Learning Management System - Virtual University of Pakistan
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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