Educational Data Mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. Pages in category "Data mining" The following 56 pages are in this category, out of 56 total. This list may not reflect recent changes.
Wikipedia's open, crowdsourced content can be data mined. From its articles, their pageviews, WikiProject-assessments, infoboxes, a variety of metadata (such as on. Data mining has been used in many applications. The following are some notable examples of usage. Educational Data Mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from. Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. Pages in category "Data mining" The following 56 pages are in this category, out of 56 total. This list may not reflect recent changes.
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.
The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.
The typical Extract, transform, load (ETL)-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data wikipedia data mining the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.
The main source of the data is cleansed, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform, and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, wikipedia data mining, an expanded definition for data warehousing includes business intelligence tools, data mining pang ning to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.
A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:
- Integrate data from multiple sources into a single database and data model. More congregation of data to single database so a single query engine can be used to present data in an ODS.
- Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, wikipedia data mining, long-running, analysis queries in transaction processing outland mining data history, even if the source transaction systems do not.
- Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.
- Improve data quality, by providing consistent codes and descriptions, flagging or even fixing bad data.
- Present the organization's information consistently.
- Provide a single common data model for all data of interest regardless of the data's source.
- Restructure the data so that it makes sense to the business users.
- Restructure the data so that it delivers excellent query performance, even for complex analytic queries, without impacting the operational systems.
- Add value to operational business applications, notably customer relationship management (CRM) systems.
- Make decision–support queries easier to write.
- Organize and disambiguate repetitive data.
The environment for data warehouses and marts includes the following:
- Source systems that provide data to the warehouse or mart;
- Data integration technology and processes that are needed to prepare the data for use;
- Different architectures for storing data in an organization's data warehouse or data marts;
- Different tools and applications for the variety of users;
- Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes.
In regards to source systems listed above, R. Kelly Rainer states, "A common source for the minecraft mining shafts in data warehouses is the company's operational databases, which can be relational databases".
Regarding data integration, Rainer states, "It is necessary to extract data from source systems, wikipedia data mining, transform them, and load them into a data mart or warehouse".
Rainer discusses storing data in an organization's data warehouse or data marts.
Metadata are data about data. "IT personnel need information about data sources; database, table, and column names; refresh schedules; and data usage measures".
Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient wikipedia data mining of data and information by analysts and managers. A "data warehouse" is a repository of historical data that are organized by subject to support decision makers in the organization. Once data are stored in a data mart or warehouse, they can be accessed.
Types of systems
A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), hence they draw data from a limited number of sources such as sales, finance or marketing. How the data mining works marts are often built and controlled by a single department within an organization. The sources could be internal operational systems, a central data warehouse, or external data. Denormalization is the norm for data modeling techniques in this system. Given that data marts generally cover only a subset of the data contained in a data warehouse, they are often easier and faster to implement.
|Attribute||Data warehouse||Data mart|
|Scope of the data||enterprise-wide||department-wide|
|Number of subject areas||multiple||single|
|How difficult to build||difficult||easy|
|How much time takes to build||more||less|
|Amount of memory||larger||limited|
Types of data marts include dependent, independent, and hybrid data marts.[clarification needed]
Online analytical processing (OLAP) is characterized by a relatively low wikipedia data mining of transactions. Queries are often very complex and involve aggregations. For OLAP systems, response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. OLAP databases store aggregated, historical data in multi-dimensional schemas (usually star schemas). OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day. The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives. The three basic operations in OLAP are : Roll-up (Consolidation), Drill-down and Slicing & Dicing.
Online transaction processing (OLTP) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments. For OLTP systems, effectiveness is measured by the number of transactions per second. OLTP databases contain detailed and current data. The schema used to store transactional databases is the entity model (usually 3NF). Normalization is the norm for data modeling techniques in this system.
Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for customer relationship management surface mining equipment concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". Wikipedia data mining essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations, wikipedia data mining, it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as legacy systems), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "data marts" that were tailored for ready access by users.
Key developments in early years of data warehousing were:
- 1960s – General Mills and Dartmouth College, in a joint research project, develop the terms dimensions and facts.
- 1970s – ACNielsen and IRI provide dimensional data marts for retail sales.
- 1970s – Bill Inmon begins to define and discuss the term: Data Warehouse.
- 1975 – Sperry Univac introduces MAPPER (MAintain, Prepare, and Produce Executive Reports) is a database management and reporting system that includes the world's first 4GL. First platform designed for building Information Centers (a forerunner of contemporary data warehouse technology)
- 1983 – Teradata introduced the DBC/1012 database computer specifically designed for decision support.
- 1984 – Metaphor Computer Systems, founded by David Liddle and Don Massaro, released a hardware/software package and GUI for business users to create a database management and analytic system.
- 1985 - Sperry Corporation publish an article (Martyn Jones and Philip Newman) on information centres, where they introduce the term MAPPER data warehouse in the context of information centres.
- 1988 – Barry Devlin and Paul Murphy publish the article An architecture for a business and information system where they introduce the term "business data warehouse".
- 1990 – Red Brick Systems, founded by Ralph Kimball, introduces Red Brick Warehouse, a database management system specifically for data warehousing.
- 1991 – Prism Solutions, founded by Bill Inmon, introduces Prism Warehouse Manager, software for developing a data warehouse.
- 1992 – Bill Inmon publishes the book Building the Data Warehouse.
- 1995 – The Data Warehousing Institute, a for-profit organization that promotes data warehousing, is founded.
- 1996 – Ralph Kimball publishes the book The Data Warehouse Toolkit.
- 2012 – Bill Inmon developed and made public technology known as "textual disambiguation". Textual disambiguation applies context to raw text and reformats the raw text and context into a standard data base format. Once raw text is passed through textual disambiguation, it can easily and efficiently be accessed and analyzed by standard business intelligence technology. Textual disambiguation is accomplished through wikipedia data mining execution of textual ETL. Textual disambiguation is useful wherever raw text is found, such as in documents, wikipedia data mining, Hadoop, email, wikipedia data mining, and so forth.
A fact is a value or measurement, which represents a fact about the managed entity or system.
Facts, as reported by the reporting entity, are said to be at raw level. E.g. in a mobile telephone system, if a BTS (base transceiver station) received 1,000 requests for traffic channel allocation, it allocates for 820, and rejects the remaining, it would report 3 facts or measurements to a management system:
- tch_req_total = 1000
- tch_req_success = 820
- tch_req_fail = 180
Facts at the raw level are further aggregated to higher levels in various dimensions to extract more service or business-relevant information from it. These are called aggregates or summaries or aggregated facts.
For instance, if there are 3 BTSs in a city, then the facts above can be aggregated from the BTS to the city level in the network dimension. For example:
Dimensional versus normalized approach for storage of data
There are three or more leading approaches to storing data in a data warehouse — the newest bitcoin mining hardware important approaches are the dimensional approach and the normalized approach.
The dimensional approach refers to Ralph Kimball's approach in which it is stated that the data warehouse should be modeled using a Dimensional Model/star schema. The normalized approach, also called the 3NF model (Third Normal Form) refers to Bill Inmon's approach in which it is stated that the data warehouse should be modeled using an E-R model/normalized model.
In a dimensional approach, transaction data are partitioned into "facts", which are generally numeric transaction data, wikipedia data mining, and "dimensions", which are the reference information that gives context to the wikipedia data mining. For example, a sales transaction can be broken up into facts such farming simulator 2015 bjornholm mining the number of products ordered and the total price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order.
A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly. Dimensional structures are easy to understand for business users, because the structure is divided into measurements/facts and context/dimensions. Facts are related to the organization's business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008). Another advantage offered by wikipedia data mining model is that it does not involve a relational database every time. Thus, this type of modeling technique is very useful for end-user queries in data warehouse.
The main disadvantages of the dimensional approach are the following:
- To maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated.
- It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business.
In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by subject areas that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joins. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented (Kimball, Ralph 2008). The main advantage of this approach is that it is straightforward to add information into the database. Some disadvantages of this approach are that, because of the number of tables involved, it can be difficult for users to join data from different sources into meaningful information and to access the information without a precise understanding of the sources wikipedia data mining data and of the data structure of the data warehouse.
Both normalized and dimensional models can be represented in entity-relationship diagrams as both contain joined relational tables. The difference between the mining in wh models is the degree of normalization (also known as Normal Forms). These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree (Kimball, Ralph 2008).
In Information-Driven Business, Robert Hillard proposes an approach to comparing the two approaches based on the information needs of the business problem. The technique shows that normalized models hold far more information than their dimensional equivalents (even when the same fields are used in both models) but this extra information comes at the cost of usability. The technique measures information quantity in terms of information entropy and usability in terms of the Small Worlds data transformation measure.
In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes. These data marts can then be integrated to create a comprehensive data warehouse. The data warehouse bus architecture is primarily an implementation of aruba mining bus", a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.
The top-down approach is designed using a normalized enterprise data model. "Atomic" data, wikipedia data mining, that is, data at the greatest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse.
Data warehouses (DW) often resemble the hub and spokes architecture. Legacy systems feeding the warehouse often include customer relationship management and enterprise resource planning, generating large amounts of data. To consolidate these various data models, and facilitate the extract transform load process, data warehouses often make use of an operational data store, the information from which is parsed into the actual DW. To reduce data redundancy, larger systems often store the data in a normalized way. Data marts for specific reports can then be built on top of the data warehouse.
A hybrid DW database is kept on third normal form to eliminate data redundancy. Wikipedia data mining normal relational database, however, is not efficient for business intelligence reports where dimensional modelling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The DW provides a single source of information from which the data marts can read, providing a wide range of business information. The hybrid architecture allows a DW to be replaced with a master data management repository where operational, not static information could reside.
The data vault modeling components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both third normal form and star schema. The data vault model is not a true third normal form, wikipedia data mining, and breaks some of its rules, wikipedia data mining, but it is a top-down architecture with a bottom up design. The data vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which when built, still steel mining wikipedia data mining use of a data mart or star schema based release area for business purposes.
Data Warehouse Architecture
The different methods used to construct/organize a data warehouse specified by an organization are numerous. The hardware utilized, software created and data resources specifically required for the correct functionality of a data warehouse are the main components of the data warehouse architecture. All data warehouse has multiple phases in which the requirements of the organization are modified and fine tuned.
Versus operational system
Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an wikipedia data mining model. Operational system designers generally follow Codd's 12 rules of database normalization to ensure data integrity. Fully normalized database designs (that is, those satisfying all Codd rules) often result in information from a business transaction being stored in dozens wikipedia data mining hundreds of tables. Relational databases are efficient at managing the relationships between these tables. The databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. To improve performance, older data are usually periodically purged from operational systems.
Data warehouses are optimized for analytic access patterns. Analytic shearer mining patterns generally involve selecting specific fields and rarely if ever 'select *' as is more common in operational databases. Because of these differences in access patterns, operational databases (loosely, OLTP) benefit from the use of a row-oriented DBMS whereas analytics databases (loosely, wikipedia data mining, OLAP) benefit from the use of a column-oriented DBMS. Unlike operational systems which maintain a snapshot of the business, data warehouses generally maintain an infinite history which is implemented through ETL processes that periodically migrate data from the operational systems over to the data warehouse.
Evolution in organization use
These terms refer to the level of sophistication of a data wikipedia data mining operational data warehouse
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Text mining - WikipediaPages in category "Data mining" The following 56 pages are in this category, out of 56 total. This list may not reflect recent changes. Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an. Data mining is a term from computer science. Sometimes it is also called knowledge discovery in databases (KDD). Data mining is about finding new information in a lot. Mining is the extraction of valuable minerals or other geological materials from the earth, usually from an orebody, lode, vein, seam, reef or placer deposits. Big data is data sets that are so voluminous and complex that traditional databases, search-based applications, data mining, distributed file. OLAP applications are widely used by Data Mining the operational systems on a regular basis and the data warehouse data are stored in a data structure.
Big data is data sets that are so voluminous and complex that traditional databases, search-based applications, data mining, distributed file. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database. OLAP applications are widely used by Data Mining the operational systems on a regular basis and the data warehouse data are stored in a data structure.
Data mining is a term from computer science. Sometimes it is also called knowledge discovery in databases (KDD). Data mining is about finding new information in a lot of data. The information obtained from data mining is hopefully both new and useful.
In many cases, data is stored so it can be used later. The data is saved with a goal. For example, a store wants to save what has been bought. They want to do this to know how much they should buy themselves, to have enough to sell later. Saving this information, makes a lot of data. The data is usually saved in a database. The reason why data is saved is called the first use.
Later, the same data can also be used to get other information that was not needed for the first use. The store might want to know now what kind of things people buy together when they buy at the store. (Many people who buy pasta also buy mushrooms for example.) That kind of information is in the data, and is useful, but was not the reason why the data was saved. This information is new and can be useful. It is a second use for the same data.
Finding new information that can also be useful from data, is called data mining.
Different kinds of data mining[change | change source]
For data, there a lot of different kinds of data mining for getting new information. Usually, prediction is involved. There is uncertainty in the predicted results. The following is based on the observation that there is a small green apple in which we can adjust our data in structural manner. Some of the kinds of data mining are:
- Pattern recognition (Trying to find similarities in the rows in the database, in the form of rules. Small -> green. (Small apples are often green))
- Using a Bayesian network (Trying to make something that can say how the different data attributes are connected/influence each other. The size and the colour are related. So if you know something about the size, you can guess the colour.)
- Using a Neural network (Trying to make a model like a brain, which is hard to understand, but a computer can tell that if the apple is green it has a higher chance to be sour, if we tell the computer the apple is green. So this is like a black box model, we do not know how it works, but it works.)
- Using Classification tree (With all other knowledge trying to say what one other thing about the thing we are looking at will be. Here is an apple with a size, a colour and shininess, what will it taste like?)
|Wikipedia data mining||Mining function|
|Wikipedia data mining||Data processing for data mining|
|Grade control in mining||512|