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Online listings for Accommodation sharing:
Many people search online to find a nearby accommodation on rent, but some may not need it wholly for their own. In such cases it is wise to share the accommodation with someone who is willing to pay for it. Flatmates.com.au allows its customers to post their accommodation share offerings on its website. The customers can share some good pictures of their house with an appropriate price. The visitors of the website can contact other customers for more details.
Summary and Recommendations:
On the basis of the above SWOT analysis, I can say that Flatmates.com.au has a lot of opportunities to grow its business in future. The ubiquitous nature of online services is the strength of Flatmates.com.au which is a completely platform for accommodation share listing services. The organization does not produce any physical product. It only acts as a communication platform to be used by different people in need.
The idea of sharing accommodation not only benefits the customers financially but also allows them to socialize with people. Before sharing the accommodation with a complete stranger, people may want to communicate with them. This generates an opportunity for flatmates.com.au to take place as their communication channel. This opportunity also has incorporates building a user community that can be target for future projects of the organization.
Only the registered members can use the services provided by Flatmates.com.au, so there is a level of security that prevents the interference of user information exchange. Once a customer contacts some other customer for getting the details about his house or flat, he may also share some additional information about himself with the other customer. This functionality provides the customer with the control over their private information.
The online services of public domain are always considered to be under the risk of hacker attacks and malware infections. To minimize this risk, Flatmates.com.au ensures that all the customers have limited or controlled access to the available data. Use of outdated or inefficient cybersecurity techniques may result in a serious security breach. Such an event will also affect the reputation of the organization among its customers. So, Flatmates.com.au has to update its security standards regularly for better protection against these threats.
Following are some recommendations for Flatmates.com.au based on the above SWOT analysis.
Flatmates.com.au should determine criteria to identify inactive users and delete their information from the website after a certain period of inactivity. This solution will help the organization to make optimal use of its resources and remove the clutter from their storage. I also recommend the organization to encourage users to give feedback for their services.
Mid-Term(next 12 months)
The user community of Flatmates.com.au includes a large number of college students and young professionals. These users tend to change their accommodations frequently. So, to target such an audience, Flatmates.com.au has to stay in touch with them. Collaboration with other related businesses including home furnishing, renovation and relocation services can prove to be beneficial for the organization in long run.
Long Term(next 3 to 5 years)
The organization should advertise their services on various online and offline platforms to reach a wider range of prospective customers. The organization needs to develop a sense of trust among its customers by providing loyalty benefits and developing interactive discussion forums. Flatmates.com.au can provide their services to the people living outside Australia just by allowing them to post and search accommodation share listings on the already available online platform.
Essay on the topic “Data Mining”
Data mining is the part of the database management system to manage the flow of information in a system. The huge amount of data is transferred in an information industry on a daily basis. To give this raw data a structure, data mining is used. If the usefulness of the transferred data is considered then data mining should be used as a part of extracting important information from it. In general, data mining is a procedure which is used by small or large organizations to have an efficient use of the data. Due to the large volume of the data, it is difficult for organizational authorities to manage the statistics of the company. It is difficult this type of complex information from the gathered bulk data. Data mining techniques have been implemented in the functional areas like to detect fraud against a network, to explore new technologies and also to study or research about the current market trends. This report will be a discussion about the different data mining techniques, its importance and effective use in the industries. The first section will provide a brief definition of data mining, then the following sections will provide techniques and applications of data mining. This report is an overview of the data mining process essential for monitoring the data flow in an organization.
The index terms for the project are as follows Data mining, data mining process, data warehousing, applications of data mining and data mining techniques.These are some of the key terms which will be used to write the description about the data mining process. I have selected this project topic to analyze the IT infrastructure of the trending industries around the globe. The above-mentioned topic will be covered in this report as the part of my research.
The data mining techniques are essential for business growth of an organization; as most of the organizations a view the effective information for improving their business processes. The process of data mining begins with the collection of the data from the database. This information is then extracted into useful data by appropriate planning. The stored data is analyzed and quantified according to the requirements of an organization. The quantified data is graphically represented with the help of a graph or a chart.
Subtopics and Supporting Arguments
According to ProgrammerInterview.com (2017), The data mining process is effective for industries in determining the return on investment statistics for the used development cost of a project. This type of process initializes a business opportunity to the organizations. It also enables the companies to promote their business not only to the domestic consumers but also for the international consumers. Data mining terminology is useful in analyzing the data that can be useful to identify the weaknesses of an organization and provide corresponding business solutions. The business solutions include proper planning of an IT strategic plan, use of advertising for services, and to enhance the business operations of an organization. The data which is extracted from the data mining process is used to make important business decisions using certain marketing policies.
The centralized data in the large business organizations is known as the data warehousing. The data is said to be centralized because it can be accessed from one location to different parts of the organization (Tutorialspoint, 2017). For example data is transferred from headquarters to local branches of an information technology company. The data warehouse is the non-volatile collection of data which also includes subject-oriented, time variant delivery of information over the different communication channels of an organization. The data warehousing process enables the management of an organization to make perfect decision to improve their business value (Zandbergen, n.d.).
Applications of data mining
The data mining process is used in various fundamental sectors as discussed below-
The great source to reach for consumer’s reviews for products of any brand in the market is by interacting with consumers in a way they could get the informative data from that advertisement. In this case, the business analytical skills are used. Data mining is used to analyze financial profit statistics of a company. Most of the multimedia and telecommunication industries are utilizing this technique of data mining to understand the requirements of customers to maintain a standard in the market.
The insurance companies store a huge amount of data in their database systems. This data consists of the information regarding the client’s application for loan, to open a new account, change account and other details. Since, the insurance company have all the personal information of a client these are prone to cyber vulnerabilities. These complex problems involve customer attrition and fraud cases of client’s information. The data mining techniques ensures the insurance companies to secure their data from any fraud happenings by providing the risk analysis and risk management strategies.
As we know, the education hubs worldwide consist of a large amount of information of students. These details include contact information of a student and also all of their personal information. Data mining provides strategies to effectively manage the student’s data in their database management systems. This formatted information received from the data mining process is used for the educational industries to know the progress reports of a student and to manage their academic progress accordingly. The data mining process also helps the staff members to retrieve the last saved information from the database systems.
The manufacturing industry has to be updated about their supply chains for the products or goods. To detect the issues like quality of product or brand equity, it is essential to prepare a plan on how to deal with the needs of a consumer. The data mining process helps the manufacturers to examine the requirements of a consumer but analyzing their IT assets from the current statistics of the market. Manufacturing industries have a large amount of data about clients, suppliers, and retailers that has to be stored and managed on a daily basis.
The data morning techniques are essential for retailers to enhance their relationships with consumers by making effective deals and offers that could be reliable for the consumer. The most common market strategy is to decrease the price of a product without compromising in quality. The retailers can adopt the effective data models to make their business much easier and to offer campaigns to the targeted customers. The competitors of the retailers are the e-commerce companies that are setting up their business in various parts of the world. To achieve success
Tools and Techniques used in Data Mining
According to Witten et al (2016), “Data mining techniques are used in many research areas, including mathematics, genetics and marketing. While data mining techniques are a means to drive efficiencies and predict customer behavior, if used correctly, a business can set itself apart from its competition through the use of predictive analysis. A type of data mining used in customer relationship management, integrates information gathered by traditional data mining methods and techniques over the web. Web mining aims to understand customer behavior and to evaluate how effective a particular website”.
As per Martin Brown (2012), “data mining is about processing data and identifying patterns and trends in that information so that you can decide or judge”. The data mining principles are used to evaluate the efficiency of the big data in the database systems. This type of extensive big data has to be modified according to the business structure of an organization for which the data mining techniques are used.
1.Association: this type of data mining technique represents the correlation between different entities of two or more items in a database. The ‘InfoSphere Warehouse’ software tool is used in the database to detect the information flow that can be useful to understand the relationship between different items like a product and a consumer.
2.Classification: classification of the data is done on the basis of the objects that are used in the collected information of a database system.This type of technique is used to manage the data according to the groups such as one which have been associated with a social entities and the one which have been considered to be a single entity.
3.Clustering: Clustering allows users to classify between the given attributes of an item to arrange them in a sequence according to their requirements. Clustering provides a structure to the data that can be controlled by the database systems. Clustering is a reasonable approach to the non-volatile information flow within a database system (IBM, 2017). Data recovery techniques in data mining
Data mining industry is vulnerable to accidental data loss and damage to the consistency of data in adverse situations. This loss of important information is not acceptable in many cases and can also result in serious failures. The database mining techniques allow us to prevent these situations and recover the lost data.
The missing values in database can also be found or estimated with the use of different recovery strategies. Following are the levels of recovery which can be possible:
2.Rollback to a restore point.
3.Going back to a previous state.
4.Restoring to the last valid state
5.Recovering the data based on consistency.
6.Preventing system failure or crash.
The best suitable recovery technique is determined by identifying the severity of the situation.
It is a set of actions to be performed as a first aid to the data loss situation. Once the system crashes, this technique attempts to rollback the system to the last valid state. This technique does not allow the user to continue from where the system had crashed. It allows the user to regenerate the lost data starting from the last valid state. Supported levels of recovery are: 4 & 5.
This incorporates adding the small units of modified information to the database in regular intervals and checking the behavior of system with this change. Supported levels of recovery are: 3 and 4.
The set of implemented actions is recorded and analyzed if these actions can be reversed. In case of data loss, the situation is handled by rolling back the faulty actions. Supported levels of recovery are: 1, 2, and 3.
All the changes are recorded in a separate file that is later merged with the original file. In case of data loss, the merged file is detached from the system. Supported levels of recovery are: 2, and 3.
Copying from backup
Create a backup of sensitive sectors of the database and recover the valid state from this backup in case of data loss. Supported levels of recovery are: 2, and 3.
Keeping multiple copies
In case of small databases, multiple copies of the files can be saved and used as a backup at the time of accidental data loss. A comparison between the files can also be helpful in determining the most consistent version of database. Supported level of recovery is: 6.
This method keeps both the original file and modified file in database and deletes the original file after ensuring proper implementation of the applied changes. Supported levels of recovery are: 2 and 6.
A set of the above-mentioned techniques can also be used simultaneously for increased protection against data loss situations.
1.The implementation of the data recovery techniques may enforces following actions:
2.The structure of data is changed.
3.Change in data update and manipulation procedures.
According to IBM (2017), “Data mining is more than running some complex queries on the data you stored in your database. You must work with your data, reformat it, or restructure it, regardless of whether you are using SQL, document-based databases such as Hadoop, or simple flat files. Identifying the format of the information that you need is based upon the technique and the analysis that you want to do. After you have the information in the format you need, you can apply the different techniques (individually or together) regardless of the required underlying data structure or data set”.
This paper describes about the importance of data mining in various fundamental sectors. The paper describes about the usefulness of a data mining process in making effective decisions by organizations for their business growth.
Analysis of Turnitin Report
Based on the similarity index of the turnitin report the following questions have been answered.
a). Are any of the bold, colored text matches in my self-check report missing in-text references?
No, there are no such text segments.
b). Do any of the bold, colored text matches in my self-check report include more than three words in a row copied from the original source without quotation marks?
There is no such colored matches in the Turnitin document which have not been quoted. The data which has been used from the internet is properly referenced and marked as a quote as shown in the Turnitin report.
c). Do direct quotations take up more than 10% of the essay?
The essay on the topic “Data Mining” has been written in my own language. It has not been copied from any internet sources.
d). Are any of the bold, colored text matches in my originality report purely coincidental?
There are no such matches in the document. The text which is extracted from internet has been appropriately referenced.
e). Do any of the short strings of matching text indicate that my attempts at paraphrasing were not completely successful?
With the Turnitin document, it is clear that I have not taken any direct text from the internet sources. I have paraphrased the information and have written the text in my own language.
f). Have I synthesized all of the sources’ ideas into my essay by introducing each piece of source information with a signal phrase and by adding my own comments or interpretation to it in the following sentence?
I have written this essay based on my own thinking and ideas. I have reflected my ideas for data mining corresponding to the current trend for this type of technology.
IBM. (2017). Data mining techniques. Ibm.com. Retrieved 28 September 2017, from https://www.ibm.com/developerworks/library/ba-data-mining-techniques/
ProgrammerInterview.com. Data Mining vs. Data Warehousing. Programmer and Software Interview Questions and Answers. Retrieved 28 September 2017, from http://www.programmerinterview.com/index.php/database-sql/data-mining-vs-warehousing/
Tutorialspoint. (2017). Data Warehousing Overview. www.tutorialspoint.com. Retrieved 28 September 2017, from https://www.tutorialspoint.com/dwh/dwh_overview.htm
Data Warehousing Zandbergen, P. and Data Mining: Information for Business Intelligence - Video & Lesson Transcript. Study. Retrieved 28 September 2017, from http://study.com/academy/lesson/data-warehousing-and-data-mining-information-for-business-intelligence.html
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.