Science Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free