Software Engineering

BPM World and Event-based World EVENT-BASED WORLD > Real-time   data streams consisting of   events. > Bottom-up view. > Push-based   approach with implicit   invocation. > Complex Event Processing etc. > Data   processing: Reactive and asynchronous (pub/sub). BPM WORLD  > Structured business processes > Top-down view > Pull-based approach with > explicit invocation SOA, Databases, ERP, etc. > Data processing:  Request/reply

             Introduction to Applied Statistics Applied Statistics is a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters. In applying statistics such as a scientific, industrial, or societal problem, it is necessary to begin with a population or process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal". It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.                    Software Environment The S, S plus, Mat lab and R environments are an integrated suite of software facilities for data analysis and graphical display with the feature like an extensive and coherent collection of tools for statistics and data analysis, language for expressing statistical models and tools for using linear an

  Multimedia Team ( Project manager, Multimedia Designer, Interface Designer, Writer, Video Specialist, Audio Specialist, Multimedia Programmer, The Sum Of the parts ). A typical team for developing multimedia for CD-ROM or the Web consists of people who bring various capabilities to the table. Often, individual members of multimedia production teams wear several hats. 1.       Project Manager: A project manager’s role is at the center of the action. He or she is responsible for overall development and implementation of a project as well as for day-to-day operations. Budgets, schedules, creative sessions, time sheets, illness, invoices, team dynamics-the project manager is the glue that holds it together.                         2.       Multimedia Designer : Multimedia designers need variety of skills. We need to be able to analyze content structurally and match it up with effective presentation methods. We need to be an expert on different media types, and capable media inte

  THE STAGES OF PROJECT :   Most multimedia and Web projects must be undertaken in stages. Some stages should be completed before other stages begin, and some stages may be skipped or combined. Here are the four basic stages in a multimedia project. Planning and costing : A project always begins with an idea or need that we refine by outlining its messages and objective. Before we begin developing, plan what writing skills, graphic art, music, video, and other multimedia expertise will be required. Develop a creative graphic look and feel, as well as  a structure and navigation system that will let the viewer visit the messages and content, estimate the time needed to do all elements, and prepare a budget. Work up a short prototype or proof of concept  Designing and Producing :  The major goal of this phase is to translate the problem studied in the first phase and design made in second phase into proper a finished project. Generally, pr

Multimedia: Multimedia is any combination of text, graphic art, sound, animation, and video delivered to you by computer or other electronic means. It is richly presented sensation. When you weave together the sensual  elements of multimedia-dazzling pictures and animations, engaging sounds, compelling video clips, and raw textual information, you can electrify the thought and action centers of people’s minds.  When we allow an end user – the viewer of a multimedia project-to controls what and when the elements are delivered, it is interactive multimedia.   Its applications :  Multimedia is appropriate whenever a human interface connects a human user to electronic information of any kind. Multimedia enhances minimalist text-only computer interfaces and yields measurable benefit by gaining and holding attention and interest, multimedia improves information retention. There are following areas where multimedia is being used. 1. Multimedia in Business : Business applications

THE RISK DRIVEN MODEL - Introduction As digital systems become part of everyday life for most of us that caused increase in the development of software systems and applications hugely. The increasing number also shows the worries of developers for risk of failure for different stages of the development and different platforms including web. The management of risk is very important factor to be considered for all software development processes. The risk driven model is very simple and easy to use with having low “risk of failure” level which attracts most of the developers. However, the development of software means developer must anticipate the possible risk of failure for different stages by using right tools and techniques. Though which development technique is more reliable? Or which architecture is enough for developer? And which is model should developer use is still a question. So here I present how risk driven is enough for the developers for all stages (including an

Writing Research Proposals Most students and beginning researchers do not fully understand what a research proposal means, nor do they understand its importance. To put it bluntly, one’s research is only as a good as one’s proposal. An ill-conceived proposal dooms the project even if it somehow gets through the Thesis Supervisory Committee. A high quality proposal, on the other hand, not only promises success for the project, but also impresses your Thesis Committee about your potential as a researcher. A research proposal is intended to convince others that you have a worthwhile research project and that you have the competence and the work-plan to complete it. Generally, a research proposal should contain all the key elements involved in the research process and include sufficient information for the readers to evaluate the proposed study. Regardless of your research area and the methodology you choose, all research proposals must address the following questions: What you plan to

Weka - Data Mining Tool   Experiment  (ALL at once- “Adult, Irish, Zoo” & “J48, Naïve Bayes, KNN) In this experiment I am using all algorithms and datasets at once in order to show the comparison and do the evaluation among the datasets and its correctness including standard deviation.   Here I am comparing and evaluating the previous results. v – significantly better, *-significantly worse Evaluating J48, Naïve Bayes, KNN on Zoo dataset:  We got average of 92.66(SD: 7.07), 93.69(6.99) and 96.06(5.41) percent correct using J48, Naïve Bayes, KNN on zoo dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel. Evaluating J48, Naïve Bayes, KNN on Iris dataset:  We got average of 95.33, 92.60, 86.47  percent correct using J48, Naïve Bayes, KNN on iris dataset. It’s a  10 fold cross validation so, if we want to see individual result we can save result on CVS file from setup panel. Eva

Weka - Data Mining Tool   Experiment  (Irish Dataset): In this experiment I am using Irish dataset and different algorithm to show classification using 10 fold cross-validation methods, there will be 10 repetitions on the processes to determine the results. (Note: Use of all other datasets and algorithms is similar process).  For beginners, start Weka and click on Experimenter Option 1          Using J48      Here I am using J48 algorithm to Irish datasets, the process is as follows: There are three panels starting with Setup Panel: ·          Click New to start new experiment ·          Click add new under datasets in order to add new dataset i.e. Irish.arff ·            Click add new under Algorithm in order to add new dataset i.e. J48 ·          Experiment type is cross validation by default  ·          And Its classification by default We can also choose other experiment types such as percentage split etc., regression types and we can set the number of

Weka - Data Mining Tool  Algorithms: There are a lot of algorithm in weka for various classification and experiments and some the major and widely used are following : Decision tree(J48): NAME: weka.classifiers.trees.J48 SYNOPSIS: Class for generating a pruned or Un pruned  C4.5 decision tree. Naïve Bayes: NAME: weka.classifiers.bayes.NaiveBayes SYNOPSIS: Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) KNN(IBK): NAME: weka.classifiers.lazy.IBk SYNOPSIS: K-nearest neighbours classifier. Can select appropriate value of K based on cross-validation. Can also do distance weighting. SVM(LibSVM):  NAME: weka.classifiers.functions.LibSVM SYNOPSIS: A wrapper class for the libsvm tools (the libsvm classes,

Weka - Data Mining Tool Description of Datasets   Description of Adult dataset:   Name: Adult Number of instances: 32561 Number of attributes: 15 Description about attributes:  ·          Age: Type: Numeric,  Missing: 0, Distinct:73 ·          Workclass: Type: Nominal,  Missing: 0, Distinct:9 ·          Fnlwgt: Type: Numeric,  Missing: 0, Distinct:21648 ·          Education: Type: Nominal,  Missing: 0, Distinct:16 ·          Education-num: Type: Numeric,  Missing: 0, Distinct:16 ·          Marital-status: Type: Nominal,  Missing: 0, Distinct:7 ·          Occupation: Type: Nominal,  Missing: 0, Distinct:15 ·          Relationship: Type: Nominal,  Missing: 0, Distinct:6 ·          Race: Type: Nominal,  Missing: 0, Distinct:5 ·          Sex: Type: Nominal,  Missing: 0, Distinct:2 ·          Capital-gain: Type: Numeric,  Missing: 0, Distinct:119 ·          Capital-loss: Type: Numeric,  Missing: 0, Distinct: 92 ·          Hours-per-week: Type: Num