Thursday, November 28, 2019

Management Using Data Mining And Data Warehousing Information Technology Essay Example

Management Using Data Mining And Data Warehousing Information Technology Essay Data excavation has been used for happening utile and related information from different beginnings ; Data Warehousing has been used for hive awaying organisation informations it is merely like a shop, Knowledge direction ( KM ) has been used for pull offing the cognition. The job of big sum of informations in the organisations, it is challenge for determination shapers to command the informations ( Shaw, Subramaniam, A Tan, A ; A Welge, 2001 ) . In 1999, it was estimated that 30 % of houses informations warehouses contained greater than one trillion characters of informations ( Bransten, 1999 cited in Heinrichs, A ; A Lim, 2003 p.103 ) . Harmonizing to Shaw et Al ( 2001 ) the information repositing and knowledge direction contribute for informations excavation procedure. Data excavation techniques can be used for detecting undeveloped forms of informations signifier informations warehouses ( Nemati, Steiger, Iyer A ; Herschel, 2002 ) . Data excavation is a procedure of nontrivial extraction of implicit, antecedently unknown, and potentially utile information from databases ( Chen, Han, A ; Yu, 1996 cited in Chen, Chen A ; A Tung, 2006 p.1505 ) It is really difficult to mange and do determinations organize immense sum of informations. Organizations or even little concerns want their informations to be meaningful, manageable and optimized. Time is money and organisations do nt desire to blow their clip to seek records from different beginnings ( Han A ; Kamber 2006 ) . Sometimes directors need to take speedy determinations and it is difficult for them because related information is stored in different formats and even on different locations and organisations do nt desire to blow clip. The information size is traveling to increase and it is really difficult for a determinations shaper to pull out related information from immense sums of informations ( Han A ; Kamber 2006 ) . In this study how determination devising from immense sum of informations by utilizing informations excavation techniques ( Han A ; Kamber 2006 ) . In this study, how we extract our interesting form from database. We will write a custom essay sample on Management Using Data Mining And Data Warehousing Information Technology specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Management Using Data Mining And Data Warehousing Information Technology specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Management Using Data Mining And Data Warehousing Information Technology specifically for you FOR ONLY $16.38 $13.9/page Hire Writer The remaining of the study is divided into following subdivisions. In subdivision 2, is about background of the DSS, KM, DM, Data repositing and connexion between DSS, KM, DM, and informations warehousing. In Section 3 is a instance survey of Ghani Group of companies procurement section presented, in subdivision 4 is how informations excavation assisting to determination devising is discussed in subdivision 5 is about treatment and future work and in the last subdivision 6 is decision of the study. 2. Theory 2.1 Overview of DSS Decision devising is a really tuff and encephalon storming undertaking. It can be more tuff and negative affect non merely concern even your personal life, if you have non good information about background cognition of the undertaking. If you have good background cognition and history of any relevant determination that you have made it can be really helpful for doing determination. The construct of DSSs was introduced, from a theoretical point of position, in the late sixtiess ( Zopounidis, Doumpos A ; Matsatsinis, 1997 p.260 ) . We have needs determination support system for doing rapidly and acquiring positive consequences on our concern. What is determination support system? Harmonizing to Keen and Scott Morton s ( 1978, p 1 ) cited in ( Alenljung, 2008 ) : Decision support implies the usage of computing machines to: ( 1 ) Aid directors in their determination processes in semi structured undertakings. ( 2 ) Support, instead than replace, managerial judgement. ( 3 ) Better the effectivity of determination doing instead than its efficiency. And how is work. Harmonizing to Turban s ( 1990, p 109 ) definition cited in ( Alenljung, 2008 ) : A DSS is an synergistic, flexible, and adaptable CBIS [ Computer-Based Information System ] that utilizes determination regulations, theoretical accounts, and theoretical account bases coupled with a comprehensive database and the determination shaper s ain penetrations, taking to specific, implementable determinations in work outing jobs that would non be conformable to direction scientific discipline optimisation theoretical accounts per Se. Therefore, a DSS supports complex determination devising and increases its effectivity. A DSS is a computing machine based, user friendly, support for different determination devising at different managerial degrees, efficient and effectual system ( Alter, 1980 ; Bidgoli, 1989 ; Keen A ; Scott Morton, 1978 ; Mallach 1994 ; Marakas, 1999 ; Sprague, 1989 ; Turban et al. , 2007 cit ed in Alenljund, 2008 ) . There are different types of determination support harmonizing to Power s ( 2002 ) cited in Alenljund, 2008 classs are: Data-driven DSS Model-driven DSS Knowledge-driven DSS Document-driven DSS Communication-driven and group DSS. In this study I have chiefly concern data-driven DSS. Data-driven determination support system is assisting for analysis from the big sum of informations and informations repositing, executive information system and informations excavation are subcategories of data-driven determination support system ( Power, 2002 ; Turban et al.,2007 cited in Alenljund, 2008 ) . In the data-driven determination support system we use historical informations informations is loaded into the informations warehouse and is accessed at that place, but one time the snapshot of information is made, the informations in the warehouse does non alter ( Inmon A ; Hackathorn, 1994, p 10 cited in Alenjund, 2008 ) . And informations excavation can assist to happening concealed form and values from the informations ( Han A ; Kamber 2006 ) . Data-driven emphasizes the analysis of informations that are in big measure. This type of DSS normally data warehouses, EIS, and analytical systems ( Power, 2002 ; Turban et a l.,2007 cited in Alenljund, 2008 ) . It accesses the big sum of structured informations from companies informations and external every bit good. The users of this type of DSS are troughs, staff and providers ( Power, 2002 ) . The intent of data-driven DSS is query informations from informations ware houses and engineerings used in data-driven DSS are client waiter or we can utilize web as good ( power, 2002 ) . The primary aim of these engineerings is to do the informations accessible for direction and allow it be turned into information to back up decision-making ( Lohman et al, 2003 ) . 2.2. Overview of Data Mining in Decision support System Simply stated, informations excavation refers to pull out or excavation cognition from big sums of information. Harmonizing to Han A ; Kamber ( 2006 ) Data excavation is the procedure of pull outing concealed forms from informations. As more information is gathered, with the sum of informations duplicating every three old ages ( Lyman, 2000 ) , informations excavation is going an progressively of import tool to transform this information into cognition. Data excavation is normally used in every type of application in these yearss like fraud sensing, selling applications etc. In the information mineworker centered DM rhythm, there are five stages: communication and planning, developing hypotheses, informations readying, choosing DM tools, and measuring DM consequences, most of the descriptions of these stages can be found in the DM literature ( Berry A ; Linoff, 2000 ) . 2.2.1 Data Mining Tasks Data excavation is the procedure of seeking and analysing informations in order to happen inexplicit, but potentially utile, information ( Berry and Linoff, 2007 ) . Data excavation involves choosing, researching and patterning immense sums of informations to pull out unknown forms and the consequence of that comprehensive information from big sums of informations ( Han A ; Kamber 2006 ) . Data excavation has a statistical analysis graphical representation, regulation find, determination trees. Extracting forms from big informations sets is a information excavation undertaking. We can split informations mining undertakings into five classs ( Shaw et al, 2001 ) : Figure1: figure is taken from ( Shaw et al, 2001 ) Dependence analysis The primary type of dependence cognition is the association between sets of points stated with some minimal specified assurance ( Shaw et al, 2001 ) . This type of analysis is helpful for marketing analysis for illustration a client purchases eggs and how many opportunities are at that place he/she purchases bread excessively. Market basket analysis is another name of dependence analysis ( shaw et Al, 2001 ) Class designation We can sort clients into categories, for illustration we can do a category of one client income, often purchased points, and past purchases of that client ( Shaw et al, 2001 ) . Concept description Concept description is a technique to group clients based on their sphere cognition and the database, without any forced definitions of the groups ( Shaw et al, 2001, p.129 ) . Concept description can be used for summarisation, favoritism, or comparing of selling and client cognition ( Shaw et al, 2001 p.129 ) . Data summarisation is the procedure of deducing a characteristic sum-up of a information subset that is interesting with regard to domain cognition and the full information file ( Shaw et al, 2001 p.129 ) . Customers profile can be build utilizing grouping harmonizing to their business, income and interesting thing that he/she purchase ( Shaw et al, 2001 ) . Deviation sensing Deviation sensings are really utile for detecting the anomalousness and alterations ( Shaw et al, 2001 ) . Anomalies can be defined as those things that have uniqueness or different from the normal ( Shaw et al, 2001 ) . For illustration recognition card company happen a sudden addition in the recognition purchases of an single client ( Shaw et al, 2001 ) . It may be behavioural alteration of a client, and non needfully a fraud ( Shaw et al, 2001 ) . Therefore, verification of the alteration is made after probe and the cognition is updated ( Shaw et al, 2001 ) . Data visual image There are many package plans available for informations visual image intents for illustration graph base representation is 2-dimensionaland 3- dimensional ( Shaw et al, 2001 ) . Data visual image can be used to tie in other undertakings such as dependence analysis, category designation, concept description and divergence sensing ( Shaw et al, 2001 p.130 ) . Day by twenty-four hours informations is traveling to increase and his creates troubles for direction to take determinations utilizing old informations, for illustration a procurance section wants to buy some points, foremost of all the procurance trough checks the providers that already supplied these points so he/she cheques the which provider gave good rates, quality and supply in clip ( Shaw et al, 2001 ) . The procurement director can pull out this information by utilizing informations excavation methods ( Shaw et al, 2001 ) . 2.2.2 Knowledge Discovered from Database Figure2: The above figure is taken from informations mining book by ( Han A ; Kamber, 2001 ) . It show the stairss of cognition Discovered from Database ( KDD ) In the above figure, we understand domain larning. In selected procurance instance survey we learn all the relevant information for points purchase. We find the utile characteristics for all purchases, provider, supply goods, faulty points how many times a specific provider supply the same point and how many times same mistake is repeated. Then we apply categorization, sum up, constellating etc and eventually we present the cognition. There are four common maps in informations excavation ( Han A ; Kamber, 2001 ) : Categorization It describes and distinguishes the categories for future anticipation ( Han A ; Kamber, 2001 ) . For illustration this recognition card is fraud or non? Classify the states harmonizing to their clime ( Han A ; Kamber, 2001 ) . This provider provide the goods in clip or non etc Clustering Grouping informations into signifier of new categories ( Han A ; Kamber, 2001 ) . For illustration a provider payments, supplier purchase orders, provider citations etc. Arrested development In arrested development we find the solutions that decrease the mistakes. Association regulation It define the connexion between different points for illustration if a purchase requisition have less points so what is the provider citation and if the no of points are increase so what is the provider citation. If in purchase petitions have more than one point so what is the provider response about it alternatively a individual point. We use informations excavation for acquiring historical informations ( Han A ; Kamber, 2001 ) the chief purpose to this study to increase the determination doing power utilizing historical or old informations. Mostly companies do non utilize their old informations, ghani group of companies has same job, they do non utilize there old informations, they think that it is nonmeaningful, clip consuming, excess load and necessitate more human resources for utilizing old informations. But in existent universe historical information is meaningful, clip economy, assisting to diminishing the load, when we are roll uping informations we need excess human resources but when it completed and organized state of affairs will wholly alter and it decrease the cost of excess human resources, if we mine this information and do some interesting forms from that information it will helpful in the future determination devising. We can detect our error that companies did in the past, we can detect utilizin g old informations in meaningful ways and increase determination devising. Connection between DSS, KM and DM The intent of informations excavation and determination support system solves the hard and large jobs ( Bohanec A ; Zupan, 2001 ) . Harmonizing to Bojance and Zupan ( 2001 ) The integrating of DM and DS may better the quality of problem-solving methods, procedures, and achieved consequences. Data excavation and information warehouses for illustration informations regular hexahedron, OLAP, information marketplaces, determination trees and so on can be used for doing determinations as it has used in instance survey. The cognition from antecedently stored used make new cognition assisting to determination shaper to do determination ( Nemati, Steiger, Iyer A ; Herschel, 2002 ) . Explicit cognition generated from GSS brainstorming Sessionss and stored as text watercourses can be analyzed by text excavation package, a signifier of AI based information excavation, to supply cardinal words, related constructs, bunchs of similar thoughts, etc ( Nemati, Steiger, Iyer A ; Herschel, 2002 p.147 ) . Park et Al ( 2001 ) proposed the KREFS cognition direction model has four major faculties, the natural information preprocess and send to data excavation faculty so knowledge extracted from informations mining faculty and direct it to knowledge polish and cognition public presentation component faculties. Harmonizing to Nemati et Al ( 2002 ) cognition wherhouse consist of six constituents ( 1 ) the data/knowledge acquisition faculty, ( 2 ) the two feedback cringles, ( 3 ) the extraction, transmutation and burden faculty, ( 4 ) a cognition warehouse ( storage ) faculty, ( 5 ) the analysis work bench, and ( 6 ) a communicating manager/ user interface faculty . The information excavation techniques like association regulations apply for old cognition to acquiring new forms from old informations utilizing as input for determination support system. 3. Case study-presentation The intent of this study is to efficient usage to informations excavation techniques for determination support system. The usage of informations mining techniques to better the whole concern procedure, as it has shown in instance survey of the procurance section. Introduction of company First of all brief introduce of the Ghani group of companies. Ghani Group is a Lahore based group which is running a diverse scope of concerns including three glass workss, an car works, a figure of taking excavation companies, and trading houses. The Ghani Group of companies has an one-year turnover of over 5 billion Sri lanka rupees. Their venture into the fabrication field took the signifier of Ghani Glass, incorporated in 1992 and get downing production in 1995, organizing the first measure on the route to success of Ghani Glass limited, which today own three glass workss viz. GGL1 Hattar, GGL2 Landhi and GGL3 Sheikhupura. Background and Problems From my experience of the Ghani Group of companies in the section of Procurement, the procedure of procurance is a manual. The duty of procurement section is buying natural stuff or any type of goods that ghani group of companies demand. It has some draw dorsums and first of all how purchase requisitions occur, how it reach in the procurance section, and so how the procurance section works that purchase petition measure by measure every bit defined below. Bing a anchor of the Ghani Group of companies Procurement Department it is still working under the traditional methods of buying. Pull offing more than 100 petitions per twenty-four hours is a hard occupation for the staff. As specified earlier, their procurance system is traditional. Demands and purchase requisitions come from the workss and so procurement staff get down the procedure. The purchase requisitions come from the works by station or particular individual allocated for that intent. When the purchase requisitions reach in the procurance section foremost the requisitions are being separate harmonizing to their nature. Then they are handed over to the appropriate field staff that brings them to market ( Suppliers/Vendors ) . They collect the rates from 3 to 4 providers and subject them to the procurance coordinator in the corporate office who compares the rates and finalizes the order by making a insouciant deal with the provider. A purchase order is issued with the blessing of the manager procurance and the provider gets apt to supply the needed stuff on committed rates and required bringing program. As the providers send the stuff to the works, the chief shop issues a GRIR papers ( Good Received and Inspection Report ) , which authenticates that the stuffs has received with the criterions needed and committed. The procurance section receives the GRIR and submits them to the accountant section with the bills of stuff to finalise the payments. How natural stuffs or any other purchase petition cause meet demand on. The procurement section of the Ghani Group of companies Lahore has some jobs in buying points, since the Ghani groups of companies have many workss in different metropoliss and the procurement section of Ghani group of companies is situated in City Lahore. The procurance section duties to carry through every works purchase petitions ( natural stuff or any type of goods ) . For illustration bike works out of natural stuff. The works trough cheques if the natural stuff is available in stock list or non if stuff is non available in the stock list. The director of the section concerned makes a purchase requisitions for needful points. In the Purchase Requisition there are reference Department Name and Address, day of the month, point Idaho, point name, No of Item, Unit, needed day of the month, and remarks. The Purchase Requisition id sent to the procurance section manually by manus or by station. When purchase requisitions reach the procurance section. They are sorted out and manus after the concern individuals. The individual Concerned checks the purchase requisition and contacts providers that he/she has on record. The provider gives rates of that point, manner of payment and bringing day of the month. The procurance coordinator compares all the citations and gives a purchase order to the provider selected. At this phase the Ghani Group of companies face job, if they do non pull off the cognition. If they compare old records they need a batch of clip to make that because they have no records of old records except packages of documents in files. Then the provider supplies the point desired to the requested section. The requested section checks the quality of points and measure of points ; the requested section sends the Goods Received Investigation Report ( GRIR ) to the history subdivision. The provider besides sends the bill to the history section. The procurance section had already sent the purchase order transcript to the history section. The history section makes the payment to the provider. In this method there are many jobs and we holding batch of records of providers after some old ages. Company needs batch of human resource. Company need batch of infinite required where we save the paper based records. Lot of clip is required to seek the specific merchandise record that had purchased some twelvemonth ago. In Procurement section demand determination support system, helpful for doing determinations in selecting and linking providers. The chief job of the procurement section of the Ghani Group of companies is that they lack of cognition direction. Company has batch of informations after few old ages. Ghani group of companies should mange that information in a signifier that it supports for determination devising. For all purchase requisitions, citations, purchase orders, goods received probe study, bills etc, we should sort the informations and extract cognition from it. 4. Deployment Analysis and Design This subdivision is about description of the system. The system will be web based. Harmonizing to Bharati A ; Chaudhury ( 2004 p.187 ) Web-based determination support systems are being employed by organisations as determination AIDSs for employees every bit good as clients . First of all we have to roll up the informations from all beginnings as described in the figure below. We have extract cognition from informations and happen interesting forms from that information ( Venter et al, 1997 ) . The information is now in good formed and meaningful. Data excavation techniques ( determinations tree, regulation finds, categorization etc ) for the intent of determination support system ( Wan A ; Lei, 2009 ) we can easy take many determinations for illustration about supplier choice, old purchase records, supplier history etc. When we manage the informations in Knowledge Management utilizing informations excavation techniques the procurance section can easy do determinations. The diagram shows the high degree extraction from operational databases and other beginnings. Figure3: The above figure is taken from informations mining book by ( Han A ; Kamber, 2001 ) . The above usage instance diagram shows how procurement coordinators can interact the system. The diagram besides shows what tasks that procurance coordinator to execute. Data Base Design This is the proposed database design. We can utilize this database for salvaging records, recovering records, modifying records, delete records. How system Work In my intent system there will be a web based system. If any point should be out of stock, the concern director ( section director ) login the system in and creates a purchase requisition when he shall come in the point information point codification or point name in the purchase requisition signifier, the system will demo automatically the available measure of that point. This is produce with the aid of determination support system. The concern trough fills out the purchase requisition signifier and submits that form to the system. Some clip he want to cognize when this point purchased last clip how much measure and where it has used we make these type of regulation utilizing informations excavation techniques. When new purchase requisitions arrive in the system, the system shows an qui vive and the procurance director cheques and verifies that purchase requisition. He can verify for wrong entry or multiple entry etc. After verifying he/she will print that purchase petition on the web. The system besides sends an electronic mail to all relevant registered providers for that purchase petition. The providers view the purchase petitions and submit their citations to the system. In the instance of a new provider should register himself in the system. It will be web based so local providers registry and subject citations every bit good as international providers. The procurance trough gets all the citations from the system and so analyse them. At this phase we implement informations excavation techniques in our system. We enter the point code/name the system will demo us old records of that point, the providers name and point rate, the provider Goods Received Investigation Report that have purchased late. We can pull out different type of informations utilizing informations excavation for illustration we make some regulations ( Wan A ; Lei, 2009 ) like best provider who supply that merchandise we shall look into these properties merchandise lowest cost, best quality, minimal shipment clip, and long clip recognition installation. This type of association regulations build. In these records we can easy analyse any provider and citation that we have received. Databases Cost Quality Shipment clip Credit Facility Decision Making Using determination support system we can easy do determinations. We can recycle old informations, for illustration we can look into the provider s history like cost, quality, shipped clip and recognition installation. We can look into that the provider delivers points in clip or non. So data excavation is used for pull outing informations from databases and gives input to determination shaper as shown above in the figure. We can see the informations from different angles and do determinations from this cognition. It will increase the control for choosing supplier procedure. It will besides assist for many procedures are automated. One of the providers shall be selected for a purchase order on the footing of old cognition and his new citation. After successfully holding delivered the points, the provider shall besides subject an bill. The concern director shall do a GRIR on the bases of the purchase order. The accountant shall verify the bill with GRIR and purchase order and so he shall unclutter the payment of that provider on the method which is mentioned in the purchase order. So it is really helpful for determination devising, whenever the procurance section receives the new purchase petition. They can easy reach providers. The procurement section can easy do determinations which provider is the best. In some instances, points ( natural stuff ) can needed desperately so the procurance section look into the old bringing of that point and contact that provider. If any organisation implements that system they will hold good bases for determination devising. Implementing that system they will non necessitate a immense figure of human resorts for the procurance section. After deployment of the undertaking in the ghani group of companies it will helpful making interesting forms from the old fresh information ( Han A ; Kamber 2006 ) , it will helpful for pull offing the cognition in the signifier of informations marketplaces ( Han A ; Kamber 2006 ) , it will diminish the cost of human resources, it will helpful when the new procurance director replace, it will increase the determination doing power, it will helpful for doing speedy determinations, it will helpful for larning errors that company did in the yesteryear, it will helpful for diminishing the frauds in the procurance section, and it will increase the efficiency of the section. The system will demo all the historical information which come the determination rapidly. The system shows the old records of relevant purchases and its affects that will helpful for larning positive and negative points for illustration purchase order given to non popular provider, who gave us non standard natural st uff with low monetary value. If the natural stuff is non up to standard the resulted merchandise will non reliable and it will damage the company reputation. A instance survey has discussed in the paper of Chen et Al ( 2007 ) it will specifically manage the procurance section but my proposed system mange the whole procedure of procurance from purchase petition coevals to raw stuff delivered. 5. Discussion The information excavation techniques are helpful and usage for determination support system. Harmonizing to shaw et Al ( 2001 ) Ghani group of companies belief that it is need and requirement to deeply understand and usage of cognition direction and informations excavation for determination support system. Harmonizing to Power ( 2002 ) there are different types of determination support system. In this study chiefly stress on data-driven determination support system for pull outing cognition from big sum of informations ( Nilsson A ; Ziemke, 2007 ) . The procurance coordinators ( determination shapers ) confronting batch of jobs during the procurement procedure in the Ghani group of companies. The handiness of big volume of informations, made possible by modern information engineering, a major job is to filtrate, kind, procedure, analyze and manage this information in order to pull out the information relevant to the user and the growing in the size and figure of bing databases far exceeds human abilities to analyse such informations utilizing traditional tools and therefore creates both a demand and an chance for informations excavation tools ( shaw et Al, 2001 p.135-136 ) . The information excavation techniques utilizing web as described in instance survey will hold better decision-making satisfaction with timely, accurate, and complete information ( Bharati A ; Chaudhury, 2004 ) The part of the study is use of informations excavation technique specially association regulations for determination support system and a web based system acquiring inputs from the informations excavation for determination support system. The system has designed to carry through the demands of procurance section but it will be extendible to whole organisation the undermentioned research issues are identified: How we can we seprate the trd What type of function drama gatekeepers of the workss? How to hive away new natural stuff? How to incorporate the system into whole organisation? 6. Decision In this study I present a instance survey of procurement systems. We have lot of informations that is nonmeaningful and we have to utilize in an efficiently manner so that we can make determination support system. I used informations excavation and Knowledge Management for determination devising. We can salvage clip and money implementing the system in the organisation. We can do speedy determinations for any instance of purchase. We can pull off our informations in a good organized signifier. The system shall work in optimized.

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