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Chapter: Data Warehousing and Data Mining

Data Mining - On What Kind of Data? ( Types of Data )

Relational Databases: A database system, also called a database management system (DBMS), consists of a collection of interrelated data, known as a database, and a set of software programs to manage and access the data.

Data Mining—On What Kind of Data? ( Types of Data )


Relational Databases: A database system, also called a database management system (DBMS), consists of a collection of interrelated data, known as a database, and a set of software programs to manage and access the data.


A relational database: is a collection of tables, each of which is assigned a unique name Each table consists of a set of attributes (columns or fields) and usually stores a large set of tuples (records or rows). Each tuple in a relational table represents an object identified by a unique key and described by a set of attribute values. A semantic data model, such as an entity-relationship (ER) data model, is often constructed for relational databases. An ER data model represents the database as a set of entities and their relationships.


Data Warehouses: A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and that usually resides at a single site. Data warehouses are constructed via a process of data cleaning, data integration, data transformation, data loading, and periodic data refreshing.


The data are stored to provide information from a historical perspective (such as from the past 5–10 years) and are typically summarized.


A data warehouse is usually modeled by a multidimensional database structure, where each dimension corresponds to an attribute or a set of attributes in the schema, and each cell stores the value of some aggregate measure, such as count or sales amount


The actual physical structure of a data warehouse may be a relational data store or a multidimensional data cube. A data cube provides a multidimensional view of data and allows the precomputation and fast accessing of summarized data



What is the difference between a data warehouse and a data mart?” you may ask.


A data warehouse collects information about subjects that span an entire organization, and thus its scope is enterprise-wide.


A data mart, on the other hand, is a department subset of a data warehouse. It focuses on selected subjects, and thus its scope is department-wide. Data warehouse systems are well suited for on-line analytical processing, or OLAP. OLAP operations use background knowledge regarding the domain of the data being studied in order to allow the presentation of data at different levels of abstraction. Such operations accommodate different user viewpoints.


Examples of OLAP operations include drill-down and roll-up, which allow the user to view the data at differing degrees of summarization,


Transactional Databases: Transactional database consists of a file where each record represents a transaction. A transaction typically includes a unique transaction identity number (trans ID) and a list of the items making up the transaction (such as items purchased in a store).


The transactional database may have additional tables associated with it, which contain other information regarding the sale, such as the date of the transaction, the customer ID number, the ID


number of the salesperson and of the branch at which the sale occurred, and so on.


Advanced Data and Information Systems and Advanced Applications


The new database applications include handling spatial data (such as maps), engineering design data (such as the design of buildings, system components, or integrated circuits), hypertext and multimedia data (including text, image, video, and audio data), time-related data (such as historical records or stock exchange data), stream data (such as video surveillance and sensor data, where data flow in and out like streams), and the WorldWideWeb (a huge, widely distributed information repository made available by the Internet).


These applications require efficient data structures and scalable methods for handling complex object structures; variable-length records; semi structured or unstructured data; text, spatiotemporal, and multimedia data; and database schemas with complex structures and dynamic changes.



Object-Relational Databases:Object-relational databases are constructed based on an object-relational data model. This model extends the relational model by providing a rich data type for handling complex objects and object orientation object-relational databases are becoming increasingly popular in industry and applications.


The object-relational data model inherits the essential concepts of object-oriented databases Each object has associated with it the following:


A set of variables that describe the objects. These correspond to attributes in the entity relationship


and relational models.


A set of messages that the object can use to communicate with other objects, or with the rest of the database system.


A set of methods, where each method holds the code to implement a message. Upon receiving a message, the method returns a value in response. For instance, the method for the message get photo(employee) will retrieve and return a photo of the given employee object.


Objects that share a common set of properties can be grouped into an object class. Each object is an instance of its class. Object classes can be organized into class/subclass hierarchies so that each class represents properties that are common to objects in that class


Temporal Databases, Sequence Databases, and Time-Series Databases


A temporal database typically stores relational data that include time-related attributes. These attributes may involve several timestamps, each having different semantics.


A sequence database stores sequences of ordered events, with or without a concrete notion of time. Examples include customer shopping sequences, Web click streams, and biological sequences. A time series database stores sequences of values or events obtained over repeated measurements of time (e.g., hourly, daily, weekly). Examples include data collected from the stock exchange, inventory control, and the observation of natural phenomena (like temperature and wind).


Spatial Databases and Spatiotemporal Databases


Spatial databases contain spatial-related information. Examples include geographic (map) databases, very large-scale integration (VLSI) or computed-aided design databases, and medical and satellite image databases.


Spatial data may be represented in raster format, consisting of n-dimensional bit maps or pixel maps. For example, a 2-D satellite image may be represented as raster data, where each pixel registers the rainfall in a given area. Maps can be represented in vector format, where roads, bridges, buildings, and lakes are represented as unions or overlays of basic geometric constructs, such as points, lines, polygons, and the partitions and networks formed by these components.



“What kind of data mining can be performed on spatial databases?” you may ask. Data mining may uncover patterns describing the characteristics of houses located near a specified kind of location, such as a park, for instance. A spatial database that stores spatial objects that change with time is called a spatiotemporal database, from which interesting information can be mined


Text Databases and Multimedia Databases


Text databases are databases that contain word descriptions for objects. These word descriptions are usually not simple keywords but rather long sentences or paragraphs, such as product specifications, error or bug reports, warning messages, summary reports, notes, or other documents.


Text databases may be highly unstructured (such as some Web pages on the WorldWideWeb). Some text databases may be somewhat structured, that is, semistructured (such as e-mail messages and many HTML/XML Web pages), whereas others are relatively well structured (such as library catalogue databases). Text databases with highly regular structures typically can be implemented using relational database systems.


“What can data mining on text databases uncover?” By mining text data, one may uncover general and concise descriptions of the text documents, keyword or content associations, as well as the clustering behavior of text objects.


Multimedia databases store image, audio, and video data. They are used in applications such as picture content-based retrieval, voice-mail systems, video-on-demand systems, the World Wide Web, and speech-based user interfaces that recognize spoken commands. Multimedia databases must support large objects, because data objects such as video can require gigabytes of storage. Specialized storage and search techniques are also required. Because video and audio data require real-time retrieval at a steady and predetermined rate in order to avoid picture or sound gaps and system buffer overflows, such data are referred to as continuous-media data.


Heterogeneous Databases and Legacy Databases


A heterogeneous database consists of a set of interconnected, autonomous component databases. The components communicate in order to exchange information and answer queries. Objects in one component database may differ greatly from objects in other component databases, making it difficult to assimilate their semantics into the overall heterogeneous database.


A legacy database is a group of heterogeneous databases that combines different kinds of data systems, such as relational or object-oriented databases, hierarchical databases, network databases, spreadsheets, multimedia databases, or file systems. The heterogeneous databases in a legacy database may be connected by intra or inter-computer networks.


Data Streams


Many applications involve the generation and analysis of a new kind of data, called stream data, where data flow in and out of an observation platform (or window) dynamically. Such data streams have the following unique features: huge or possibly infinite volume, dynamically changing, flowing in and out in a fixed order, allowing only one or a small number of scans, and demanding fast (often real-time) response time.


Typical examples of data streams include various kinds of scientific and engineering data, time-series data, and data produced in other dynamic environments, such as power supply, network traffic, stock exchange, telecommunications, Web click streams, video surveillance, and weather or environment monitoring.


Mining data streams involves the efficient discovery of general patterns and dynamic changes within stream data.


The World Wide Web

The World Wide Web and its associated distributed information services, such as Yahoo!, Google, America Online, and AltaVista, provide rich, worldwide, on-line information services, where data objects are linked together to facilitate interactive access. Users seeking information of interest traverse from one object via links to another. Such systems provide ample opportunities and challenges for data mining.


For example, understanding user access patterns will not only help improve system design (by providing efficient access between highly correlated objects), but also leads to better marketing decisions (e.g., by placing advertisements in frequently visited documents, or by providing better customer/user classification and behavior analysis). Capturing user access patterns in such distributed information environments is called Web usage mining (or Weblog mining).

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