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Database management systems

Database management systems

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Professor

Institution

Course

Date

Database management systems

Q#1)What are the advantages and disadvantages of implementing distributed and centralized databases? Does the size and location of the organization dictate if the database is centralized or distributed? Why?

Distributed database is a collection of data which are connected in a system through a simple logical network. They are managed through a distributed database management system in a single network. In the system, each part has its own dat6abse and an operating system. On the other Hand, a centralized database management has all the data stored in a single place fined as collection of logically distributed database which are connected with each other through a network.

The advantages of a centralized database are that all the data can be mined from the same network, this enhance the efficiency of the system and data recovery. This is also advantageous as the systems can be maintained from a central place in well contained manner. Centralized database is limited to those at central place, any downtime in the system leads to lose as the system is completely locked down

The distributed database is advantageous because the data can be accesses by geographically distributed clients without slowing down the system. This system is also advantageous because of the location transparency (Korth, & Silberschatz, 1998).

The size and location of the organization does not determine if the database is distributed because distribution of the database depends on the storage facilities and the computers in use. In a distributed system the remote databases have their own local autonomies over their data as they can take responsibility for the security of the data, the backup and recovery. They also control the concurrency of the database as well.

Q#2) what are some of the important considerations regarding DBMS/Database design? Explain why these considerations are important.

1) Security – DBMS is a very important part of any setting that takes care of the data stored in the database, security of the system should be given the highest priority. This does NOT only apply to the data in the data base but also to the [possible breaches. Therefore security of the dbms also concerns the hardware, software, people, and data. THIS requires the implementation of a superior mission objectives for the whole debase management systems. This increasing interest in the security of then database is as results of the reliance on the computer for the storage of data. The main concepts in ensuring the security of a DBMS include authorization, Views, backup and recovery, integrity, encryption and RAID (Elmasri, &Navathe, 2004).

2) Transaction rate: the kind of traffics to the data base. A debase is supposed to be scalable to allow for future un-predetermined database accesses. This will ensure that the number of systems downtimes is reduced and the throughput is increased thereby enabling the organization to realize their database design objectives. The designer should know whether the system is read only or both. Determination of the usage patterns also helps in determining if the system can work at peak times or even if the usage patterns are evenly distributed

3) The nature of the uptime- uptimes is also another important factor to consider as it determines the number of downtimes s set is supposed to register to [prepare for the possibilities of failures. The uptimes range from the normal 16×5 and the 24×7. But it is imperative to know that the 24×7 does not allow for downtimes when then systems can be maintained

4) The nature of database administration- according to Elmasri, &Navathe, (2004), it is important to consider the way the database will be administered, as it helps in reducing the number of system downtimes when administering the database. If the administration is to be carried every day at a central point or if the administrations to be carried at different remote points. DB administration may include backups, transfers to other systems, systems integrations, data loading etc are much more important to consider especially if the database is used for commercial purposes (Korth, &Silberschatz, 1998).

Q#3) Does MS Access support all nine DBMS functions identified listed in chapter service? Why or Why not? Describe how MS Access implements these functions.

Teorey, Lightsome &, Nadeau, (2006), states that the main functions of database management systems are: indexing, Views, Security, Integrity, Concurrency, Backup/Recovery, Design, Documentation and Update/Query. Ms Access can support a number of this function, but cannot support some functions due to incompatibility between the systems. For example, access has a number of query performance problems based on the resource mamanement systems used in the latest version of the databases. This was dues to the incompatibility and causes delays in returning the delays. However, Ms Access uses the hot fixes KB2553029 to speed up the query performance. On the other hand, ms access does not function in a number of operating systems like the Mac Os, Linux, BSD, and Amiga etc. this is due to the configuration systems used in the systems (Raghu, & Gehrke, 2000).

There are a number of functionalities such as connectivity and interactions that the ms access does not support. The ms access is a native PHP; it is therefore not easy for their connectivity to offer high indexing functionality and query / update. Ms can only achieve this by using the open database management system for direct access. Then reason form thins is that ms Access is a low end natively DBMS with a poor middle-tier PHP script. Ms can also reverse engineer the script to work sufficiently.

Ms Access has a limited support for the storage functionality. This is achieved but importing database for Ms Access jet 4. By conecting ms access to live databases through the ODBC, one can easily imports the database schemas into any standard UML model. This also enhances the level of synchronization between the data models and the live databases (Teorey, Lightsome, & Nadeau, 2006).

References

Elmasri, R. Navathe, S. (2004). Fundamentals of Database Systems. 4th ed., Pearson Addison Wesley,

Korth, H.; Silberschatz. (1998). Database Systems. Third Edition. Makron Books.

Raghu R., & J. Gehrke., (2000). Database Management Systems, Second Edition, McGraw-Hill,

Teorey, T.J., Lighstone S., Nadeau, (2006). Database Modeling and Design, 4th. ed., Morgan Kaufmann Publishers, Inc, San Francisco

Database Design Database Architecture

Database Design: Database Architecture

A database is defined as an organized collection of data. Today most Database Management Systems use different database system architectures such as centralized and client-server systems, server system architectures, parallel systems, distributed systems and many others for system implementation. However, in this case, the database architecture being used for my subsystem is a three-level architecture that has external level, internal level and conceptual level (Hellerstein, Stonebraker & Hamilton, 2007).

In physical database, the users’ view of the data is defined. My subsystem is a customer relationship system (CRM) where customers interact with the system to purchase goods online. Conversely, the managers also interact with the system to manage the employees (Hopper, 2008). The following is an entity-relationship diagram (ERDs) of the subsystem for CRM. In the diagram;

Manager manages Employees

Manager manages Customers

Manager control Goods

Customer orders goods from website

Customer signs up in a website

5172710192405Goods name

Goods name

17081513970Employee name name

Employee name name

-914400170180Employee ID no.

Employee ID no.

1028700169545Managers ID no.

Managers ID no.

2723515163195Managers Name

Managers Name

3676650150495Controls

Controls

5229225215900Goods

Goods

646430215900Manages

Manages

-590550255270Employees

Employees

21443958255Manager

Manager

546735087630Goods no.

Goods no.

1885950200025Customer ID

No.

Customer ID

No.

715010200025Customers

Customers

3781425120015Warehouse

Warehouse

-208915103505Custome Name

Custome Name

The below diagram is a use case Diagram representation for the subsystem to be implemented. Use case diagram is the view of system that looks on the behavior of the system as it appears to external viewers.

Database architecture is essential for system implementation in that it maintains a catalogue of schemas, users as well as, applications. Database architecture also allow for the storage, retrieval along with the manipulation of data. The other significance include; authorization and security, integrity and consistency services as well as, promotion of Data interdependence (Connelly, 2001).

References

HYPERLINK “http://www.bibme.org/” o “Edit this item” Connelly, J. (2001). Architecture Database on DIALOG: a user’s manual. London: British Architectural Library, Royal Institute of British Architects.

HYPERLINK “http://www.bibme.org/” o “Edit this item” Hellerstein, J. M., Stonebraker, M., & Hamilton, J. (2007). Architecture of a database system. London: Now.

HYPERLINK “http://www.bibme.org/” o “Edit this item” Hopper, T. (2008). Distributed relational database architecture: connectivity guide (4th ed.). New York: Prentice Hall PTR.

Database design and development

Database design and development – Computing

Student name

Institution

Physical Design Considerations

In the setup of information given in the system, it is clear that the numbers of different relations mostly the one-to-many relationships are eight in number while some of them like the case of 2 are taken as the reference entities in this case and these are the cases of PB_vet and the PB_visit. This concept clearly shows that one-to-many relationship can be implemented in this database design to come up with an appropriate solution to solve the company’s problem.

The different types of processes involved in the database development with respect to the company is like the case of the horizontal partitioning criteria and also the case of vertical partitioning technique. They cannot be within the application that is intended to be constructed since the feasibility of their use does not exist within the system.

Processes

Enter, modify or delete clients

Indexing

List clients(clientID, lastname, firstname, phone_number) this is by using the firstname within the lastname basis.

The function is performed in order to ensure that there is improved means of getting the information form the database without necessarily having to perform the sorting of each member available in the database. Despite all these, still it is clear that the process will be slowed down than ever.Despite the benefits of the process the indexing will be important in this context and will not be used in this case.

A client who owns only deceased animals can be removed

This action is to be performed in order to enable the performance to be improved simply by placing the index entity on the description of the client’s details.This also implies that the sorting out of the elements will not be required within the database management. The demerit of this method is that it makes the system to be generally slow in operation as compared to the other system. Also the indexing will not be that important in this case since the process will not be that of assistance at all since the application does not support any thing in reference to indexing.

A client who owns no animals can be removed.

This query is to be run in order to get the correct number of clients who do not own any animal. This allows the user to be able to delete the client who does not have any animal can be removed from the list or the data in the application.

A client with a credit status of bad cannot be removed.

This portion of the system enables the user to know the status of the client with the credit status of bad and gets a recommendation not to remove them from the database. This enables the user to know the correct number of clients who owns the animals and those that does not have any. This reduces the trend of sorting which is believed to be time wasting and uses many resources in the process.

Enter, modify or delete veterinarians.

Indexing

List veterinarians by first name within last name (all fields to be shown)

On this case the process of indexing can be of great importance since it enable the number of veterinarians be gotten and their time of visit can be sorted out easily without any problem. This query helps in getting the number of veterinarians who pays visit to the clinic to administer the respective treatment to the animals. Their ID is also very paramount and can be used in retrieving the information concerning their performance in the firm. It reduces the issue of the sorting that could have taken some times to come up with the solution. Most importantly again, it would be in order to get the information about their level of knowledge of the kind of diseases they deal with.

Enter, modify or delete clients’ visits.

Indexing

This query also is very important in that it enable one to be able to get more information concerning the client that needs to be deleted from the system. This helps in avoiding the number of the client that still in the clients within the system and removes them without the retaining them which at the end creates the redundancy of the data within the system.

List animals by name (animal id, name, gender, name of type plus id, last name, first name and credit status of client) – only show animals whose client has a credit status of ‘good’.

This query helps in getting the information concerning the various types of the animals their respective gender of the animals and also tend to reduce the level of the redundancy within the system.

Physical Design

2.1 SQL to implement the operational business rules

Enter, modify or delete clients

List clients by first name within last name (all fields to be shown select ClientID, FirstName, LastName, StreetAddress, Suburb, City, Phone, CreditStatus,

Order by lastname, firstname;

A client who owns only deceased animals can be removed SelectclientID, AnimalID, Name, Gender, Deceased,Breed, DateOfBirth, AnimalTypeID*, ClientID*Description,

Order by Description;

A client who owns no animals can be removed. selectclientID, lastname, firstname, AnimalTypeID, Name;

A client with a credit status of bad cannot be removed. select clientID, lastname, firstname, status(bad/good);

Enter, modify or delete veterinarians.

List veterinarians by first name within last name (all fields to be shown) select veterinarianID, firstname, lastname;

Enter, modify or delete animals

List clients by first name within last name (id, last name, first name and street address to be shown) Select clientID, lastname, firstname,

Order by street address;

List animals by name (all fields to be shown from animal plus id, last name, first name of client Select animalID, lastname, firstnameOfclient;

List animal types by name (all fields to be shown) Select AnimalID, Name, Gender, Deceased,Breed, DateOfBirth, AnimalTypeID*, ClientID*);

Animals that have current visits cannot be removed

Select animalID, status (visits/no visits),

Order by status_of_visit;

Enter, modify or delete clients’ visits.

List animals by name (animal id, name, gender, name of type plus id, last name, first name and credit status of client) – only show animals whose client has a credit status of ‘good’. Select animalID, name, gender, nameOfType_ID,lastname,firstname, CreditStatus(‘G’ for Good OR ‘B’ for Bad

),

Order by lastname;

List veterinarians by first name within last name (all fields to be shown).

Select veteranianID, lastname, firstname;

List visits (visit ID, visit date, status, animal ID, animal name, client ID, client last name, client first name, veterinarian ID, veterinarian last name, and veterinarian first name) by visit date.

Select VisitlD, VisitDate, Status AnimalID, VetID, PreviousVisitID)

Order by visit date;

When a record of the visit is created the visit is recorded as ‘current’ Select visit_record, status_of_record(‘current’)

Order by visit;

Only ‘paid’ visits that are over 3 months old can be deleted Select visitID, visit_duration, visit status (‘paid’/ ‘not paid’),

Order by paid;

Visits with a status of ‘current’ cannot be deleted Select visitID, visit_status (‘current’),

Order by visit_status;

A ‘paid’ visit that is linked to other visits cannot be deleted Select visitID, visit_status(‘paid’/’not paid’),

Order by visit_status;

Add or remove treatments/medication to or from visits.

List treatment type by description (all fields to be shown) Select (TreatmentID, Description, Cost);

List medication type by description (all fields to be shown). Select MedicationID, Description, Cost;

List visits (visit ID, visit date, status, animal ID, animal name, veterinarian ID, veterinarian last name, and veterinarian first name) by visit date.

Select visitID,visit_date, status, animalID, animal_name, veterinarianID, veterinarian_last name, veterinarian_first name,

Order by visit_date;

Treatments and medications can only be entered against ‘current’ visits Select treatmentID, visit_status,

Order by visit status(‘curent’);

Mark a visit as ‘paid’.

List visits (visit ID, visit date, status, animal ID, animal name, client ID, client last name, and client first name) by visit date

Select visitsID, status, animalID, animal_name, clientID, client_lastname, client_firstname,

Order by visit_date;

When an invoice has been paid, the corresponding visit is marked as ‘paid’ and all associated medications and treatments are deleted.

Select invoiceID, clientID, visitID, lastname,firstname, current_status(‘paid’/’not paid’);

Only current visits can be updated to ‘paid’ Select visitID, current_status(‘paid’/’not paid’);

A ‘paid’ visit cannot be updated to current Select visitID, current_status(‘paid’/’not paid’);

All veterinarians and paid visits

Show the id and full name of every veterinarian that has ‘paid’ visits – sort by veterinarian first name within veterinarian last name

Select VisitID, TreatmentID, VetID, FirstName, LastName, MobilePhone, Fee;

Order by vet_firstname;

Invoice for one selected animal’s visit.

List visits (visit ID, visit date, status, animal ID, animal name, client ID, client last name, and client first name) by visit date of all the ‘current’ visits.

SelectvisitID, visit_date, status, animalID, animal name, client ID, client last name, and client first name,

Order by visit date;

For the selected visit show the client’s id, full name, street address, suburb and city, also show the visit id and the animal’s name Select animalID,lastname, firstname, fee_status, vetID, visitID;

Select vetID, medicationID, cost, description,

Order by DESCRIPTION;

Data Usage Maps

2257425405765PB_VISIT

00PB_VISIT

209550289560PB_CLIENT

00PB_CLIENT

2276475289560PB_ANIMAL

00PB_ANIMAL

4181475222885PB_ANIMAL

TYPE

00PB_ANIMAL

TYPE

Business function (process) to physical design technique matrix

One-to-One

relationship Associative entity with non-key attributes One-to-many

relationship with reference data Duplication Horizontal

Partitioning Vertical

Partitioning Indexing

Enter, modify or delete clients No No No No No No Yes

Enter, modify or delete veterinarians. No No Yes No No Yes Yes

Enter, modify or delete animals No No Yes No Yes Yes Yes

Enter, modify or delete clients’ visits. No No No No No No Yes

Add or remove treatments/medication to or from visits. No No No No No No Yes

Mark a visit as ‘paid’. No No No No No No Yes

All veterinarians and paid visits No No Yes Yes Yes No Yes

Invoice for one selected animal’s visit. Yes Yes No Yes Yes Yes Yes

For the database given, it is clearly evident that one-to-one normalization proficiency should be usedsince in the given database, the one to one relationship is evident in the case of the as there are no one-to-one relationships in this database.

The associative entity normalization technique cannot be used as there are no associative entities in this database.

The vertical partitioning cannot be used because there are no queries that access mutually exclusive fields.

Final ERD with relations (and on-delete actions)

4352925140335PB_

MEDICATION

00PB_

MEDICATION

Paws and Beaks ERD

2028825200025PB_

TREATMENT

00PB_

TREATMENT

-95250247650PB_VISIT

TREATMENT

00PB_VISIT

TREATMENT

4657725437515PB_VISIT

MEDICATION

00PB_VISIT

MEDICATION

026670PB_VET

00PB_VET

191452583820PB_VISIT

00PB_VISIT

2028825157480PB_ANIMAL

00PB_ANIMAL

4124325243205PB_ANIMAL

TYPE

00PB_ANIMAL

TYPE

-95250290830PB_CLIENT

00PB_CLIENT

2.2 Data Volume Map

The data volume shown below shows the number of each entity as shown in the ERD diagram and their possible changes that are inevitable in this case. It enable the user to be able to know the number of data that can be fed in the system within a given particular time span and also ensure that they maintain the number in order to avoid any form of integrity constraints within the system.

3952240197485PB_

MEDICATION100

00PB_

MEDICATION100

185674043815PB_

TREATMENT5

00PB_

TREATMENT5

-22860043815PB_VISIT

TREATMENT

3

00PB_VISIT

TREATMENT

3

3648075232410PB_VISIT

MEDICATION200

00PB_VISIT

MEDICATION200

212090377190PB_VET10

0PB_VET10

1704975434340PB_VISIT20

00PB_VISIT20

1704975935990PB_ANIMAL25

00PB_ANIMAL25

-228600935990PB_CLIENT

500

00PB_CLIENT

500

4038600840740PB_ANIMAL

TYPE40

00PB_ANIMAL

TYPE40

Data Dictionary

Referential Integrity Constraints

A client with animals cannot be deleted from the database.

An animal type with animals ID cannot be deleted also from the database.

An animal with visits cannot be deleted directly from the database.

A visit with medication allocated cannot be deleted

A visit with treatment allocated cannot be deleted.

A visit related to a previous visit will have its previous visit ID set to null if that previous visit is deleted.

A veterinarian with visits cannot be deleted

A treatment assigned to a visit cannot be deleted.

A medication assigned to a visit cannot be deleted also from the database.

Reference

Frost, R., Day, J. C., & Van, S. C. (2006). Database design and development: A visual approach. Upper Saddle River, New Jersey: Pearson Education.

Siau, K. (2007). Contemporary issues in database design and information systems development. Hershey, PA: IGI Pub.

Ponniah, P. (2003). Database design and development: An essential guide for IT professionals. Piscataway, NJ?: Hoboken, NJ.

Database And Data Warehousing Design

Database And Data Warehousing Design

Factors supporting the need for a data warehouse

Due to the enormous amount data from different sources, the company needs a system to help if leverage its data so as to obtain the desired outcome. In order to maintain competitive advantage in the market, the company needs to use its information as an asset to make decisions. To use the information, the company needs a means of gathering, storage, and analyzing the data. The development and implementation of the data warehouse will provide the following benefits:

The data warehouse will be able to handle the increasing data that the company is receiving. In addition, the data warehouse will be able to process data from a variety of sources containing both structured and unstructured data. Thus this will facilitate the need to develop a data warehouse.

The data warehouse will be aligned to the strategic objectives of the company. Therefore, data processing will be carried out according to the specific needs of the company. The need for a system that will be aligned to the strategic vision of the company will therefore facilitate the need to develop a data warehouse.

The data warehouse will provide the company with a secure platform which can be used by the staff to perform their activities. Thus, this will facilitate the need for the company to develop a data warehouse.

The data warehouse will enable collection of data from all the departments of the company into a central location. This will allow comprehensive processing of data in one central location. The results of data processing will reflect the performance of the entire company and not a specific department. This will enable quick and quality decision making which the company needs. Thus, there is the need to develop a data warehouse.

The data warehouse will be constructed using standard interfaces which will allow interoperability between systems. This will facilitate outsourcing some of the activities such as data backup into other companies like cloud service providers. Thus, this facilitates the need to develop a data warehouse (March & Hevner, 2007).

Best practices that the company should follow

There are various best practices which must be followed by the company in order to develop a data warehouse that will meet the requirements of all the stakeholders.

The data warehouse should comply with the Information Technology (IT) requirements. These include development of standard interfaces and data security requirements. The data warehouse should meet all the legal requirements of the industry.

The data warehouse should be treated as an information asset of the company. Therefore, the data warehouse should be aligned to the strategic vision of the company. There should be a thorough analysis of all the requirements of the company before development of the warehouse. In addition, all the stakeholders of the data warehouse need to be comprehensively involved. This will ensure that the data warehouse will meet all the requirements.

The data warehouse should not be developed at once (Dung, Rahayu & Taniar, 2007). The best practice is to construct the data warehouse in phases so as to ensure that all the requirements are fully satisfied at each stage. In addition, carrying out the development in staged will allow the flexibility to accommodate any necessary changes which may be required.

The other best practice is to ensure that the data warehouse is usable. Usability is an important aspect of any information system. It will be a great loss to the company to construct an appropriate data warehouse which is not usable to the users of the company. Poor usability means that the employees of the company find it difficult to learn and use the system optimally. There, the data warehouse should be usable to ensure that it will be highly productive.

The other best practice is associated with accessibility. The data warehouse should be accessible to all the users of the company as required. The data warehouse should be used as a center for unlimited access of information to its users. However, access to information should be controlled to ensure that only authorized people can access the information (Jukic, 2006). It is important to have the data warehouse administrator who will be responsible for controlling access to the warehouse. The users of the data warehouse need to be authenticated by use of authentication mechanisms such as use of a username and a password.

Schema supporting the company’s business and processes

In order to develop a suitable schema that will support the business and operations of the company, the following assumptions will be made. The first assumption is that the company operates in the health care sector. The company collects patient and hospital data for storage and analysis. The company collects patient and hospital data from its various branches in the country. The Extraction-transformation-loading model will be used. The diagram below indicates a star data schema which shows how the data warehouse will monitor hospital and patient information between two of its branches. The star schema shown below is made up of one fact table and a four-dimensional table.

Figure SEQ Figure * ARABIC 1: Star Schema for the Warehouse

Appendix

Figure SEQ Figure * ARABIC 2: Flow of data in the data warehouse

Figure SEQ Figure * ARABIC 3: Entity Relationship Diagram

Figure SEQ Figure * ARABIC 4: Patient registration data flow diagram

References

Dung, X. T., Rahayu, W., & Taniar, D. (2007). A high performance integrated web data warehousing. Cluster Computing, 10(1), 95-109.

Jukic, N. (2006). Modeling strategies and alternatives for data warehousing projects. Communications of the ACM, 49(4), 83-88.

March, S. T., & Hevner, A. R. (2007). Integrated decision support systems: A data warehousing perspective. Decision Support Systems, 43(3), 1031-1043.

Data Transformation Method

Data Transformation: Remote Sensing

Contents

TOC o “1-3” h z u HYPERLINK l “_Toc323462844” CHAPTER TWO PAGEREF _Toc323462844 h 2

HYPERLINK l “_Toc323462845” LITERATURE REVIEW PAGEREF _Toc323462845 h 2

HYPERLINK l “_Toc323462846” Introduction PAGEREF _Toc323462846 h 2

HYPERLINK l “_Toc323462847” Mapping and Classification of Forest Species PAGEREF _Toc323462847 h 3

HYPERLINK l “_Toc323462848” Continuum-Removed Absorption PAGEREF _Toc323462848 h 4

HYPERLINK l “_Toc323462849” Data Transformation through Discrete Wavelet PAGEREF _Toc323462849 h 8

HYPERLINK l “_Toc323462850” Data Transformation through First derivative PAGEREF _Toc323462850 h 10

HYPERLINK l “_Toc323462851” Conclusion PAGEREF _Toc323462851 h 11

HYPERLINK l “_Toc323462852” Bibliography PAGEREF _Toc323462852 h 14

CHAPTER TWOLITERATURE REVIEWIntroductionRemote sensing methods have found their application in evaluating the absorption of foliar macro elements for various grasses and plants even though it has mostly been done under laboratory conditions, which are generally controlled in nature. The increased use of remote sensing in management of forest and natural resources is as a result of substantial advancements in spectral resolutions coupled with advancement in data processing techniques over recent years. These have enabled production or generation of meticulous maps for cataloging forest communities, specific plant species, groups and sub-groups of species, in addition to forest species can be generated to provide a better source of information for an array of administrative resolutions and environmental applications.

Nevertheless, remote sensing is a vital technique that helps in boosting the understanding of flora and fauna in terms of feeding patterns and other living patterns. It is therefore imperative that the quality of data collected be of high quality to improve the quality of judgment from the information gathered. Therefore, in order to improve on discrimination and classification of forest species, it is essential that the data acquired via remote sensing be as helpful and high quality as possible. Data transformation is one of the techniques that have been used to remove the noise contained in the hyperspectral reflectance data to obtain accurate measurements of biochemicals and macronutrients in the forest species. In light of this, this literature review focuses on three data transformation methods that help in noise removal in the hyperspectral data obtained through remote sensing of Eucalyptus. The methods focused on are Continuum removal, discrete wavelet and First derivative.

Mapping and Classification of Forest SpeciesMapping and classification of the spatial distribution of individual Eucalyptus species or any other forest species is a significant ecological subject that calls for sustained study to correspond with developments in remote sensing equipments. The use of high spatial resolution (80cm) or hyperspectral remote sensing imager data has been scrutinized and examined by various studies for mapping tree biophysical aspects in different places around the world. For instance, Goodwin et al (2010) conducted a study in which they used hyperspectral data from the Compact Airborne Spectrographic Imager 2 (CASI-2) for discrimination, classification and development of appropriate mapping for the Australian eucalypt forest. In the study, CASI-2 provided an operative dataset to enable effectual discrimination and allow for generation of maps for spectrally multifaceted species thereby allowing for successful sub-genus grouping. The use of high spatial resolution dataset enabled proper discrimination of individual tree crowns of the Eucalyptus their crown aspects like sunlit aspects and shaded aspects.

On the other hand, spectral data from ten narrow bands ranging from 400nm to 700 nm in the visible range and data from ranges from 700nm to 1300nm within the near-infrared wavelengths provided comprehensive data for thin foliage absorption and reflective aspects (for instance, the green reflectance climax at 550 nm). The findings of the study indicated that economically significant Syncarpia glomulifera (Turpentine) can be successfully discriminated within communities that have assorted species. While the study employed a multi-stage analysis, strong spectral resemblance were exhibited by the foremost two phases of analysis for specific eucalypt tree species that were once more replicated by low CASI-2 categorization accuracies. Spatial resolution relates to the size of the pixel and the acquisition of higher spatial resolutions not only bolsters the ability to detect targets besides allowing the assessment of spatial correlations between pixels within particular tree crowns (Franklin et al. 2000). In contrast, Spectral resolution indicates the bandwidth response for a specific band. Constricted spectral bands (<10nm) can intensify the number of bands documented for a given optical region and help in targeting particular absorption attributes such as chlorophyll absorption of the plant leaves.

Continuum-Removed AbsorptionVarious studies have established that continuum-removed absorption is a useful data transformation method that can be used to compare the predicted measurements of mineral distribution against the mapping band shapes of the remotely sensed data (e.g. Roberts et al 2011; Datt 2000; Muya and Oguge 2000). Kokaly and Clark (1999) conducted a study in which they applied the continuum-removed absorption method alongside a refined method of band depth analysis of biochemical absorption features in studying dried plant material of Eucalyptus species and found that when these two methods are used three problems are overcome. In a follow-up study by Kokaly (2001), the study found that this method can appropriately be used in vegetation science.

Since the remote sensing of macronutrients helps in determination of plant quality in terms of determination of plant growth and development or health status, studies have employed the continuum-removed absorption method to help in understanding the plant quality of Eucalyptus especially in the tropical rangelands (e.g. Datt 2000). In their study, Muya and Oguge (2000) found that the use of continuum-removal minimizes the problem of over-fitting when near-infrared spectroscopy laboratory methods are used to estimate macronutrients at the canopy level of the tropical rainforests.

First, the problem of inconsistency that has been noted when methods such as multiple linear regression analysis are used across different vegetation types. In addition, these regression methods suffer problems of over-fitting and when the number of wavebands used is more than the samples, there is a higher likelihood of getting higher spectral variability (Kokaly & Clark 1999). Since spectral variability is independent of biochemical concentration, spectral variability is another problem that is solved when continuum removal is used in data transformation given that by using continuum removal the known chemical absorption features of the Eucalyptus are standardized (Zhang et al 2006).

Another problem that necessitates noise removal in the spectral data is that when dealing with canopies in large forest species, water that may be present in the canopies masks absorption features thereby complicating the remote sensing of the biochemicals more so at the field level (Cheng et al 2011). This problem becomes worse when soil background features together with atmospheric absorption effects are considered. Thus, the use of continuum removal standardizes the data by overcoming these problems and removing undesirable noise from the spectral data. When Kokaly and Clark (1999) used the band depth analysis with continuum removal in their study, they established that there is a strong correlation between nitrogen concentration and absorption (r2 = 0.95). They used dried plant material of Eucalyptus sub-genera. In another laboratory experiment, Curran et al., (2001) applied the methodology used by Kokaly and Clark on 12 macronutrients and achieved higher accuracy. It is however notable that most studies have been conducted under laboratory conditions rather than field level. In addition, not many studies have aimed at exploring or estimating foliar nutrient status of certain nutrients such as calcium, potassium, and magnesium.

The following diagram shows a graph of reflectance plotted against wavelength.

Figure SEQ Figure * ARABIC 1: Mean canopy spectrum and whole fresh leaf spectrum.

In the diagram, HYMAP 3-m spatial resolution data for 60 mature eucalypt trees was obtained. The researchers, Huang et al (2004) were interested in getting comparatively pure spectrum hence they located each of the individual eucalypt using the false color image of the HYMAP. They achieved this via field inspection. Since the sensor receives reflectance from both vertical mixture of the foreground and the background, separating tree pixels from the adjacent pixels is considerably easy given that tree pixels are different color to the background (Huang et al 2004). When the researchers applied the continuum removal data transform method, they obtained the following spectral profile.

Figure SEQ Figure * ARABIC 2: Continuum Removed Spectral Profile of eucalypt

For the study, Huang et al (2004) used the continuum removal method calculated as the band depth normalized as a ratio of the band depth at the centre of the absorption aspect and they used the following formula:

Where:

R is the reflectance at the waveband under consideration Ri represents the reflectance of the continuum line at the waveband being considered, Rc represents the reflectance at the centre of the absorption feature and Ric represents the reflectance of the continuum line at the center of the absorption feature.

In another study, Mutanga and Skidmore (2003) carried out a study in which they aimed at developing further and extending the band depth analysis method to estimate the concentration of the above macronutrients. The researchers enhanced the accuracy and validity of their study by combining the short wave infrared absorption effects that had earlier been used by Kokaly and Clark with two other key absorption features situated in the visible region. The effect of water is minimal in this region. Mutanga and Skidmore (2003) further developed and tested a modified first derivative reflectance approach to enhance the objective of continuum removal in data transformation.

Data Transformation through Discrete WaveletWavelet transform has in recent times become a very popular data transformation method when it comes to analysis, noise removal and compression of signals and images. Various research studies have been carried out exploring the latent benefits of combining active and passive remotely sensed data for assessment of forest structures (e.g. Banskota et al 2011). Image fusion has been applied as a way maintaining the incongruent data features that might be relevant to mapping of the forest structures under consideration. In the study carried out by Jan et al (2011), Eucalyptus plantations in the midlands of South Africa were studied using the near-infrared and the visible bands of ASTER and a fine beam Radarsat-1 images. ASTER is the Advanced Spaceborne Thermal Emission and Reflection Radiometer. The researchers obtained the data and modified it using the discrete wavelet transformation. In addition, the researchers obtained spatially documented data sets for the 38 plantations for the sake of comparisons between the measured data and the referenced data. In order to test whether fused data sets could produce better statistical models, the researchers applied ordinary least squares regression and multiple regression analyses to obtain empirical relationships. In their findings, it was established that single bands from both data sets did not provide sufficient adeptness for modeling basal area or even merchantable volume of timber. The adjusted R2 produced values that ranged below 0.3. when they used an optimized multiple regression, they got improved results in terms of mean and standard deviation when they compared the results to those generated from single bands (also in Zhang et al 2006). Nevertheless, these were still found to be unsuitable for application or mapping of forest species (Gong et al 2001). Williams and Amaratunga (1995) used Discrete Wavelet transform in their study and obtained the following results after data transformation.

Figure SEQ Figure * ARABIC 3: Discrete Wavelet Transform using Low Pass (a) and High Pass (b) (Williams & Amaratunga 1995)

Studies have shown that since more vigorous statistical methods are requisite for investigating phenological time series due to their characteristic of being noisy and non-stationary, wavelet transform analytic methods have been found to handle such data easily (e.g. Zhang et al 2006). Hudson et al (2011) conducted a study in which they delved to characterize flowering of eucalypt and the climate influences this flowering. In the study, the researchers used wavelet transform to remove noise from the remote sensing data. They used maximal overlap discrete wavelet transform to analyze the flowering records of four Eucalyptus subgenera. The flowering records were for the period between 1940 and 1970 and identified four sub-constituents in each flowering sequence. The subcomponents were the non-flowering phase, the duration cycle, the annual cycle and intensity cycle. A diminishing overall tendency in flowering was recognized by the maximal overlap discrete wavelet transform when the series were smoothed. Similar results were achieved by Cheng et al (2011), in which the researchers observed that wavelet correlation found the same simultaneous effects of climate on flowering for all the four Eucalyptus subgenera. When the researchers carried a wavelet cross-correlation analysis, they found that rainfall and temperature have a cyclical effect on the peak flowering intensity of Eucalyptus (P < 0.0001). For every species of Eucalyptus, there are 6 months of the yearly cycle in which any particular climate variable affects flowering intensity positively. In the same cycle, there are 6 months that any specific climate variable influences flowering peak negatively. The study established that for all the Eucalyptus species, rainfall wields a negative impact as long as the temperature is positive.

In another study, Curran et al (1995) used wavelet data transform method to investigate the relationship between reflectance of near infrared or visible beam and the chlorophyll content in Eucalyptus leaves. In the study, the reflectance properties of near infrared and the visible beam for the leaves of several Eucalyptus were analyzed to establish suitable indicators for remotely sensing the chlorophyll content in the leaves (Hudson et al 2011). The study employed the use of a scatter correction method to the reflectance spectra to decrease the additive and multiplicative scattering consequences of foliar surface and interior structure. The study further established that with an improved calibration of the chlorophyll content, reflectance near 710nm wavelength demonstrated greatest response to chlorophyll content. Moreover, reflectance near 550nm showed a less sensitivity to chlorophyll content in the Eucalyptus leaves. Generally, there are two levels of discrete wavelet transform that can be used to transform hyperspectral data (Hudson et al 2011). There is the single level technique and the multiple level decomposition technique.

Data Transformation through First derivativeVisible and near infrared areas of spectrum frequently exhibit spectral differences, which are often used to describe various vegetation classes. Reflectance spectrum and first derivative spectrum that stretch from 350 to 700 nm are used to enhance the shape disparities between the spectral signatures for every tree species used or included in a sample. By precisely encapsulating these spectral differences, it is possible to improve vegetation classification by allowing for investigation of band ratios and vegetation indices. In the study done by Datt (2000), the best performing reflectance index ratio was the (R850-R710)/ (R850-R680) and hence it was proposed as the new index for estimating chlorophyll content in higher plants such eucalypt.

While Huang et al (2004) conducted a study in which they employed continuum removal to transform the hyperspectral data for the eucalypt tree, they also used standard derivative method to transform the same data and compare the outcomes. However, the researchers used the standard derivative data transform method to estimate nitrogen concentration in the eucalypt trees. They used the standard derivative method to transform the log (1/R) spectra data where R is the reflectance at the waveband under consideration. In order to reach the best possible combination for the average spectra as well as the maximum spectra, the researchers tried various scatter correction methods. They however established that Modified Partial Least Squares method resulted to a higher coefficient of determination when maximum spectra were used as compared to when the mean spectra were used. The results of the study by Huang et al (2004) agree with the findings by Mutanga and Skidmore (2003) where the latter carried out a study to investigate the correlation between nitrogen absorption and the chlorophyll level in eucalypt. For the study by Huang et al (2004), when the standard derivative method was used for nitrogen absorption in eucalypt, the following diagram illustrates the results.

Conclusion

Developing maps for spatial distribution of particular species is a significant ecological aspect that calls for sustained research to match the advances achieved in remote sensing technologies. Due to the characteristic of the hyper-spectral data of being noisy and non-stationary, it is imperative to use any of the wavelet transform analytic methods, which have been found to handle such data easily. The literature review has revealed that the of wavelet transform methods or techniques helps in removing noise from the hyperspectral data hence improve quality of the data for the sake of comparison with referenced data for individual plant species. The review has also revealed that the studies that used maximal overlap discrete wavelet transform to analyze the flowering records of forest species were effective in reducing inconsistencies and achieving increased accuracies. Out of many studies reviewed, many of them established that the use of data transform techniques for noise removal was not only a good approach to improving discrimination and classification among individual plant species but also increased discrimination and classification accuracies. The studies reviewed further show that reflectance spectrum and first derivative spectrum that stretch from 350 to 700 nm are effectively used to enhance the shape disparities between the spectral signatures for every tree species used or included in a sample. This is achieved by precisely encapsulating the spectral differences thereby improving vegetation classification by allowing for investigation of band ratios and vegetation indices.

The findings of the literature review also indicate that due to the economic viability of most of the eucalypt sub-species such as Syncarpia glomulifera (Turpentine) discrimination within communities that have mixed species can be achieved successfully using data transformation methods highlighted such as continuum removal, standard derivative and wavelet transformation. Lastly, the review has also showed that the math treatment method adopted also affects the consistency of the results hence it is important to choose method astutely. Most studies successfully used modified least squares regression method with increased accuracy.

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Curran, P. J., (1989) “Remote sensing of foliar chemistry” Remote Sensing of Environment, 30, pp. 271-278

Curran, P. J., Dungan, J. L., and Peterson, L., (2001) “Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies” Remote Sensing of Environment, 76, pp. 349-359.

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Data Mining in Marketing

Data Mining in Marketing

Name

Institution

Data mining in marketing

Marketing refers to a science and an art of investigating, developing and delivering significant value in goods and services to meet the needs of the target market at the benefits of a particular company or business (Burns, 2009). In marketing a company identifies the unfulfilled desires and needs of the consumers. It creates solutions and quantifies the dimension of the target market and business profit potentials.

This paper will be looking at ways which “Shoprite Stores” will use in implementing a data mining project. The first will be to look at the customers demographic. This will help them understand what kind of customers they have.

Shoprite Stores has looked at the target market. In this situation, the customer is the main focus of the business or company when developing their strategies and activities, compared to the product of the company. The company’s model shifts in marketing needs in that the company builds a commitment to quality of goods and service and to critically listen to the needs of the customers to determine the market requirements and how the business can solve these needs effectively and efficiently (Burns, 2009).

Customer frequency usage is also an important factor that Shoprite Stores need to consider while implement a data mining project. This will give a hint on how long particular goods or service are sort after by clients. As a result, it will be easy to decide what stock or to purchase (Burns, 2009).

After all these are done, a business must be able to evaluate and measure the success of the marketing strategy they are using. Shoprite Stores should emphasize on developing strategies that are customer centred, and which will allow them evaluate the marketing strategy.

References

Burns, N. (2009). Understanding marketing research: Introduction to data mining, 2nd edn. Philadelphia: W.B. Saunders Company

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