Concept hierarchies organize data or concepts in hierarchical forms or in certain partial order, these are used for expressing knowledge in concise, high-Ieve1 terms, and facilitating mining knowledge at multiple levels of abstraction. Concept hierarchies are also utilized to form dimensions in multidimensional databases and thus are essential components for data warehousing as well. In areas other than data mining, concept hierarchy is commonly called taxonomy. We adopt the term concept hierarchy because of its popularity in the community of data mining and knowledge discovery.
Concept Hierarchy in Data Warehousing:
While operational databases maintain state information, data warehouses typically maintain historical information. Although there are several forms of schema, e.g., star schema and snowflake schema, in the design of a data warehouse, the fact tables and dimension tables are its essential components. Users typically view the fact tables as multidimensional data cubes. The attributes of a dimension table may be organized as one or more concept hierarchies.
The use of concept hierarchies in a data warehousing system provides the foundation of operations roll-up and drill-down. To improve the
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Upon recognizing the importance of concept hierarchies, they proposed algorithms for mining generalized association rules, in which concept hierarchies are used for mining association rules and interesting rule detections. Interestingness is an important measure to determine the value of the discovered knowledge. In the complexity of a concept hierarchy is defined in terms of the number of its interior nodes, and the depth and height of each of these interior nodes. This complexity is then used to measure the interestingness of the discovered knowledge
The company can make use of SAP BW/4HANA warehouse in order to perform any analytical operations on real time data. Using this data warehouse the company can generate reports which will be helpful: • For the business managers to know more about their product manufacturing and distribution costs. These reports will provide them with necessary information so that they can build new ways to reduce overall
Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
Now we can say that an enterprise data warehouse could be used to manage the big data and the extreme workloads but we would find that often it is more efficient to preprocess the data before storing it in the warehouse. Let’s consider an example even data from hardware sensors have a lar...
A data warehouse can be defined (Section 5.2) as “a pool of data produced to support decision making.” This focuses on the essentials, leaving out characteristics that may vary from one DW to another but are not essential to the basic concept.
Data mining is a field that is a combination of numerous other fields such as the database research, artificial intelligence and statistics. Data mining involves looking for patterns in vast amounts of data as a part of knowledge discovery process. (Huang, Joshua Zhexue, Cao, Longbing, Srivastava, Jaideep, 2011) contains numerous papers that are solely dedicated to discussing the advancements that have been made in the field of data mining and knowledge discovery. A lot of people have performed a thorough research on all that has been done in data mining and the future possibilities that are soon to be implemented practically. The research not only covers the history and the reasons that led to various advancements being made but they also cover the detail models of the proposed solutions to deficiencies in existing systems.
The term Taxonomy refers to the classification of a subject using a hierarchical structure. Blooms taxonomy is a form of this system, designed and published by Benjamin Bloom in 1956. Bloom’s ideology follows the main principle of standard taxonomy using it to refer to the different levels of learning. These levels are known as ‘domains’ which are the different series
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Concepts are determined by attributes and definitions of key terms. Relationships are determined by concepts, and data derived from the various source vocabularies.
At the core of Management Information Systems is the ability to process and manage a vital human asset: information. Writing, in its original clay tablet and papyrus forms, is the original management information system, affecting society as a whole, not just through business, but also through government and religion. Writing allowed information to be widely accessible and manageable, beginning a dynamic process in which information lead to technological advances which in turn lead to greater information processing and management tools. At each stage of this process, technology transformed the medium of information: taking it from clay, to ink, to print, to analog, and now to digital. It is in this digital age that information is once again experiencing a renaissance transformation. More than ever, massive amounts of information, in the form of quantitative data and relevant business intelligence, is available to businesses and business partners. This new form of information is known as “big data,” which according to Viktor Mayer-Schonberger and Kenneth Cukier is not merely the ownership of large amounts of data, but also the “ability of society [including businesses] to harness information in novel ways to produce useful insights or goods and services of significant value.” A key example given by Cukier and Mayer in their book Big Data, is the case of the H1NI influenza outbreak in 2009. The U.S. Centers of Desease Control and Prevention (CDC) set out to map all outbreaks of H1N1. In practice, the CDC was able to identify outbreaks, through hospital reports, two weeks after actual events unfolded on the ground. With the help of Google and their data experts however, the CDC was able to locate outbreaks in near real-tim...
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Friedman, Uri. “Big Data: A Short History.” foreignpolicy.com. Foreign Policy, 8 Oct. 2012. Web. 16 Mar. 2014.
Databases are becoming as common in the workplace as the stapler. Businesses use databases to keep track of payroll, vacations, inventory, and a multitude of other taske of which are to vast to mention here. Basically businesses use databases anytime a large amount of data must be stored in such a manor that it can easily be searched, categorized and recalled in different means that can be easily read and understood by the end user. Databases are used extensively where I work. In fact, since Hyperion Solutions is a database and financial intelligence software developing company we produce one. To keep the material within scope I shall narrow the use of databases down to what we use just in the Orlando office of Hyperion Solutions alone.
The relational database model is based upon tables or relations. In this model, the physical implementation of the database is abstracted away from the user. Users query the database using a high-level query language, such as SQL. The relations are made up of columns, which have headings indicating the attribute represented by that column. Tables have key fields, which can be used to identify unique records. Keys relate tables to each other. The rows of the relation are also called tuples, and there is one tuple component for each attribute – or column – in that relation. A relation or table name, along with those relation’s attributes, make up the relational schema. Relational Database models are server-centric.
In our world, people rely heavily on the power of technology every day. Kids are learning how to operate an iPad before they can even say their first word. School assignments have become virtual, making it possible to do anywhere in the world. We can receive information from across the world in less than a second with the touch of a button. Technology is a big part of our lives, and without it life just becomes a lot harder. Just like our phones have such an importance to us in our daily lives, database management systems are the same for businesses. Without this important software, it would be almost impossible for companies to complete simple daily tasks with such ease.