There are the following methods for designing an Individual Fact Table which is as follows −Choosing the Data Mart − It can be choosing the data mart in the simplest method is the same as choosing the legacy source of information. Typical data marts involve purchase orders, shipments, retail sales, payments, or user connections. These can be an instance of single-source data marts.In some cases, it can define a data mart that should contain multiple-legacy sources. The instance of a multiple-source data mart is user profitability, where legacy sources that define revenue should be combined with legacy sources that represent ... Read More

Dimensional modeling is a logical design method that follows to present the data in a standard structure that is perceptive and enables high-performance access. It is genetically dimensional and observes to a discipline that needs the relational model with several restrictions.Each dimensional model is composed of one table with a multipart key, known as the fact table, and a group of smaller tables known as dimension tables. Each dimension table has an individual element primary key that correlates to one of the elements of the multipart key in the fact table. This distinctive star-like structure is known as star join. ... Read More

There are various approaches of Business Dimensional Lifecycle which are as follows −Project planning − Project planning addresses the description and scoping of the data warehouse project, such as readiness evaluation and business justification. These are the tasks because of the high visibility and costs related to data warehouse projects.Project planning targets resource and skill-level staffing requirements, coupled with project task assignments, continuation, and sequencing. The resulting integrated project plan recognizes all tasks related to the Business Dimensional Lifecycle and the parties included. It can deliver as the foundation for the ongoing administration of the data warehouse project. Project planning ... Read More

Data staging is a major process that includes the following subprocesses which are as follows −Extracting − The extract step is the first phase of getting information into the data warehouse environment. Extracting defines reading and learning the source data, and copying the elements that are required to the data staging area for more work.Transforming − Because the data is extracted into the data staging area, there are several possible transformation processes, are as follows −It can be cleaning the data by correcting misspellings, resolving domain conflicts (including a city name that is inconsistent with a postal code), dealing with ... Read More

There are various elements of a data warehouse system which are as follows −Source System − An operational system of data whose service it is to capture the transactions of the business. A source system is known as a “legacy system” in a mainframe environment.The features of the source system are uptime and availability. Queries opposite to source systems are definite, “account-based” queries that are elements of the normal transaction flow and firmly restricted in their demands on the legacy system.Data Staging Area − A storage area and group of processes that simple, transform, combine, de-duplicate, household, archive, and produce ... Read More

The trends in data mining are as follows −Application exploration − Early data mining applications targeted generally on helping businesses gain a competitive edge. The exploration of data mining for businesses continues to expand as e-commerce and e-marketing have become mainstream components of the retail market.Data mining is increasingly used for the exploration of applications in several areas, including financial analysis, telecommunications, biomedicine, and science. Emerging software areas contain data mining for counterterrorism (including and beyond intrusion detection) and mobile (wireless) data mining. As generic data mining systems can have limitations in dealing with application-specific issues, it can view a ... Read More

There are various features of data mining that are as follows −Data types − Most data mining systems that are accessible in the industry handle formatted, record-based, relational-like data with statistical, categorical, and symbolic attributes. The data can be in the form of ASCII text, relational database data, or data warehouse data. It is essential to test what exact format(s) each system it is treating can handle.Some types of data or applications can require specialized algorithms to search for patterns, and so their requirements cannot be managed by off-the-shelf, generic data mining systems. Rather than, specialized data mining systems can ... Read More

The following are areas in which data mining technology can be used or created for intrusion detection which are as follows −Development of data mining algorithms for intrusion detection − Data mining algorithms can be used for misuse detection and anomaly detection. In misuse detection, training information is labeled as either “normal” or “intrusion.” A classifier can then be changed to detect known intrusions.There is multiple research in this area that has contained the application of classification algorithms, association rule mining, and cost-sensitive modeling. Anomaly detection constructs models of normal behavior and automatically detects significant deviations from it and supervised ... Read More

The security of our computer systems and information is at constant risk. The extensive growth of the web and increasing accessibility of tools and tricks for intruding and attacking networks have prompted intrusion detection to become an important element of network administration. An intrusion can be represented as any set of events that threaten the integrity, confidentiality, or availability of a network resource (including user accounts, file systems, system kernels, etc).Some commercial intrusion detection systems are limiting and do not support a whole solution. Such systems generally employ misuse detection approaches. Misuse detection searches for designs of program or user ... Read More

There are the following aspects of data mining for biological data analysis which are as follows −Semantic integration of heterogeneous, distributed genomic and proteomic databases − Genomic and proteomic data sets are generated at multiple labs and by various methods. They are distributed, heterogeneous, and of a wide variety. The semantic integration of such data is important to the cross-site analysis of biological records.Furthermore, it is essential to find correct linkages among research literature and their related biological entities. Such integration and linkage analysis can support the systematic and coordinated analysis of genome and biological records. This has promoted the ... Read More