Virtual University



Addendum CS403

|Lecture No. |1 |

|Suggestion type |New Database Management Tools |

|Artifact |Video lecture |

|Placement on Time Line / Page |50:00 |

|no. | |

|MySQL Tools |

|Oracle Tools |

|PostgreSQL Tools |

|DB2 Tools |

|MaxDB Tools |

|Lecture No. |2 |

|Suggestion type |New Example Added |

|Artifact |Video lecture |

|Placement on Time Line / Page |1:50 - 2:40 |

|no. | |

|Data is a collection of facts, figures and statistics related to an object. Data can be processed to create useful information. Data is a |

|valuable asset for an organization. |

|Example |

|Students fill an admission form when they get admission in college. The form consists of raw facts about the students. These raw facts are |

|student's name, father name, address etc. The purpose of collecting this data is to maintain the records of the students during their study|

|period in the college. |

|Lecture No. |2 |

|Suggestion type |Adding Database Application |

|Artifact |Video lecture |

|Placement on Time Line / Page |5:25 - 6:25 |

|no. | |

|Database systems are widely used in different areas because of their numerous advantages. Some of the most common database applications are|

|listed here. |

|Airlines and railways: Airlines and railways use online databases for reservation, and for displaying the schedule information. |

|Banking: Banks use databases for customer inquiry, accounts, loans, and other transactions. |

|Education: Schools and colleges use databases for course registration, result, and other information. |

|Telecommunications: Telecommunication departments use databases to store information about the communication network, telephone numbers, |

|record of calls, for generating monthly bills, etc. |

|Credit card transactions: Databases are used for keeping track of purchases on credit cards in order to generate monthly statements. |

|E-commerce: Integration of heterogeneous information sources (for example, catalogs) for business activity such as online shopping, booking|

|of holiday package, consulting a doctor, etc. |

|Health care information systems and electronic patient record: Databases are used for maintaining the patient health care details. |

|Digital libraries and digital publishing: Databases are used for management and delivery of large bodies of textual and multimedia data. |

|Finance: Databases are used for storing information such as sales, purchases of stocks and bonds or data useful for online trading. |

|Sales: Databases are used to store product, customer and transaction details. |

|Human resources: Organizations use databases for storing information about their employees, salaries, benefits, taxes, and for generating |

|salary checks. |

|Lecture No. |2 |

|Suggestion type |New concept |

|Artifact |Video lecture |

|Placement on Time Line / Page |32:20 |

|no. | |

|System Analyst: System Analyst determines the requirement of end users, especially naïve and parametric end users and develops |

|specifications for transactions that meet these requirements. System analyst plays a major role in database design, its properties; the |

|structure prepares the system requirements statement, which involves the feasibility aspect, economic aspect, technical aspect etc. of the |

|system. |

|Lecture No. |4 |

|Suggestion type |Adding more detail |

|Artifact |Video lecture |

|Placement on Time Line / Page |33:13:00 |

|no. | |

| Data Dictionary Management: The DBMS stores Metadata (i.e. definitions of the data elements) in a data dictionary. All the programs that |

|access the data in the database work through the DBMS. The DBMS uses the data dictionary to look up the required data component structures |

|and relationships. Additionally, any changes made in a database structure are automatically recorded in the data dictionary. Thus, the DBMS|

|provides data abstraction, and it removes structural and data dependency from the system.  |

| Data storage management: The DBMS creates and manages the complex structures required for data storage. A modern DBMS provides storage not|

|only for the data, but also for related data-entry forms or reports, data validation rules, code, structures to handle video and picture |

|formats, and so on. Data storage management is also important for database performance tuning. The DBMS actually stores the database in |

|multiple physical data files.   |

|Lecture No. |4 |

|Suggestion type |Example of catalog |

|Artifact |Video lecture |

|Placement on Time Line / Page |34:48 - 36: 51 |

|no. | |

|CAT is a table (view) from the data dictionary. |

|For example we have tables of STUDENT, TEACHER, REGISTRAR etc, from this scenario the catalog will be |

| |

|Catalog |

| |

|TABLE_NAME |

|TALBLE_TYPE |

| |

|STUDENT |

|TABLE |

| |

|TACHER |

|TABLE |

| |

|Lecture No. |4 |

|Suggestion type |Adding more detail for selection of a particular DBMS |

|Artifact |Video lecture |

|Placement on Time Line / Page |41:12:00 |

|no. | |

| Based on Number of Sites (Locations): |

|Depending on the number of sites over which the database is distributed, it is divided into two types, namely, centralized and distributed |

|database systems. Centralized database systems run on a single computer system. Both the database and DBMS software reside at a single |

|computer site. The user interacts with the centralized system through a dummy terminal connected to it for information retrieval. |

|In distributed database systems, the database and DBMS are distributed over several computers located at different sites. The computers |

|communicate with each other through various communication media such as high-speed networks or telephone lines. Distributed databases can |

|be classified as homogeneous and heterogeneous. In homogeneous distributed database system, all sites have identical database management |

|system software, whereas in heterogeneous distributed database system, different sites use different database management system software. |

|Lecture No. |4 |

|Suggestion type |Adding detail of 2-tier and 3-tier architecture |

|Artifact |Video lecture |

|Placement on Time Line / Page |Interval: 50:30 |

|no. | |

|  Two-tier architecture: |

| Two-tier architecture is a software architecture in which a presentation layer or interface runs on a client, and a data layer or data |

|structure gets stored on a server. Separating these two components into different locations represents two-tier architecture, as opposed to|

|single-tier architecture. |

|Three-tier architecture: |

|3-tier architecture is a client server architecture where the user interfaces, computer data storage and data access are created and |

|maintained as independent modules. It is mean to allow any of the three tiers to be replaced or developed independently. For example, a |

|change in the operating system of a presentation tier will only affect the user interface code. |

|Lecture No. |5 |

|Suggestion type |Names of Tools for database design |

|Artifact |Video lecture |

|Placement on Time Line / Page |20:00:00 |

|no. | |

|Some tools for database design are given below: |

|ERStudio |

|Microsoft Visio |

|Rational Rose |

|PowerDesigner |

|TOAD Data Modeler |

|Lecture No. |5 |

|Suggestion type |Some important web links for the current Lecture. |

|Artifact |Video lecture |

|Placement on Time Line / Page |At the end of the lecture |

|no. | |

|Important Links: |

|  |

|1. |

|2. |

|3. |

|Lecture No. |6 |

|Suggestion type |Adding definition of Data Dictionary |

|Artifact |Video lecture |

|Placement on Time Line / Page |14:40:00 |

|no. | |

|Data Dictionary: |

| |

|A data dictionary is a file or a set of files that contains a database's metadata. The data dictionary contains records about other objects|

|in the database, such as data ownership, data relationships to other objects, and other data. |

| |

|The data dictionary is a crucial component of any relational database. Ironically, because of its importance, it is invisible to most |

|database users. Typically, only database administrators interact with the data dictionary. |

|Lecture No. |6 |

|Suggestion type |Freestanding data dictionary description |

|Artifact |Video lecture |

|Placement on Time Line / Page |15:55 :00– 16:50:00 |

|no. | |

|Freestanding Data Dictionary: |

|A data dictionary that is a separate part of DBMS (non-integrated data dictionary) is referred to as freestanding data dictionary. It may |

|be a commercial product or a simple file developed and maintained by a database designer. The freestanding data dictionary is useful in the|

|initial stage of design for collecting and organizing information about data. |

|Lecture No. |6 |

|Suggestion type |Important web link for the current Lecture. |

|Artifact |Video lecture |

|Placement on Time Line / Page |38:00:00 |

|no. | |

| |

|Lecture No. |7 |

|Suggestion type |Entity definition |

|Artifact |Video lecture |

|Placement on Time Line / Page |3:16 – 5:55:00 |

|no. | |

|An entity is a thing or object of importance about which data must be captured. All things aren't entities, only those about which |

|information should be captured. |

|Information about an entity is captured in the form of attributes and/or relationships. If something is a candidate for being an entity and|

|it has no attributes or relationships, it isn't an entity. |

|Database entities appear in a data model as a box with a title. The title is the name of the entity. |

|Lecture No. |7 |

|Suggestion type |Entity description |

|Artifact |Video lecture |

|Placement on Time Line / Page |16:00:00 |

|no. | |

|External entity: Anything that receives or generates data from or to the system is an external entity, this term is normally used in data |

|flow diagram. |

|Entity Type: Where as entity type is name assigned to a collection of properties of different things existing in an environment, this term |

|is used in the context of ERD. |

|Lecture No. |7 |

|Suggestion type |New concept added |

|Artifact |Video lecture |

|Placement on Time Line / Page |17:00:00 |

|no. | |

|Entity with single instance is not represented in ER Diagram; this concept is missing in video lectures. So below contents should be added |

|at interval 17:00 |

|“Things with a single instance are not explicitly identified as entity type, so they are not represented in the E-R diagram. For example, a|

|librarian is a single instance in a library system, that plays certain role in the library system and at many places data is generated from|

|or to the librarian, so it will be represented at relevant places in the DFDs. But the librarian will not be explicitly represented in the |

|E-R diagram of the library system” |

|Lecture No. |8 |

|Suggestion type |Significance of keys |

|Artifact |Video lecture |

|Placement on Time Line / Page |07:00:00 |

|no. | |

|Significance of Keys: |

|Keys are crucial to a table structure for the following reasons: |

|Keys ensure that each record in a table is precisely identified. As you already know, a table represents a singular collection of similar |

|objects or events. (For example, a CLASSES table represents a collection of classes, not just a single class.) The complete set of records |

|within the table constitutes the collection, and each record represents a unique instance of the table's subject within that collection. |

|You must have some means of accurately identifying each instance, and a key is the device that allows you to do so. |

|Keys help establish and enforce various types of integrity. Keys are a major component of table-level integrity and relationship-level |

|integrity. For instance, they enable you to ensure that a table has unique records and that the fields you use to establish a relationship |

|between a pair of tables always contain matching values. |

|Lecture No. |8 |

|Suggestion type |Identification of key attribute |

|Artifact |Video lecture |

|Placement on Time Line / Page |12:30:00 |

|no. | |

|While defining entity type, key attribute should be identified. |

|Lecture No. |8 |

|Suggestion type |Concept added |

|Artifact |Video lecture |

|Placement on Time Line / Page |20:30 – 27:30:00 |

|no. | |

|A Super key and composite key are similar to one another. Both of them are used to uniquely identify a row in a database table.  |

|A super key is a set of columns within a table that can be used to identify a particular row in a table. A super key can be only one column|

|or a combination of multiple columns. If a super key contains multiple columns it becomes a composite key. |

|Lecture No. |8 |

|Suggestion type |Concept explanation and example added |

|Artifact |Video lecture |

|Placement on Time Line / Page |31:32 – 33:00:00 |

|no. | |

|Candidate Key |

|A candidate is a subset of a super key. A candidate key is a single field or the least combination of fields that uniquely identifies each |

|record in the table. The least combination of fields distinguishes a candidate key from a super key. Every table must have at least one |

|candidate key but at the same time can have several. |

|As an example we might have a student_id that uniquely identifies the students in a student table. This would be a candidate key. But in |

|the same table we might have the student’s first name and last name that also, when combined, uniquely identify the student in a student |

|table. These would both be candidate keys. |

|Lecture No. |9 |

|Suggestion type |Concept added |

|Artifact |Video lecture |

|Placement on Time Line / Page |Interval: 13:30:00 |

|no. | |

| |

|Entities enrolled in a relationship are called its participants. The participation of an entity in a relationship is total when all |

|entities of that set might be participant in the relationship otherwise it is partial e.g. if every Part is supplied by a Supplier then the|

|SUPP_PART relationship is total. If certain parts are available without a supplier than it is partial. |

|Lecture No. |10 |

|Suggestion type |New slide added |

|Artifact |Video lecture |

|Placement on Time Line / Page |35:00:00 |

|no. | |

|Identifier Dependency: |

|It means that the dependent entity in case of existence dependency does not have its own identifier and any external identifier is used to |

|pick data for that entity. And to define a key in this entity the key of the parent entity is to be used in the key for this entity may be |

|used as composite keys. |

|Lecture No. |11 |

|Suggestion type |Missing Diagram |

|Artifact |Video lecture |

|Placement on Time Line / Page |8:30 – 11:35 :00 |

|no. | |

|[pic] |

| |

|Subtypes hold all the properties of their corresponding super-types. Means all those subtypes which are connected to a specific supertype |

|will have all the properties of their supertype. |

|The Figure shows that the supertype and subtype relation between the SALARIED and HOURLY employees with the supertype entity EMPLOYEE, we |

|can see that the attributes which are specific to the subtype entities are not shown with the supertype entity. Only those attributes are |

|shown on the supertype entity which are to be inherited to the subtypes and are common to all the subtype entities associated with this |

|supertype. The example shows that there is a major entity or entity supertype name EMPLOYEE and has a number of attributes. Now that in a |

|certain organization there can be a number of employees being paid on different payment criteria. |

|Lecture No. |12 |

|Suggestion type |Modification/ Missing module name |

|Artifact |Video lecture |

|Placement on Time Line / Page |23:00 |

|no. | |

|The heading “Subject registration” should add in the slides at interval 23:00. The lecturer explaining the example of three main modules of|

|DFD with the help of the diagram but the slide contains no heading on it. It could be better to place names along with a diagram for the |

|understanding of students that what module; the lecturer is explaining. |

|Lecture No. |12 |

|Suggestion type |Modification/ Missing module name |

|Artifact |Video lecture |

|Placement on Time Line / Page |27:05 |

|no. | |

|The headings “Result submission” should add in the slides at the interval 27:05. The lecturer explaining the example of three main modules |

|of DFD with the help of the diagram but the slide contains no heading on it. |

|Lecture No. |12 |

|Suggestion type |Modification/ Missing module name |

|Artifact |Video lecture |

|Placement on Time Line / Page |28:25 |

|no. | |

|The headings “Result calculation” should add in the slides at the interval 28:25. The lecturer explaining the example of three main modules|

|of DFD with the help of the diagram but the slide contains no heading on it. |

|Lecture No. |14 |

|Suggestion type |Missing Table/ Difference |

|Artifact |Video lecture |

|Placement on Time Line / Page |2:15 – 2:58 |

|no. | |

|“Difference between conceptual database design and logical database design” table is missing in video lecture which is present in handouts.|

| |

| |

|Conceptual Database Design |

|Logical Database Design |

| |

|1 |

|Developed in a semantic data model (generally E-R data model) |

|In legacy data models (relational generally in current age) |

| |

|2 |

|Free of data model in which going to be implemented; many/any possible |

|Free of particular DBMS in which going to be implemented; many/any possible |

| |

|3 |

|Results from Analysis Phase |

|Obtained by translating the conceptual database design into another data model |

| |

|4 |

|Represented graphically |

|Descriptive |

| |

|5 |

|More expressive |

|Relatively less expressive |

| |

|6 |

|Going to be transformed and then implemented |

|Going to be implemented |

| |

| |

|Lecture No. |14 |

|Suggestion type |Missing Definition –data model |

|Artifact |Video lecture |

|Placement on Time Line / Page |2:50 – 4:15:00 |

|no. | |

|Data model definition is missing. Definition and some general discussion on data model for concept clarity are necessary. |

|Data model is a set or collection of construct used for creating a database and producing designs for the databases. |

|The conceptual database design can be transformed into any data model, like, hierarchical, network, relational or object-oriented. |

|Lecture No. |14 |

|Suggestion type |Missing concept |

|Artifact |Video lecture |

|Placement on Time Line / Page |Interval: 03:00:00 |

|no. | |

|The advantages of relational data model are given in handouts at page no 115, but it is not discussed in video lecture. The advantages of |

|relational data model should also be given in video lecture at interval 03:00. |

|relational data model has a strong mathematical foundation that gives many advantages, like: |

|Anything included/defined in RDM has got a precise meaning since it is based on mathematics, so there is no confusion. |

|If we want to test something regarding RDM we can test it mathematically, if it works mathematically it will work with RDM (apart from some|

|exceptions). |

|The mathematics not only provided the RDM the structure (relation) but also well defined manipulation languages (relational algebra and |

|relational calculus). |

|It provided RDM certain boundaries, so any modification or addition we want to make in RDM, we have to see if it complies with the |

|relational mathematics or not. We cannot afford to cross these boundaries since we will be losing the huge advantages provided by the |

|mathematical backup. |

|Lecture No. |15 |

|Suggestion type |Insufficient explanation / detail |

|Artifact |Video lecture |

|Placement on Time Line / Page |32:30 - 33:40 |

|no. | |

|The respected instructor did not explain the concept of Null constraints, default value and Domain constraint sufficiently. There should be|

|proper explanation of such a important concepts. |

|A Null value of an attribute means that the value of attribute is not yet given, not defined yet. It can be assigned or defined later |

|however. Through Null constraint we can monitor whether an attribute can have Null value or not. |

|Default value constraint means that if we do not give any value to any particular attribute, it will be given a certain (default) value. |

|This constraint is generally used for the efficiency purpose in the data entry process. |

|This is an essential constraint that is applied on every attribute, that is, every attribute has got a domain. Domain means the possible |

|set of values that an attribute can have. For example, some attributes may have numeric values, like salary, age, marks etc. Some |

|attributes may possess text or character values, like, name and address. |

|Lecture No. |15 |

|Suggestion type |New slide |

|Artifact |Video lecture |

|Placement on Time Line / Page |45:00:00 |

|no. | |

|The Respected instructor explained the mapping of a composite attribute into relational data model, but did not include the table/relation |

|as an example in slide, so I suggest that there should be a separate slide that will contain the resultant relation of transforming |

|composite attribute into a relation. |

|Transformation of composite attribute: |

|STUDENT (stId, stName, stDoB) |

|STDADRES (stId, hNo, strNo, country, cityCode, city, areaCode) |

| |

|Transformation of multi-valued attribute: |

|STUDENT (stId, stName, stDoB) |

|STDADRES (stId, hNo, strNo, country, cityCode, city, areaCode) |

|STHOBBY(stId, stHobby) |

| |

|Lecture No. |16 |

|Suggestion type |Example/ Insufficient explanation |

|Artifact |Video lecture |

|Placement on Time Line / Page |(36:14 – 38:00) |

|no. | |

|Insufficient detail and example. Subtype discriminator detail and example is insufficient. Explanation should be provided with relation/ |

|examples for elaborating these topics. |

|Explanation: |

|If we link the super type with concerned subtype there is a requirement of descriptive attribute, which is called as discriminator. It is |

|used to identify which subtype is to be linked. For Example there is an entity type EMP which is a super type, now there are three |

|subtypes, which are salaried, hourly and consultants. So now there is a requirement of a determinant, which can identify that which |

|subtypes to be consulted, so with empId a special character can be added which can be used to identify the concerned subtype. |

|Lecture No. |17 |

|Suggestion type |Example need to be shown on the slides |

|Artifact |Video lecture |

|Placement on Time Line / Page |07:10 |

|no. | |

|Example: |

|SELECT emp_name FROM employee |

|WHERE domicile = ‘Multan’; |

|Lecture No. |17 |

|Suggestion type |Output of example missing |

|Artifact |Video lecture |

|Placement on Time Line / Page |15:53 |

|no. | |

|Output of example is shown on page 150 but not shown in the video lecture. It should be on 15:53 |

|Output: |

|σ Curr_Sem > 3 (STUDENT) |

|stId |

|stName |

|stAdr |

|prName |

|curSem |

| |

|S1020 |

| |

|Sohail Dar |

|H#14, F/8-4,Islamabad. |

|MCS |

|4 |

| |

|S1015 |

|Tahira Ejaz |

|H#99, Lala Rukh Wah. |

|MCS |

|5 |

| |

|S1018 |

|Arif Zia |

|H#10, E-8, Islamabad. |

|BIT |

|5 |

| |

|Fig. 2: Output relation of a select operation |

| |

| |

| |

|Lecture No. |18 |

|Suggestion type |Explanation/ example |

|Artifact |Video lecture |

|Placement on Time Line / Page no. |17:02 – 19:25 |

|Equi join and natural join explanation is insufficient. There should be a clear understanding between these joins with solid examples. |

| |

|Example: |

| |

|Equi join |

| |

|In equi join, rows are joined on the basis of values of a common attribute between the two relations i.e. rows having the same value in the |

|common attribute are joined. Common attributes appears twice in the output and common attribute with the same name is qualified with the |

|relation name in the output. |

|For example: if we take the above example then result of equi join is: |

| |

|EMPLOYEE employee.emp_id = project.emp_id PROJECT |

| |

|Emp_id |

|Emp_name |

|Designation |

|age |

|Project-id |

|Project_name |

|Emp_id |

|Project_status |

| |

|1 |

|abc |

|Project-coordinator |

|35 |

|10 |

|Alpha |

|1 |

|open |

| |

|2 |

|def |

|worker |

|25 |

|30 |

|gamma |

|2 |

|open |

| |

| |

|Natural join |

| |

|I t is also called simply the join and called most general form of join. It is same as equijoin with common column appearing once. |

| |

|For example: if we take the above example then result of Natural join is: |

| |

|[pic] |

| |

| |

|Emp_id |

|Emp_name |

|Designation |

|age |

|Project-id |

|Project_name |

|Project_status |

| |

|1 |

|abc |

|Project-coordinator |

|35 |

|10 |

|Alpha |

|open |

| |

|2 |

|def |

|worker |

|25 |

|30 |

|gamma |

|open |

| |

|Lecture No. |24 |

|Suggestion type |Example should be given in the background while explaining verbally |

|Artifact |Video lecture |

|Placement on Time Line / Page |14:00 – 21:35 |

|no. | |

|Generally adopted where updation is not very frequent |

|In PROJ-EMP we replicate the data with both the PROJ and EMP tables so on the cost of extra storage both tables have the related data |

|Lecture No. |24 |

|Suggestion type |Slides should be visible in the background while explaining verbally |

|Artifact |Video lecture |

|Placement on Time Line / Page |21:40 - 27:20 |

|no. | |

|Clustering is a process, which means to place records from different tables to place in adjacent physical locations, called clusters. It |

|increases the efficiency since related records are placed close to each other. Clustering is also suitable to relatively static situations.|

|The advantage of clustering is that while accessing the records it is easy to access. Define cluster, define the key of the cluster, and |

|include the tables into the cluster while creating associating the key with it. |

|Lecture No. |25 |

|Suggestion type |Insufficient Detail/ example |

|Artifact |Video lecture |

|Placement on Time Line / Page |28:00 – 29:18 |

|no. | |

|Example of money and floating point can be seen on the given website: |

| |

| |

|Lecture No. |26 |

|Suggestion type |Complex Command format |

|Artifact |Video lecture |

|Placement on Time Line / Page |15:00 – 19:47 |

|no. | |

|You can find the easy method of create table command on the following site: |

| |

|Lecture No. |29 |

|Suggestion type |Same example of between operator in the video lecture should also be discussed in the handouts |

|Artifact |Video lecture |

|Placement on Time Line / Page |35:25 |

|no. | |

|Example: List the IDs of the students with course codes having marks between 70 and 80: |

|Select StId, crCODE, totMrks from ENROLL where totMrks between 70 and 80. |

|This query will show the records of the students whose total marks are between 70 and 80. |

|Lecture No. |29 |

|Suggestion type |Example should be given on the slide |

|Artifact |Video lecture |

|Placement on Time Line / Page |46:30 |

|no. | |

|The patterns that you can choose from are: |

|% Allows you to match any string of any length (including zero length) |

|_ Allows you to match on a single character |

|Q: Display the names and credits of CS programs |

|SELECT crName, crCrdts, prName FROM course |

|WHERE prName like '%CS' |

|If we have the following courses in the table: cs403,cs402,cs401,mgt101 |

|In this case if we want to select the courses which are of cs: |

|SELECT crName, crCrdts, prName FROM course |

|WHERE prName like '_s%' |

|This will select all the courses which have second character s and at the start only one character and follows by any number of characters.|

|Lecture No. |30 |

|Suggestion type |Example of order by clause |

|Artifact |Video lecture |

|Placement on Time Line / Page |03:00 |

|no. | |

|The example of order by clause given in the video lectures is given below: |

|Select * from STUDENT order by stName |

|It will display the records of all the students in the ascending order. |

|Lecture No. |30 |

|Suggestion type |Example of group by clause |

|Artifact |Video lecture |

|Placement on Time Line / Page |36:50 |

|no. | |

|The example of group by clause given in the video lectures is given below: |

|Select prName, max(cgpa) as ‘Max CGPA’, min(cgpa) as ‘Min CGPA’ FROM student |

|GROUP BY prName |

|Lecture No. |30 |

|Suggestion type |Example of Having clause |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Select prName, max(cgpa) as ‘Max CGPA’, min(cgpa) as ‘Min CGPA’ FROM student |

|GROUP BY prName having max(cgpa)>3 |

|Lecture No. |21 |

|Suggestion type |Tool/Example |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Database tool explanation is missing. Needs about tool while transforming from logical design to physical design. Latest tools are uploaded|

|on LMS download section. Detailed document about installation is uploaded on LMS. |

|Lecture No. |22 |

|Suggestion type |Missing data types |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Data types and ranges for Microsoft Access, MySQL and SQL Server. |

| |

|Lecture No. |24 |

|Suggestion type |New version of SQL should be introduced |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Database tool explanation is missing. Needs about tool while transforming from logical design to physical design. Latest tools are uploaded|

|on LMS download section. |

|Lecture No. |25 |

|Suggestion type |MS SQL Server tutorial |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Detailed document about SQL server installation is uploaded on LMS. |

|Lecture No. |26 |

|Suggestion type |To introduce various SQL commands (query) |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Various commands including create are discussed in detail on the given website. |

| |

|Lecture No. |27 |

|Suggestion type |Examples of TRUNCATE DELETE and DROP commands should be included |

|Artifact |Video lecture |

|Placement on Time Line / Page |42:05 |

|no. | |

|Commands are discussed in detail on the given website. |

| |

| |

|Lecture No. |34 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |16:00 |

|no. | |

|At the specified interval detail about SRAM and DRAM is missing. It provided below: |

|SRAM: |

|Static Random Access Memory is a type of semiconductor memory that uses latches to store each bit. It is different from the DRAM because it|

|does not need to be updated periodically. It is also more expensive and hence not used main memory in personal computers. |

|DRAM: |

|Dynamic random access memory uses IC’s made up of capacitors to store bits. Due to properties of capacitor it requires frequent refreshes |

|to maintain the data. Mostly DRAM is used as main memory in personal computers. |

|Sources: |

| |

| |

|Lecture No. |34 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |18:00 |

|no. | |

|Electrically Erasable Programmable Read-Only Memory is a type of non-volatile memory used in computers and other electronic devices to |

|store small amounts of data that must be saved when power is removed. |

|Sources: |

|Lecture No. |34 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |30:40 |

|no. | |

|Students can consult handouts for the lecture on page 257 for more details about optical storage devices. |

|Lecture No. |35 |

|Suggestion type |Missing Diagram |

|Artifact |Video Lecture |

|Placement on Time Line / Page |17:50 |

|no. | |

|Diagram and explanation in the video lecture is missing about Index sequential file. Students can see the image below to have a more clear |

|idea about the concept. |

| |

|[pic] |

|Sources: |

| |

|Lecture No. |35 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |52:30 |

|no. | |

|Overflow file is used to store the indexes of new records. New records cannot be directly added to the current indexed database because it |

|will create overhead to add new records one by one. New records are processed in batch which is stored in the overflow file. You can visit |

|the link below to see the example: |

| |

|Lecture No. |36 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |23:38-40:00 |

|no. | |

|Students can consult for Mapping function topic from handouts page 266. |

|Lecture No. |36 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |23:38-40:00 |

|no. | |

|Students can consult for topics Chaining, Re-hashing, Linear probing, Clustering, Open addressing from handouts page 267-269 |

|Lecture No. |36 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |03:40 |

|no. | |

|The data is distributed across various disks/sector or tracks; each data row is physically stored in data blocks in the disk. The Cylinder |

|index is used to identify the data block to which the row belongs (to be read/written). Cylinder index increases the efficiency to access |

|the particular data. |

|Lecture No. |36 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |47:00 |

|no. | |

|At the specified interval these definitions are missing. |

|Index: An index contains a collection of data entries, and supports efficient retrieval of all records with a given search key value k. |

|Views: A view is defined to combine certain data from one or more tables for different reasons. A view is like a window through which we |

|can see data from one or more tables. |

|Lecture No. |37 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |5:40 – 9:15 |

|no. | |

|Students can consult handouts for index diagram and explanation on page 271 |

|Lecture No. |37 |

|Suggestion type |Example |

|Artifact |Handouts |

|Placement on Time Line / Page |Page 273 |

|no. | |

|B+ Tree explanation is missing in the handouts: |

|Index is maintained in a hierarchical arrangement of nodes. Tree node has key value which we want to search and pointer that point to those|

|nodes; nodes are connected through links. There is a special node called the root node and each node has parent node and has one or more |

|children except the leaf node. B+ Trees has number of node one more than the number of keys. Number of pointers in a node is called the |

|order of the tree and the distance of all leaf nodes is same from the root. Left most node will point to the nodes which have less keys |

|than the current node and right node will point to the nodes which have more keys than the. For example, index entries: they direct search for data entries in leaves. More detail is also given in the video of Lecture No. 38 from 35:00 to 43:10 with |

|example explanation. |

|Lecture No. |38 |

|Suggestion type |Missing concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |Full lecture from page 275 to 279 |

|no. | |

|Lecture 37 & 38 are based on the single topic i.e. Indexing. In handouts of lecture 38 there are some concepts like Clustered Versus |

|Un-clustered Indexes, Dense Verses Sparse Indexes, Primary and Secondary Indexes and Indexes Using Composite Search Keys which are not |

|properly discussed in the video of Lecture No.38, therefore students can read from handouts to grasp those topics. |

|Lecture No. |39 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |51:30 |

|no. | |

|Definition of simple, complex, materialized views are missing in video lecture it is included in handouts at page no 283-284, you can |

|consult from there. |

|Lecture No. |40 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |07:50 |

|no. | |

|The examples of views using function is discussed in handouts on page 285. |

|Lecture No. |42 |

|Suggestion type |Addition of consistency example |

|Artifact |Video Lecture |

|Placement on Time Line / Page |24:20 |

|no. | |

|At the specified interval example is missing. You can consider the example below to clarify the idea. |

|For example, a transfer of Rs 1000 from your checking account to your savings account would consist of two steps: debiting your checking |

|account by Rs1000 and crediting your savings account with Rs1000. To protect data integrity and consistency and the interests of the bank |

|and the customer these two operations must be applied together or not at all. Thus, they constitute a transaction and ensure the |

|consistency. |

|Lecture No. |43 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |14:00 |

|no. | |

|At the specified interval example of sample log file of recovery manager in the topic Incremental Log with Deferred Updates is missing. |

|Students can refer to page 303 of handouts to see the example for details. |

|Lecture No. |43 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |30:15 |

|no. | |

|At the specified interval example of sample log file of recovery manager in the topic Incremental Log with Immediate Updates is missing. |

|Students can refer to page 306 of handouts to see the example for details. |

|Lecture No. |44 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |31:00 |

|no. | |

|At the specified interval of time a slide is missing in video lecture whose contents will be as follows: |

|There are different situations during concurrent access of the data: |

|Different transactions accessing (reading or writing) different objects |

|Different transactions reading same object |

|Different transactions accessing same object and one or all writing it |

|The last point results in conflict |

|Lecture No. |45 |

|Suggestion type |Missing Concept |

|Artifact |Video Lecture |

|Placement on Time Line / Page |16:30 |

|no. | |

|The new slide which contains the explanation of wait for graph diagram is missing at the specified interval. The contents of slide should |

|be: |

|In figure transaction A is waiting for transaction B and B is waiting for N. So it will move inversely for releasing of lock and |

|transaction A will be the last one to execute. In the second figure there is a cycle, which represents deadlock; this kind of cycle can be |

|in between two or more transactions as well. The DBMS keeps on checking for the cycle. |

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