Application Security vs Application Simpleness

Let me get back to my right track that is IT world first. Huf, such a hard time to share something if u don’t have enough experience ><

I get this idea to bring out the topic about Application simpleness vs Application security. Why should we tandem these two different things? Because these two are rival which influence each other. How come? Let’s get started to the main topic then~

Should get to the understanding of each terms first to compare them, so you’ll get the reasons why these things above is head to head in an application.

Application simpleness often comes along with the term usability. It’s the usability of the application, the simpleness of user interface of an application which makes common people can use the application easily. Its simpleness makes it possible for common people not to undergo any training in using the application. I choose the term common people here to describe people who not related at all to the application, or people who don’t know much about the application, or people who don’t have any or only have less knowledge in the ability and usability of the application. As the additional, I believe that the simpleness of an application can’t be significant enough if the application’s complexity is still low. You know, you can’t get the simpleness of the application only by using search engine, email, messenger, word processor, etc. You’ll know the meaning of application simpleness when using complicated applications such as ERP modules like financial, human resource, asset management, etc. You’ll realize that without having enough knowledge about the application, you can’t use the application to the fullest. I wish this explanation is good enough to make you all understand the meaning of application simpleness.

Move on to the next topic, application security. You should have understood the terms well because  almost all of us had used the application at least once. Usually, term application security gets tightly related with application data. Imagine when we have really important data to be saved in an application, unfortunately the application can be accessed worldwide through the internet. How can the application ensures that our really important data won’t be taken by some irresponsible parties? Or how can our important data not to be exchanged between another user? This is what application security all about.

So, how can application security be compared to application simpleness? You see, when using a simple application, you don’t have to go through many procedure to insert your data to the application. But of course, for the application to ensure your data security, there should be some procedures for you to insert the data. The easy example where simpleness is go head to head with the security is, when you insert many types of data to an application. Simple application will take whatever data you’ve inserted, while secure application ensures that the data you’ve inserted is correct. For example for phone number, to make it easy for the user (in relation with application simpleness), an application can take whatever value you’ll insert, but a secure application should ensure that the application should only accept number value. For higher example, we can take user’s phone number field. For simple application, whatever phone number inserted shouldn’t be processed longer, application should only save it to the database. For secure application, phone number inserted will be processed longer to the provider of phone number to check the availability and validity of inserted phone number.

Building a simple application is really important, as we can’t avoid common people who will use our application, while building a secure application is other story which like a wall that should be broken by all applications. That’s why building a simple application is good enough, but you shouldn’t forget about the security of the application. Go back to your original purpose to know which one should be higher priority, because as I described before, these two things can’t stand in the same terms.

I hope this description can be useful as usual~

Read Full Post »

Models in DSS

MODELS IN DSS

All DSS above the simplest data-oriented ones are based on models. Their purpose is to hence the decision maker who is using it to predict what would happen in the real world if certain choices were made.

This enables the decision maker to evaluate alternative actions without trying them out in practice – obviously saving in time, expense and overall hassle to say nothing of reducing the likelihood of seriously wrong decision that could do major damage to an organization.

Models

Model is a representation of an actual system.

Models embody system characteristics that are important to the model’s users.

At the same time, models simplify reality by eliminating other characteristics that are not important for their purpose.

The central idea of a model is that important relationships that apply to the system being modeled also apply to the model.

Type of Models

 

Static vs Dynamic Models

• Static Models

shows the values that system attributes take when the system is in balance (steady).

Static model can model either a static system or a dynamic system.

Showing when a system is in balance, can tell decision makers how the system will eventually stabilize even if it does not show them how it gets to that point. Because it involves much less data, it can be easier for a decision maker to analyze.

may also be able to provide results more quickly than a dynamic one, allowing decision makers to consider more options in a given amount of time.

Ex: estimate next year’s profits from calculate profits of sales volume for each of the firm’s five products

• Dynamic Models

follows the changes over time that result from system activities.

The passage of time

With cause and effect relationships connecting one time period to the next is essential to system behavior.

Continuous VS Discrete-Event Model

• Continuous models :

Describe physical or economic processes in which the numbers that describe the system vary continuously.

Ex:Blood pressure varies continuously over time.

• Discrete-event models :

Deal with systems in which individual events occur at identifiable points in time and change the state of the system instantaneously from one value to a different one.

Discrete-event Simulation Models

v A model that allows us to predict the behavior of a business system by modelling the espected behaviors and interactions of its components over time.

v This is useful because we often know how each system components behaves but we are unable to assess the impact of their interactions of the behavior of the overall system.

Ex: barbershop

Model Types

• A Graphical model

Data flow diagram

• A narrative model

Natural language such as English

• A physical model

A smaller or idealized representation of the real system such as a model railroad or an architecturel model of a building being designed.

 

Read Full Post »

Introduction to Data Mining

DATA MINING

• Organizes and employs information and knowledge from databases
• Statistical, mathematical, artificial intelligence, and machine-learning techniques
• Automatic and fast
• Tools look for patterns

Simple models

Intermediate models

Complex Models

• Data mining application classes of

Problems	Sequencing
Classification	Regression
Clustering	Forecasting
Association	Others

• Hypothesis or discovery driven
• Iterative
• Scalable

Tools and Techniques

• Data mining

Statistical methods	Neural computing
Decision trees	Intelligent agents
Case based reasoning	Genetic algorithms

• Text Mining

Hidden content

Group by themes

Determine relationships

Knowledge Discovery in Databases

• Data mining used to find patterns in data

–         Identification of data

–         Preprocessing

–         Transformation to common format

–         Data mining through algorithms

–         Evaluation

Data Visualization

• Technologies supporting visualization and interpretation

–         Digital imaging, GIS, GUI, tables, multidimensions, graphs, VR, 3D, animation

–         Identify relationships and trends

• Data manipulation allows real time look at performance data

Multidimensionality

• Data organized according to business standards, not analysts
• Conceptual
• Factors

Dimensions	Time
Measures

• Significant overhead and storage
• Expensive
• Complex

Analytic systems

• Real-time queries and analysis
• Real-time decision-making
• Real-time data warehouses updated daily or more frequently

–      Updates may be made while queries are active

–      Not all data updated continuously

• Deployment of business analytic applications

Web Analytics/Intelligence

• Web analytics

–      Application of business analytics to Web sites

• Web intelligence

–      Application of business intelligence techniques to Web sites

 

Read Full Post »

Introduction of Data Warehouse

Data Warehouse

•        Subject oriented
•        Scrubbed so that data from heterogeneous sources are standardized
•        Time series; no current status
•        Nonvolatile
• Read only
•        Summarized
•        Not normalized; may be redundant
•        Data from both internal and external sources is present
•        Metadata included

Data about data

• Business metadata
• Semantic metadata

Architecture

May have one or more tiers

Determined by warehouse, data acquisition (back end), and client (front end)

• One tier, where all run on same platform, is rare
• Two tier usually combines DSS engine (client) with warehouse

More economical

• Three tier separates these functional parts

Migrating Data

v  Business rules

• Stored in metadata repository
• Applied to data warehouse centrally

v  Data extracted from all relevant sources

• Loaded through data-transformation tools or programs
• Separate operation and decision support environments

v  Correct problems in quality before data stored

• Cleanse and organize in consistent manner

Data Marts

v  Dependent

• Created from warehouse
• Replicated

Functional subset of warehouse

v  Independent

• Scaled down, less expensive version of data warehouse
• Designed for a department or SBU
• Organization may have multiple data marts

Difficult to integrate

Business Intelligence and Analytics

• Business intelligence

Acquisition of data and information for use in decision-making activities

• Business analytics

Models and solution methods

• Data mining

Applying models and methods to data to identify patterns and trends

OLAP

• Activities performed by end users in online systems

–         Specific, open-ended query generation

SQL

–         Ad hoc reports

–         Statistical analysis

–         Building DSS applications

• Modeling and visualization capabilities
• Special class of tools

–         DSS/BI/BA front ends

–         Data access front ends

–         Database front ends

–          Visual information access systems

Read Full Post »

Data vs Information vs Knowledge

I’m here again~

This time I want to share some knowledge about the difference of data, information, and knowledge. I also want to share a little about some data models.

Data, Information, Knowledge

Data

• Items that are the most elementary descriptions of things, events, activities, and transactions
• May be internal or external
• Raw data collected manually or by instruments
• Representative data collection methods are time studies, surveys (using questionnaires), observations (eg using video cameras) and soliciting information from experts (eq interviews).
• Quality is critical

–         Quality determines usefulness

• Contextual data quality
• Intrinsic data quality
• Accessibility data quality
• Representation data quality

–         Often neglected or casually handled

–        Problems exposed when data is summarized

• Cleanse data
  • When populating warehouse
  • Data quality action plan
  • Best practices for data quality
  • Measure results
• Data integrity issues
  • Uniformity
  • Version
  • Completeness check
  • Conformity check
  • Genealogy or drill-down
• Data Integration
• Access needed to multiple sources
  • Often enterprise-wide
  • Disparate and heterogeneous databases
  • XML becoming language standard

External Data Sources

• Web

–         Intelligent agents

–         Document management systems

–         Content management systems

• Commercial databases

–         Sell access to specialized databases

Database Management Systems

• Software program
• Supplements operating system
• Manages data
• Queries data and generates reports
• Data security
• Combines with modeling language for construction of DSS

Database Models

• Hierarchical

–         Top down, like inverted tree

–         Fields have only one “parent”, each “parent” can have multiple “children”

–         Fast

• Network

–         Relationships created through linked lists, using pointers

–         “Children” can have multiple “parents”

–         Greater flexibility, substantial overhead

• Relational

–         Flat, two-dimensional tables with multiple access queries

–         Examines relations between multiple tables

–         Flexible, quick, and extendable with data independence

• Object oriented

–         Data analyzed at conceptual level

–         Inheritance, abstraction, encapsulation

• Multimedia Based

–         Multiple data formats

JPEG, GIF, bitmap, PNG, sound, video, virtual reality

–         Requires specific hardware for full feature availability

• Document Based

–         Document storage and management

• Intelligent

–         Intelligent agents and ANN

Inference engines

Information

Organized data that has meaning and value

Knowledge

Processed data or information that conveys understanding or learning applicable to a problem or activity

Actually I get this theory when I still learn in university. I made the synopsis of the theory and here is the result. I hope this article can be useful~

Comments, questions, suggestions, critics are welcomed~

Read Full Post »

Make a good database design

As we all know that a good database design results in a reliable, maintainable, and extendable of an application. Database is a core of an application because as we know, application exist in order to let user do manipulation to the data based on each user’s role. It’ll be difficult for application if the database design is poor, because the maintain process will be more difficult. It’ll also be difficult for application to be extended because poor database design needs more effort to be extended well. Therefore, when we plan an application, while preparing UI design we can also analyze database design.

To design a database, people usually use ER diagram or DFD as common method. I myself prefer ER diagram more, because I often use it. I can’t give any reference about DFD though, because I don’t use that method yet until now.

I’ll share some best practice in designing database:

• Use similar naming convention when deciding tables name and columns name.
• Make sure the database design has been in at least 3NF (this can be done by designing database using ER diagram).
• Define the attributes of each table using datatype which is best choice between many data types exist. This can add database performance later.
• Perform de-normalization if you think the existing design makes database performance slower.
• Give index to columns which are often used. This can makes database performance faster.

I think that’s all I can share for now. I’ll update this post if I have new resource related this matter. ^^

Read Full Post »