Multidimensional Data Model

This data is collected and organized in order to process orders and maintain good customer service. The logical view of data would allow a knowledge worker to arrange and access information based on the needs of the business separating it from the physical view of how information is arranged and stored. The ability to do this allows for an employee to create detailed reports in order to determine information such as customer information and their order numbers and dates. This is imperative for a company like Comcast who has over 27 million customers in order to have a system to keep important data to analyze. Using a data warehouse allows them to gather from several databases and then the company can use the information to determine for example how many units of voice products are sold to create the necessary business intelligence to make future decisions and remain

The database schema used in gradTrack is a star schema which has a fact table in the centre and multiple lookup tables surrounding it.

What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data

One crucial thing that organizations need to consider in today’s unstructured data world is to successfully integrate data warehouses. For this, the companies need to re-consider their enterprise data architecture and classify the governance strategy that can be talented through such efforts. There lies a need for data managers

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

The dimension table for Location contains concepts such as city, state and country. Similarly, the dimension table for Time contains concepts such as year, month, week, date and hour. PROBLEMS:

·         Moving the data into data marts, where it is often managed by a multidimensional engine

The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the “business data warehouse”. Data warehouse (DW) is an application which allowed you to execute ad-hoc queries; multi-dimensional analysis and query information by

Furthermore, the Gartner website argues that “BI has become a strategic initiative and is now recognised by chief information officers (CIOs) and business leaders as instrumental in driving business effectiveness and innovation,” (Anon., 2007). Gartner also argues that “BI projects were the number one technology priority for 2007” (Anon., 2007). According to the Bill Inmon, data warehouse is “a subject-oriented, integrated, time variant and non-volatile collection of data used in strategic decision making”. Hammergen & Simon, (2009) define data warehouse more simpler by saying that “ Data warehousing is therefore the process of creating an architected information management solution to enable analytical and information processing despite platform, application, organizational, and other barriers.“ It is important to note that data warehouse system is different from relational database. The reasons of that are: (1) In the data warehouse data is stored for long term; (2) DW is designed for high performance for analytical queries; (3) its OLAP (Online Analytical Processing) technology enables to view data in various form; (4) linking between tables are simple (Tushman, 2014). Databases, in contrast, have a low performance regarding data analysis; joins between tables are

A data warehouse (DW) can be acknowledged as one of the most complex information system modules available and it is a system that periodically retrieves and consolidates data from the sources into a dimensional or normalized data store. It is an integrated, subject-oriented, nonvolatile and a time-variant collection of data in support of management’s decisions (Inmon, 1993).

Data model is managing a large quantity of organized or unorganized data. Data model identify clearly in data modeling.

Data mart is a simple form of a data warehouse that is focused on a single subject, such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. Given their single-subject focus, data marts usually draw data from only a few sources. The sources could be internal operational systems, a central data warehouse, or external data. De-normalization is the norm for data modeling techniques in this system. Online Analytical Processing or OLAP is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. OLAP system response time is an effectiveness measure that is used by Data Mining techniques. OLAP databases store aggregated, historical data in multi-dimensional 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. Online Transaction Processing or OLTP is characterized by a large number of short on-line transactions. OLTP systems emphasize very fast query processing and maintaining data integrity in multi-access environments. OLTP systems the number of transactions per second measures effectiveness; this contains detailed and current data. The schema used to store transactional databases is the entity model. Normalization is the norm for data modeling techniques in this system. Predictive Analysis is about

Data warehouse are multiple databases that work together. In other words, data warehouse integrates data from other databases. This will provide a better understanding to the data. Its primary goal is not to just store data, but to enhance the business, in this case, higher education institute, a means to make decisions that can influence their success. This is accomplished, by the data warehouse providing architecture and tools which organizes and understands the

Connolly and Begg (2009) defines a data model as “an integrated collection of concepts for describing and manipulating data, relations between data, and constraints on the data in an organization.” Simply, a data model is a representation of how the different types of data interact. They can be categorized into three main broad aspects, which are object based, record based and physical data model. The object based model include the Entity- Relationship, Semantic, Functional and Object Oriented Model. The record based comprise of Relational Data Model, Network Model and the Hierarchical Model. The physical data model “describe how data is stored in the computer,

Three kinds of data need to be stored: fact table data (the transactional records), aggregates, and dimensions. The multidimensional format does not perform well when there are more than few dimensions which will lead to sparse data. A HOLAP , a hybrid OLAP system solves this problem by only storing the aggregates in multidimensional format.