The database is an integral part of modern life. From the websites that we acces

Question

The database is an integral part of modern life. From the websites that we access, to the smartphone apps we use on a daily basis, to the purchases that we make online and at the store, data is constantly being collected.

This includes everything from cellphones, coffee makers, washing machines, headphones, lamps, wearable devices and almost anything else you can think of. This also applies to components of machines, for example a jet engine of an airplane or the drill of an oil rig. As I mentioned, if it has an on and off switch then chances are it can be a part of the IoT. The analyst firm Gartner says that by 2020 there will be over 26 billion connected devices... That's a lot of connections (some even estimate this number to be much higher, over 100 billion). The IoT is a giant network of connected "things" (which also includes people). The relationship will be between people-people, people-things, and things-things. (Morgan, 2014)

As we continue to add more devices, we add more data that needs to be stored and referred to in reports and marketing and business intelligence tools, among other things.

The internet of things has the potential to revolutionize most areas of life, including personally, professionally, and commercially. This can affect things like employment and how we connect with and interact with others. There are important considerations to take into account when determining the most efficient and secure means of storage.

Write several paragraphs regarding a plan to create data collection services and a data warehouse. The information should cover the following elements:

Summary of what a data warehouse is

Construction of data collection services from multiple potential sources to feed the data warehouse

Design principles that will affect the setup of data collection services from the IoT

Strategies for ensuring that quality, verified data is being fed into the data warehouse

Securing the data warehouse

Explanation / Answer

A plan to create data collection services and a data warehouse.

(SUMMARY OF WHAT A DATA WAREHOUSE IS)

Data in real world is dirty. To make this data useful,it is essential we convert it into a suitable form to present it to the user. This suitable form is called knowledge. Data needs to be cleaned and integrated from several external sources. Once it is done, the cleaned data is stored in the data warehouse for several purpose.Data warehouse is a central repository in which all the data is kept together for the organisational usage. It contains both historical as well as updated data to help users and organisation carry out there important task easily and accurately. Thus data warehouse possess the following characteristics- subject-oriented,integrated,non-volatile and time-variant.

(CONSTRUCTION OF DATA COLLECTION SERVICES FROM MULTIPLE SOURCES TO FEED THE DATA WAREHOUSE)

A data warehouse gets the data from several external sources. This data needs to be in appropriate form before it can be stored into the warehouse. ETL (Extract-Transform-Load) process is used on the data before feeding it into the data warehouse. Extract refers to extracting the data from several sources that is appropriate tto be stored in the data warehouse. Transform refers to transforming the data coming from several sources into a single form to be stored into the warehouse for maintaining the data warehouse consistency. Finally, Loading is done to load the data into the desired form into the warehouse to construct the data collection services required by the organisation. Various ETL tools such as Informtica, CloverETL, Oracle etc can help in feeding the data coming from difference potential sources into the warehouse.

(DESIGN PRINCIPLES THAT WILL AFFECT THE SETUP OF DATA COLLECTION FROM IoT)

The Internet of Things (IoT) is the interconnection of smart objects that generates data and transmit it over the Internet. Much of the IoT initiatives are geared towards manufacturing low-cost and energy-efficient hardware that provide objects interconnectivity.Traditional database management solutions fall short in satisfying the sophisticated application needs of an IoT network that has a truly global-scale. Thus, updated solutions for IoT data management address partial aspects of the IoT environment with special focus on sensor networks.In the context of IoT, data management should act as a layer between the objects and devices generating the data and the applications accessing the data for analysis purposes and services. The devices themselves can be arranged into subsystems or subspaces with autonomous governance and internal hierarchical management. A data management framework for IoT is presented that incorporates a layered, data-centric, and federated paradigm to join the independent IoT subsystems in an adaptable, flexible, and seamless data network. In the context of IoT, data management systems must summarize data online while providing storage, logging, and auditing facilities for offline analysis. This expands the concept of data management from offline storage, query processing, and transaction management operations into online-offline communication/storage dual operations.

(STRATEGIES FOR ENSURING THAT QUALITY,VERIFIED DATA IS BEING FED INTO THE DATA WAREHOUSE)

Data Warehouse is collection of data, but what if the data stored is not appropriate in quality? To ensure that data stored in the warehouse meets all the criteria of being quality wise appropriate several strategies are taken. The data is first cleaned to remove the noisy,inconsistent and missing values. It is done by clustering,outlier analysis, binning method. It then undergoes Integration with the help of various methods such as chi-square test for maintaining the data integrity and consistency. Then, the data is Transformed into a suitable form so as to store it into the data warehouse. To transform the data normalisation can be used. Further more, data is reduced to save space and maintain the organisational requirement. This can be done by Cube Aggregation, concept hierarcy, histogram representation so to present the data appropriately to the user. Maintaining quality is essential so to provide good and right data to the user and organisation as well as to maintain the goodwill in the society and eyes of user.

(SECURING THE DATA WAREHOUSE)

Since data warehouse contains large amount of data essential to the user, it becomes important that one must secure this data to avoid misuse. To maintain the security of data warehouse transparent data encryption can be one way which enables encryption of sensitive data in database columns as the data is stored in the operating system files. Data aggregation can be done to view user only the required data and hiding many important and confidential information. Authentication must be done before any user access the data.Backups,Logs and Auditing must be maintained to ensure security. Proper datamart should be maintained for the data inside the data warehouse. All these are some of the essential technique by which one can maintain the security of data warehouse.

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