Data Integration:
Data Mining requires data integration which means the merging of data from different multiple data stores. We can reduce by careful integration and we can avoid redundancies and inconsistencies in the resulting dataset. Due to this reduction, it helps in improving the accuracy and speed of the subsequent data mining process. The semantic heterogeneity and the structure of data will have great challenges in data integration.
Entity Identification Problem:
The data analysis task will helps in involving the data integration, which combines the data from multiple sources into a coherent data store. The various different multiple sources may include multiple databases,
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The metadata will also be used to help in transforming the data. When we match attributes from one database to another during the process of data integration, a very important one is to be noticed in the structure of the data. This is to ensure that any attribute functional dependencies and referential constraints in the source system should match those in the target system.
Redundancy and Correlation Analysis:
In data integration, one of the most important issues is redundancy. An attribute should be redundant if it is derived from another attribute or set of attributes. It also cause redundancies in inconsistencies in attribute or dimension naming. Some of the redundancies can also be detected by correlation analysis.
For example if we give two attributes, such analysis can measure how strongly one attribute implies the other, based on the available data. For nominal data, we use the χ2 (chi-square) test. For numeric attributes, we can use the correlation coefficient and covariance, both of which access how one attribute’s values vary from those of another.
Tuple
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Another source of data redundancy is the use of de normalized tables. Inconsistencies often arise between various duplicates, due to inaccurate data entry.
Data Value Conflict Detection and Resolution:
Data integration also involves the detection and resolution of data value conflicts. For example, if we take the real-world entity, attribute values from various different sources may differ. These differences may be due to in representation, scaling, or encoding. Attributes may also differ on the abstraction level, where an attribute in one system is recorded at, a lower abstraction level than the same attribute in another.
Association Rule:
The most important goal of association rule is to find the extraction correlation relationships in the large datasets of items.
Association rule mining:
Finding frequent patterns is called associations, among sets of items or objects in transaction databases, relational databases, and other information repositories.
Association Rule(Data
For example, consider a patient who is in an emergency situation and he is brought to Sinclair hospital by means of the ambulance, information about the patient will be recorded following a number of steps. Typically the patient will be received from the ambulance and a nurse will enter the heath data of the patient into electronic records document. As the patient continues to be seen by other health providers e.g. physicians, their health data will be recorded in electronic health care data. The computer in turn will build the data record of the patient. Assuming that the patient had been admitted in the facility sometimes ago, these will have a unique record number that will always be assigned to that patient. Apart from this unique code, the patient will also be given an account number which will be different in all encounters. The essence of using different account numbers will be to facilitate group charges. For example, consider a patient who had been admitted a week ago with malaria, he will be given an account on that day. A week after, if he is admitted to the same facility with a different kind of illness say allergic reactions he will be given a different account with same unique code. When finding the charges of the patient it will be in form of groups since it represents different
The next project deliverable is a robust, modernized database and data warehouse design. The company collects large amounts of website data and uses this data to analyze it for the company’s customers. This document will provide an overview of the new data warehouse along with the type of database design that has been selected for the data warehouse. Included in the appendix of this document is a graphical depiction of the logical design of the
matching software will end most too all data RE entry errors that were occurring. This in
Foundational Interoperability is data exchange from one system by another, Structural Interoperability is the exchange of information between systems and it is interpreted in the data field level, and Semantic Interoperability allows multiple system to exchange information (3). Currently I believe our organization is at the structural level, but our end goal of this project is to ensure we achieve Semantic
Another challenge to electronic healthcare data is erroneous information. One of the sources of errors in healthcare datasets is data entry errors. In healthcare systems, most data is entered by practitioners and staff, which makes the data prone to human error. Data entry errors can result from direct mistakes in transferring the data to computers, or from issues related to computer use (De Lusignan, Liaw et al. 2011). For example, using different computer softwares leads to inconsistent data entry practices and scoring systems (De Lusignan, Liaw et al. 2011).
After understanding the possible outcomes and usages of Big Data Mining and Analytics, the study of the process is necessary to identify the real possibilities behind this techniques and how this can improve a business performance. To do this; we should comprehend the basics about data mining and the process that leads from pure data to insights.
Nonetheless, there is no viable strategy to use these databases productively and to locate the important relationship in the middle of them. Association rule mining finds fascinating Association or connection among a lot of data things. With immense measures of data always being gathered and amassed, numerous enterprises and stores are demonstrating enthusiasm for mining relationship from this substantial accumulation of business exchange records, as it can help with numerous business basic leadership procedures, for example, index plan, cross-advertising and
[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.
Inconsistently storing organization data creates a lot of issues, a poor database design can cause security, integrity and normalization related issues. Majority of these issues are due to redundancy and weak data integrity and irregular storage, it is an ongoing challenge for every organization and it is important for organization and DBA to build logical, conceptual and efficient design for database. In today’s complex database systems Normalization, Data Integrity and security plays a key role. Normalization as design approach helps to minimize data redundancy and optimizes data structure by systematically and properly placing data in to appropriate groupings, a successful normalize designed follows “First Normalization Flow”, “Second Normalization Flow” and “Third Normalization flow”. Data integrity helps to increase accuracy and consistency of data over its entire life cycle, it also help keep track of database objects and ensure that each object is created, formatted and maintained properly. It is critical aspect of database design which involves “Database Structure Integrity” and “Semantic data Integrity”. Database Security is another high priority and critical issue for every organization, data breaches continue to dominate business and IT, building a secure system is as much important like Normalization and Data Integrity. Secure system helps to protect data from unauthorized users, data masking and data encryption are preferred technology used by DBA to protect data.
The key objective in any data mining activity is to find as many unsuspected relationships between obtained data sets as possible to be able to achieve a better understanding on how the data and its relationships are useful to the data owner. The potential of knowledge discovery using data mining is huge and data mining has been applied in many different knowledge areas such as in large corporations to optimize their marketing strategies or even to smaller scale in medicinal research where data mining is used to find the relationship patient’s data with the corresponding medicinal prescription and symptoms.
The database application design can be improved in a number of ways as described below:
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
There are various terms that are associated with Enterprise Data Management. Some of these terms are UML, OLAP, OLTP, Data Warehouse, Data Mart and Multi-Tier Architecture. Subsequently, these terms were covered during the five week course of DMB405 and will be explained in further detail throughout the course of the paper. Although the paper will not be all inclusive to the detail of each term, it will touch upon the definition, their use and their place in Enterprise Data Management. The first term that will be discussed is UML and how it relates to the subject at hand.
An Association analysis is used to show the relationships between people, groups, or organizations to show criminal or non-criminal activity. The association matrix is used as an interim product that includes police reports, surveillance reports, field interviews, corporate records, testimony, informant data, public record data, and other information. The association analysis can be used to indicate other possible criminal activity.