1. Choose one data mining tools and briefly explain on background of the product. (10%)
IBM SPSS Modeler is a data mining and text analytics software application built by IBM. Used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
The primary adaptations of the product called Clementine and Unix-based and is composed as an arranging apparatus and is not available to be purchased to clients. Clementine was initially created by a UK organization called Integral Solutions Limited (ISL), in a joint effort with scientists in counterfeit consciousness at the University of Sussex. Unique Clementine was executed on Poplog,
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In the early 2000s has developed into a product structural engineering customer/ server, and not long after that the following client interface part has been totally revised and supplanted with a Java front home, permitting deeper combination with different apparatuses gave by SPSS.
Later on, client runs under Windows. Behind Unix server variants (Sun, HP-UX, AIX), Linux and Windows. Graphical user interface written in Java. IBM SPSS Modeler 14.2 is the first release Modeler by IBM. IBM SPSS Modeler 15, issued in June 2012, introducing important new function for Social Network Analysis and Entity Analytics.
2. Discuss on data preparation features provide by the product. (up to one page) (20%). By integrating predictive analytics with decision management, scoring and optimization in the organization's processes and operational systems, SPSS Modeler help users and systems to make the right decision every time.
Decision management
Automating and optimizing transaction decisions by combining predictive analytics, rules and scoring to deliver the recommended action in real time. Decision management capabilities enable integration of predictive analytics and business rules into organizational processes to optimize and automate decision-high volume at the time of impact.
Automated
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Discuss on data mining operations (algorithms) perform by the product (IBM SPSS Modeler). (20%)
Database algorithms are often integrated with the database server and can offer better performance. The model is built and stored in the database easier to use and share any application that can access the database.
Generation SQL database model is different from SQL generation known as "SQL pushback". This feature allows you to create SQL statement to native IBM SPSS Modeller operations that can be "pushed back" to database to improve performance. For example, the Merge, aggregate, and Select all nodes generate SQL code that can be pushed back to the database in this way. Using SQL generation in combination with the model database can result in the river that can be run from beginning to end in the database, resulting in huge profits for the performance of the river walk in IBM SPSS Modeller.
SQL database modelling and optimization require IBM SPSS Modeller Server connectivity is enabled on the computer IBM SPSS Modeller. With this setting enabled, you can access the database algorithms; push back SQL directly from the IBM SPSS Modeller and IBM SPSS Modeller Server access. To verify the current license status, choose the following from the menu IBM SPSS
This toolset will drive operational excellence by creating consistent processes for both the plans and FEPDO. The dashboard and reporting features provide a real-time insight into key performance measurements to support informed decision making, the ability to generate configurable automated reports and schedule delivery of those reports. Workflow will provide the much-needed relief to supervisors who currently manually assign workflow processes and give them greater visibility into backlogs and claims inventory. It also eliminates the paper and email trail we currently use to manage assignments and employee progress; allowing Managers and Team leads to reallocate their time to other high value
A process to load the data in the data warehouse and to create the necessary indexes C. A process to upgrade the quality of data after it is moved into a data warehouse D. A process to upgrade the quality of data before it is moved into a data warehouse
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It qualifies as an analytical company based on its utilization of enterprise level business intelligence teams and infrastructure, using local and department level analytical teams to build out competitive advantages, and having a culture that supports and can understand analytics from the top down. It lacks the features to be considered a competitor based primarily on its fractured governance of analytical initiatives and the lack of a strong central analytical corporate strategy that is applied beyond a few select measures tied to company performance to drive
There are many benefits that the BIS can bring to an organization such as boost productivity, sales and market intelligence, the setting of more accurate and realistic goals, positive return on investments, gain insights into consumer behaviors, operational visibility and identification of key trends (Holley, A. 2015). Recommendations for developing and using the BIS described in this case, include the use of an effective BIS that incorporates different factors or circumstances in the internal and external environment of the organization such as sales, costs, weather, items or services offered by the company, and trends. Another reason to implement BIS is to reduce voluminous amounts of irrelevant data, poor data quality, and user resistance that affect the effectiveness of
With the use of this framework in reengineering its call centers and the automation of manual processes through call centers. Malaysia Airlines was able to achieve the following: Cutting call center costs by 18% and tripling sales, through phone, e-mail, fax, and web chat they were able to service customers, Interactive voice response or online ticket payment, and Tracking of agent productivity done by managers. In doing the following, they were able to streamline their processes. Malaysia Airlines measure the strategic metric. It measures the functional goals so that Malaysia Airlines can boost their customer payment capabilities.
Situation Nanda has designed an innovative alarm clock named Clocky. Clocky has a unique function that it can jump off a nightstand and roll around the room to wake people up, arousing attention and interests of the public. However, Clocky is still a prototype and it was not yet commercially available. Precise marketing positioning, promotion means, distribution channels and pricing are crucial in order to make profit from the sale of Clocky. This report includes the evaluation of the current marketing strategy adopted for Clocky as well as suggestions of marketing segmentation in Hong Kong.
4.1 Project Overview This project aims to build a prototype web application to demonstrate how software can help pancreatic cancer patients. The specifications of the prototype will be discussed in this section. 4.2 User types There are mainly three types of users: Member users – these users will have a user profile created on the website and are entitled to participate in forums or chats and avail medical data storage facility to get online consult. They have authenticated user profiles.
The first part of the integration process included updating each change made to the ladder logic program for the third floor to the second and first floor. Changes were made in the alarm and the light control section to fix errors and deficiencies found in the logic. The second part of the integration process took place in programming the human machine interface (HMI) through which operators may observe, monitor and exert control over the entire system. This implementation provides functionalities such as alarming, logging, trending and in the future, offsite monitoring. The HMI was updated to include all changes done to each floor in the light control, alarm and the energy management system.
Once the data is fed into the computer, the software is able to make the association between
2.7 Observations from GMPCS Model Based on the above model, several observations can be made as follows. Observation_1: According to an interoperability feature between CSPs, a storage service will be hosted over a pool of resources that are in different geographical locations. Furthermore, different technologies, protocols, and security strategies are applied by each CSP within its datacentres to facilitate managing an environment to protect both resources and data. The technologies and strategies, therefore, might be disparate in terms of efficiency, and the type of storage network or storage system might be varied as well.
Business Name: Dymocks Booksellers Dymocks is the leading bookseller in Australia and is recognised for quality advice, value for money, professionalism and customer service. Dymocks has been franchising for over 30 years and would like to secure the vacant store in the shopping centre. Dymocks’ mission statement is “As a family owned business and the oldest Australian owned bookstore, Dymocks prides itself on meeting the leisure, learning and gift needs of all booklovers by offering superior customer service and an enhanced book buying experience.” Legal Structure Legal structure of a business determines who shares in the profit and losses, how tax is paid and where legal liability rests. The legal structure of Dymocks will be a sole trader.
L.L. Bean. Inc Item Forecasting and Inventory Management Executive Summary L.L.Bean, Inc. has been a trusted source for quality apparel, reliable outdoor equipment and expert advice for over 100 years. Founded in 1912 by Leon Leonwood Bean, the company began as one-man operation. With L. L.'s firm belief in keeping customers satisfied as a guiding principle, the company eventually grew to a global organization with annual sales of $1.56 billion. The company headquarters are in Freeport, Maine, just down the road from the original store.
It relies on sets of numbers stored in electromagnetic format used to create representations and simulations that correspond to material performance and to map out built
Social Media Analytics and features It is the process of applying data (both structures as well as unstructured) from social media sites such as twitter, facebook, Google+ and other such sites for better understanding of customer attitude and behavior. It also serve as an effective tool for business and market research and ultimately for business decision making. The large availability of user-generated data and the links between users leads to the dispersion of useful information, opinions and sentiment as well as emergent issues and trends (Leskovec 2011; Agrawal et al. 2011; Nagarajan et al. 2011) and is referred to as ‘Social Media Analytics’.