Slides on Library Management systems or in other words, how a whole library can fit in one computer.

It sounds impossible but we all know that this is now one of the main requirements for any business or institution to run effectively – Information Systems or Information technology .

Because time it is now a very important resource, we must create more and more innovative and effective computerised systems that can help us reduce time spending for doing different tasks while we are in control of the workload, and earn money in the same time.

A library is a complex system based on multiple entities that are interrelated, all orbiting around one single element – information.

Should we have a good understanding and to be able to develop an effective LMS, we need to identify the entities and their specific data so that we can establish their relations and how they influence one another.

Slide 2

But before diving into library entities and analyse each of them, I wish to stop and have a look to Data Modelling.

Data Modelling helps us to basically give a shape to all data and information we know.

 It is, if you like, as when we were kids and we used to play with coloured modelling clay. We have had a piece of that clay with no sense, and we used to transform it into a dinosaur or a frog. J Something that our minds as kids could see a sense into.

Obviously, my example is a very primal one. But through analogy, we can see data modelling as transforming chaotic data or large amounts of data into systems that our mind can understand better and work better with.

A definition of data modelling is that data modelling is the process of sorting and storing data, organising the entities and their data in such way to show how they interrelate to each other in order for the management and IT professionals to understand and apply this model for organising better work flows and subsequently, better communication, in the library, in our case.

Slide 3

Why is data modelling technique a useful tool?

It may look complicated at one first view. But as in a library we need to arrange all the books on shelves and we need to make sure we can have easy access to each of them, is the same thing with data modelling techniques.

We will have a look at few data modelling techniques.

A data modelling technique as mentioned earlier, helps us to access very quick a large amount of data organised into categories. In a college library, there are a number of entities as:  management, staff, students, borrowers and last but not least the books.  All these entities are interrelated one to another and a poor management of data can cause massive disruption within the work flows.

We need to be able to visualise the relationships between these entities. Having a list with these entities and their related data as well as being able to visualise the relationships between them, help us design efficient, business focused databases.

Essentially, a logical Entities Relationships Diagram will enable us to effectively see the relationship between existent entities or glossary terms. (Brandenburg, 2019)

Slide 4 – We are now going to have a look at few data modelling techniques punctually.

Data Dictionary

Glossary

Entity Relationship  Diagram or ERD

 Data dictionary

The Data Dictionary model provides information about each attribute. We first need to identify the attributes and specific data about them. We can then organised them into a data dictionary.

Whenever we have created spreadsheets with records of our contacts for example, that is actually creating a data dictionary.

A data dictionary has three main element

 Attribute

Attribute type

Optional/required

Attribute is a unique identifier, expressed in the typical language of the business that the data dictionary is created for, in our example: data about the student, as name, email address, phone number, address, city, etc.

Attribute type defines what type of data is allowed in that field. Most common types include text, numeric, mixed, date or time etc. In our example, you can see that the phone number attribute is numeric, but the email is mixed which means an email address can be typed as letters, symbols, and numbers. Also, the name attribute is Alpha which means the name can be typed using alphabet, etc.

Optional/required indicates if other information is required in an attribute before saving it. In our example, the rules of inserting data before being saved are optional, but this particular mapping requires it. For example, length and rules are required information for each data. In the rules column is specified that the name should be typed in as at least 2 words – name and surname right? Also, the email address must contain the symbol for “at” , and for the phone number will not be accepted all 555 numbers. (Brandenburg, 2019)

“As you can see, a data dictionary defines critical information about each attribute in a business-focused way. It also organizes information that might otherwise be scattered across multiple different documents and specs, making it easier for your database developer to design or update a database that meets business requirements.”  (Brandenburg, 2019)

Slide 5

Another data modelling technique is Glossary.

A glossary is used to ensure that all stakeholders (business and technical) understand what is meant by the terminology, acronyms, and phrases used inside an organization (Brandenburg, 2019).

A glossary helps stakeholders whether they are business or technical to understand all terminology used in that specific business environment. They can communicate better and quicker using same terminology.

Glossary is a list of terms and their definitions. In other words, everyone who can have access at the glossary can understand better of what certain terms refers for so essentially they can better understand its requirements. Improves communication and it is a reference tool and a modelling technique for capturing terms, definitions, and variations as they come up within the business process.

Imagine a meeting of stakeholders, and everyone using their own words for specific elements or important decision takers do not understand the meaning of a specific word in that particular business context. J

A glossary helps to clarify unfamiliar or new terms and is very important for the business, before taking any step further.

Slide 6

And last but not least Entity Relationships Diagram

ERD it is a data modelling technique that describes how entities or concepts or things relate to each other. From a logical point of view ERD shows how the terms in the glossary relate to one another.

“They are especially helpful in clarifying information models for relational databases and helping business users understand database structures at a high level and without details.” (Brandenburg, 2019)

This tool helps communicating how the information is stored and what is the business perspective about relationships.

A business concept could be for example, borrowing books from a library. Through a short analysis we can identify that a student can borrow one or more books from the library. But a book cannot be borrowed by multiple students.

But the book itself it is written by an author and published by a certain publisher and it might be just a limited number of that specific book so, even though the same book can be borrowed by 2-3 students, it will still be a limited numbers of borrowers that can book that book.

Also, a student can borrow for multiple times books from library, but what happens if the student do not return those books? As we all know, they can pay a fine. But if the fine is not paid, then eventually, the student will not be allowed to borrow from that library, until he pays the fine.

So first are the entities: students, books, workers, and librarian

Second are the relationships between them: a student it is able to borrow 2 books only, but a book cannot have 2 borrowers, so we are looking at this relationship as one to many. But could be when same book can be borrowed by 2 or more students because there are more than one in the library. So now we are looking to many to many type of relationship.

Third is attributes. Attributes are the details within each entity. The student has a name and student ID, the date when he registered with the library, how many books he borrowed, etc. Books have an ISBN, they are on a certain subject and written by a certain author or a group of authors.

Understanding this relationships between these entities and having their attributes can help creating a diagram which enables understanding of the processes that take place within a library system and for a better management.

This ERD from the slide, looks very complicated. The purpose of showing you this, is to understand that this diagram as it is at the moment, can make the management process very difficult.

Slide 7

In this slide we will speak about identifying the entities and the specific data related to them.

We have had a look earlier to Glossary. If there is an existent glossary for the library we can just take out from the glossary the entities.

But what happens when we do not have a glossary?

We just pull the nouns out from the process documents or from any required document

In our presentation here, we managed to extract the entities for creating an ERD for City College Library. Also we have added their attributes.

From the case study, the entities that we extracted are:

Students is an entity that has as attributes: student id, student college id, name, mobile, student email, student username, student password, student address.

It is quite straight forwards to observe that attributes are specific data related to the entity.

Next entity in our entity relationship diagram is books. Books attributes are: book id which can be identified as ISBN, for example. ISBN stands for International Standard Book Number. It is used internationally. This is a unique identifier for books.

Book student id, book name, book author, book publication date, book type, book price, book description are all attributes of book as entity.

Librarian entity has its own attributes as well: librarian id, librarian name, librarian mobile, librarian email, librarian username, librarian password, and librarian address, but we considered as per case study is not essential in this situation.

Another entity is book issues and as attributes has issue date, return date, fine for not return

The last entity extracted it is reservations with reservation book id, reservation date, reservation student id as attributes.

Slide 8

Entity relationships diagram shows the relationships between these entities. We need to understand how they are related to each other, so that we can create this diagram.

And how we do this?

We establish through simple logic the relationships between present entities. These relationships are also named cardinalities and they are represented through specific signs that you can observe in our diagram.

For example, if we take student and books, we can establish cardinality through asking 2 questions:

-       How many students can have a book issue and how many issues can have a student?

It may sound strange at the beginning, but if we think logically, in our  library environment, a student can have only one issue, just to avoid fine accumulation in case the student do not return the books in allocated time. However, when the student do not return the books, the fine is unblocked and student must pay that fine. This is what triggers another cardinality: one issue can have zero to one fine, but one fine can have only one issue.

If we look at the entity books and the entity issues, in City College Library, one book can have only one issue. And we consider this as we established that each individual book even it is a duplicate it has one individual ID. So, if one book can have only one issue, then the same book as let’s say it is not available for borrowing, can have zero to many reservations. But how many books can a reservation have?

We decided that one reservation can have many books. It is logical right? If one of our students cannot borrow two or three books from the library as they are not available, he can place only one reservation for all the books that he wants to borrow.

Slide 9

As we can see, ERD it is a graphic illustration of how entities from an information system related to each other.

In other words it is a map showing how information flows within systems, in our case in a library management system.

From a business point of view, the library management can make better business decisions having a clear understanding of the informational flow within the library.

Having used a modelling technique for data base helps IT people to implement a software easy to understand and to use for students and library employees.

Reduces the loss of information and finances, as we have clearly identified each entity’s attributes so we can control and avoid confusion, and operations disruptions.

Protects the library most important assets – books, especially those that are difficult to replace.

Establishes the correct number of employees. For example, can reduce the number of employees required to work, as the processes are centralised within an automatic operating system for the library.

In other words, with few clicks, one employee can create issues, check books availability, find books, and prevent loss by automatically stopping students that did not return books to borrow more.

 Creates the possibility for the students to check books availability and make reservations.

Eliminates unnecessary processes within the library

Slide 10

An ERD can be seen as a stage within a development process.

Having used this data modelling technique, the library management can now have a clear understanding of their assets, data base and data requirements, how entities relate to each other and how an efficient information flow looks like.

Library has now become a fast peace environment and without a good system in place that can control operations, the management and employees – good old librarian, will not be able to meet current requirements of being efficient.

References of Library Management systems or in other words, how a whole library can fit in one computer.

Brandenburg, L. (2019). How to Create an Entity Relationship Diagram (ERD). [online] Bridging-the-gap.com. Available at: https://www.bridging-the-gap.com/erd-entity-relationship-diagram/ [Accessed 16 Feb. 2019].

Brandenburg, L. (2019). What is a Data Dictionary?. [online] Bridging-the-gap.com. Available at: https://www.bridging-the-gap.com/data-dictionary/ [Accessed 16 Feb. 2019].

dzone.com. (2019). A Comparison of Data Modeling Methods for Big Data - DZone Database. [online] Available at: https://dzone.com/articles/a-comparison-of-data-modeling-methods-for-big-data [Accessed 16 Feb. 2019].

Google.co.uk. (2019). Google Images. [online] Available at: https://www.google.co.uk/search?biw=1517&bih=641&tbm=isch&sa=1&ei=Dn5sXO-HFuyX1fAPg6OX0 [Accessed 19 Feb. 2019].

Google.co.uk. (2019). library management system - Google Search. [online] Available at: https://www.google.co.uk/search?q=library+management+system&source=lnms&tbm=isch&sa=X&ved [Accessed 16 Feb. 2019].

Images.search.yahoo.com. (2019). Yahoo is now part of Oath. [online] Available at: https://images.search.yahoo.com/search/images;_ylt=AwrExl.W9Wdc82EAalyJzbkF;_ylu=X3oDMTBsZ29xY3ZzBHNlYwNzZWFyY2gEc2xrA2J1dHRvbg--;_ylc=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?gprid=ohHHLStXQrCDYJXRubb7XA&pvid=oY_YqjEwLjIkI5jqWyVUiwHPODIuMQAAAABLJLiQ&p=data+modelling+in+library&fr=yset_widemail_chr_win&fr2=sb-top-images.search.yahoo.com&ei=UTF-8&n=60&x=wrt#id=36&iurl=https%3A%2F%2Fjacksylam.files.wordpress.com%2F2015%2F04%2Ferd-library21.png&action=click [Accessed 16 Feb. 2019].