Summary
UvA IDM theory summary
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This is the summary of all theory related to the course Information and Data Management, which is a 2nd year course offered at the University of Amsterdam.
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Week 2: Introduction to DatabasesDBMS = Data Base Management Software
-> this software talks directly to the data base
The Entity-Relationship (ER) model is a tool to
make sure you design the database properly. The
ER model has entities (objects like customers and
accounts) and relationships between entities (e.g.
account A101 is held by customer Johnson). The
Entity Relationship diagram (ERD) shows how
entities are related to one another.
- One to one
- One to many
- Many to one
- Many to many
There are di erent ways of mapping these relationships:
- Chen style (M:N)
M = ordinality: describes the minimum
N = cardinality: describes the maximum
- Information Engineering style
Every entity can be converted into a table in the database.
Every column of this table represents an attribute of the
entity. The primary key uniquely identi es the entity. For
example, the customer-id is the primary key in identifying
the customer. In a relational database, every table should
have the primary in common. A primary key, therefore,
cannot change. The foreign key is the column in the table that is not the attribute of the entity but
the key that represents the relationship between di erent entities. For example, the account-
number is the foreign key for the customer. This table can be called a relational model. The
relational model is a conversion of the ER model.
Only for a ‘many to many’ relationship, you create a separate table called a junction table. In this
table, you represent many to many relationships. Therefore, you can also have tables only
representing relationships.
Another example of a database is the Sample Relational Database, in which each entity has a
separate table. For instance, there is a customer table, an account table and a depositor table (the
relationship). In this way, there is less repetition in the tables. The table for the relationship (in this
case the depositor table) is the junction table. This kind of table is only possible when there is a
‘many to many’ relationship.
Database normalisation means structuring the database, usually a relational database. It means
reducing duplication of data between tables and it gives the table as much exibility as possible.
SQL is a language that helps you create DBMS (the software talking to the database). To retrieve
data and to create new tables in the database, you need SQL. There are many di erent
components of SQL. One of which is Data De nition Language (DDL) which helps you de ne
the data in your database. It is used to create tables. The Ddl compiler generates a set of tables
stored in a data dictionary. The data dictionary contains metadata (i.e. data about data): it says
what data is in your database. Another part of SQL is Data Manipulation Language (DML) which
is a language for accessing and manipulating the data organized by the appropriate data model.
DML is also known as a query language (a language that requests and receives data from the
database by sending queries). Queries search the database, fetch info and display it. There are
two classes of languages:
- Procedural: user speci es what data is required and how to get those data
- Nonprocedural: user speci es what data is required without specifying how to get those data
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, An entity is a business object that represents a group or category of data. A single, speci c
occurrence of an entity is an instance. Other terms for an instance are record and tuple. An
attribute is a sub-group of information within an entity. A relationship is a link that relates two
entities that share one or more attributes. A table is a primary unit of physical storage for data in a
database. Usually a database contains more than one table. In a table, the column is called a eld
and the row is called a record.
Big data refers to such massively large data sets that conventional database tools do not have
the processing power to analyse them. Metadata can be understood as “data about data”. When
a database is being designed, a data dictionary is created to hold the metadata.
Loosely speaking, a data warehouse refers to a database that is maintained separately from an
organisation’s operational database. It is used for storage and analysis. It uses non-operational
data, all data has a time-variant and all data is standardised.
- OLTP (on-line transaction processing): major task of traditional relational DBMS and handles
day-to-day operations.
- OLAP (on-line analytical processing): major task of data warehouse system and is used for data
analysis and decision making.
A data warehouse can be designed bottom-up (small data warehouses, called data marts, are
combined into a larger data warehouse) or top-down (starts with an enterprise-wide data
warehouse and then smaller data marts are created).
A data warehouse is based on a multi-dimensional data model which views data in the form of a
data cube. A data cube, such as sales, allows data to be modelled and viewed in multiple
dimensions. A multi-dimensional data model can exist in two forms:
- Star schema: a large fact table in the middle connected to a set of smaller dimension tables.
- Snow ake schema: a re nement of the star schema where some dimensions are further
splitting into a set of smaller dimension tables.
Week 3: Blockchain
Wealth is generated by a ow of goods and services, either public or private. Transferring assets
means building value. Anything that is capable of being owned or controlled to produce value, is
an asset. There are tangible and intangible assets. Intangible assets can be subdivided in nancial
(e.g. bond), intellectual (e.g. patents) and digital assets (e.g. music). Cash is also an asset. A
transaction is an asset transfer. A contract are the conditions for transaction to occur.
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