COM+: Definition

COM+ Tutorial from Scratch has two parts. Part I has basic concept of COM+ and its related topics. Part II will teach you how to use COM+ in real world applications.


This article is an extended version of my previous article " What COM+ exactly is?". This tutorial explains what is COM+ and how it works in real world applications. I have used Windows 2000 and VC++ 6.0.
Windows DNA is the first steps to understand COM+.
Windows DNA
Windows DNA is a framework that describes how to develop multi-tier, high performance, scalable, and distributed applications over the network. The goal of DNA is to provide enterprise level solutions which is suitable for any size. The heart of DNA is an integrated programming model based on COM+. In other words, Windows DNA is a way to provide an enterprise based solutions from Microsoft.
Windows DNA Architecture :















Windows DNA consists of three layers

1. The Presentation Layer

This layer is responsible for information gathering from user, performing basic validation of user input, sending it to the business layer and again receiving results of the business layer and presenting these results to the user in a viewable formats such as VB forms. This layer consists tools such as VB, HTML, DHTML, Win32 applications, Client-Server scripting, Java Applets, Netscape Plug-Ins, ActiveX controls

2. The Business Layer

This layer is responsible for receiving input from the presentation layer, interacting with the data access layer to process the information and sending back the processed information to the presentation layer. This layer provides business rules and services to help to write scalable applications. These services are tightly integrated with each other and the underlying operating system and exposed in a unified way through COM. They include the following:
Web services, through Microsoft Internet Information Server ( IIS ).
Transaction and component services, through Microsoft Transaction Server ( MTS ).
Queuing and asynchronous services, through Microsoft Message Queue Server ( MSMQ).
Server-side scripting, via Active Server Pages (ASP).
Interoperability services, such as the COM Transaction Integrator ( COMTI ) for accessing the IBM Customer Information Control System (CICS) and IBM Infromation Management Systems (MIS).
3. The Data Access Layer
This layer directly interact with the data which usually reside in the databases such as SQL Server or Oracle. This layer is responsible for storage, retrieval, and general maintenance of data as well integrity of data. The Windows DNA approach of data access is called "Universal Data Access'. UDA is a set of system level and application level programming models called OLE-DB, ADO and RDO.
COM+: Definition

In 1992, Microsoft evolved OLE ( Object linking and embedding ) which was named as COM ( 1995 ) later with new enhancements and features. In 1996, COM started supporting distributing computing and Microsoft named it as DCOM ( Distributed COM). At the same time Microsoft developed one new transaction server called Microsoft Distributed Transaction Coordinator (MDTC) which was enhanced to Microsoft Transaction Server (MTS) in 1997. In 1997, Microsoft developed one more server for queuing services called Microsoft Message Queue Server ( MSMQ). Now what??? After all these development, In 1999, Microsoft combined all these services in an integrated runtime environment which is called COM+. In other words, COM+ is an integrated environment which provides developers COM, MTS, MSMQ and some other services.

CS2251 -DESIGN AND ANALYSIS OF ALGORITHMS UNIT I QUESTIONS 1

16.            What do you mean by ²Best case-Efficiency” of an algorithm?

The ²Best case-Efficiency” of an algorithm is its efficiency for the Best-case input of size n, which is an input(or inputs)  of size n for which the algorithm runs the fastest among all possible inputs of that size.

    Ex: if you want to sort a list of numbers in ascending order when the numbers are given in ascending order. In this running time will be the smallest.

17.            Define the ²Average-case efficiency” of an algorithm?

The ²Average-case efficiency” of an algorithm is its efficiency for the  input of size n,  for which the algorithm runs between the best case and the worst case among all possible inputs of that size.

18.            What do you mean by “Amortized efficiency”?

The “Amortized efficiency” applies not only a single run of an algorithm but rather to a sequence of operations performed on the same data structure. It turns out that in some situations a single operation can be expensive ,but the total  time for an entire sequence of n such operations is always significantly better than the worst case efficiency of that single operation multiplied by n. This is known as “Amortized efficiency”.

19.            How to measure the algorithm’s efficiency?

It is logical to investigate the algorithm’s efficiency as a function of some parameter n indicating the algorithm’s input size.

Example: It will be the size of the list for problems of sorting, searching, finding the list’s smallest element, and most other problems dealing with lists.

20.            What is called the basic operation of an algorithm?

            The most important operation of the algorithm is the operation contributing the most to the total running time is called basic operation of an algorithm.

21.            How to measure an algorithm’s running time?

Let C_op be the time of execution of an algorithm’s basic iteration on a particular computer and let C (n) be the number of times this operation needs to be executed for this algorithm.  Then we can estimate the running time T(n) of a program implementing this algorithm on that computer by the formula

                                    T(n)   ≈  C_op C(n)

22.            Define order of growth.

            The efficiency analysis framework concentrates on the order of growth of an algorithm’s basic operation count as the principal indicator of the algorithm’s efficiency.  To compare and rank such orders of growth we use three notations

1)                                                      O (Big oh) notation

2)                                                      Ω (Big Omega) notation &

3)                                                      Θ (Big Theta) notation

23.            Define Big oh notation May/June 2006, April/May 2008

A function t(n) is said to be in O(g(n)) denoted t(n) ε O (g(n)), if t(n) is bounded above by some constant multiple of g(n) for all large n, i.e., if there exist some positive constant c and some non negative integer n₀ such that

                                    T (n) < c g (n) for n > n₀

CS2251 -DESIGN AND ANALYSIS OF ALGORITHMS UNIT I QUESTIONS

CS2251 -DESIGN AND ANALYSIS OF ALGORITHMS

UNIT –I

  1.                What is an Algorithm?  May/June 2006, Nov/Dec 2008

An algorithm is a sequence of unambiguous instructions for solving a problem, i.e., for obtaining a required output for any legitimate input in a finite amount of time

2.                State the Euclid’s algorithm for finding GCD of two given numbers.

ALGORITHM Euclid (m, n)

// Computes gcd(m,n) by Euclid’s algorithm

//Input   : Two nonnegative, not-both-zero integers m and n

//Output: Greatest common divisor of m and n

while n ¹ 0 do

                                    r ß m mod n

                                    m ß n

                                    n ß r

return m.

3.                What are Sequential Algorithms?

The central assumption of the RAM model is that instructions are executed one after another, one operation at a time. Accordingly, algorithms designed to be executed on such machines are called Sequential algorithms.

4.                What are Parallel Algorithms?

The central assumption of the RAM model does not hold for some newer computers that can execute operations concurrently, i.e., in parallel algorithms that take advantage of this capability are called Parallel algorithms.

5.                What is Exact and Approximation algorithm?

The principal decision to choose solving the problem exactly is called exact algorithm.

The principal decision to choose solving the problem approximately is called Approximation algorithm.

6.                What is Algorithm Design Technique?  Nov/Dec 2005

            An algorithm design technique is a general approach to solving problems algorithmically that is applicable to a variety of problems from different areas of computing.

7.                Define Pseudo code.

A Pseudo code is a mixture of a natural language and programming language like constructs. A pseudo code is usually more precise than a natural language, and its usage often yields more succinct algorithm descriptions.

CS2251 -DESIGN AND ANALYSIS OF ALGORITHMS PART B QUESTIONS

PART-B

I-UNIT

1. (a) Describe the steps in analyzing & coding an algorithm. (10)

    (b) Explain some of the problem types used in the design of

          algorithm. (6)

2.(a) Discuss the fundamentals of analysis framework . (10)

   (b) Explain the various asymptotic notations used in algorithm design. (6)

3. (a) Explain the general framework for analyzing the efficiency of algorithm. (8)

    (b) Explain the various Asymptotic efficiencies of an algorithm. (8)

4. (a) Explain the basic efficiency classes. (10)

    (b) Explain briefly the concept of algorithmic strategies. (6)

5. Describe briefly the notions of complexity of an algorithm. (16)

6. (a) What is Pseudo-code?Explain with an example. (8)

    (b) Find the complexity C(n) of the algorithm for the worst

     case,best case and average case.(Evaluate average case complexity for n=3,Where n is the   

     number of inputs) (8)

7. Set up & solve a recurrence relation for the number of key comparisons made by above  

     pseudo code. (4)

II-UNIT

1) Write a pseudo code for divide & conquer algorithm for merging two sorted arrays in to a single sorted one.Explain with example. (12)

2) Construct a minimum spanning tree using Kruskal’s algorithm with your own example. (10)

3) Explain about Knapsack Problem with example

4) Explain Dijikstra algorithm (8)

5) Define Spanning tree.Discuss design steps in Prim’s algorithm to construct minimum spanning   

    tree with an example. (16)

6)Explain Kruskal’s algorithm. (8)

7) Explain about binary search with example.

CS2251 -DESIGN AND ANALYSIS OF ALGORITHMS UNIT V QUESTIONS

UNIT V

1) Define tractable and intractable problems

Problems that can be solved in polynomial time are called tractable problems, problems that cannot be solved in polynomial time are called intractable problems.

2) Explain the theory of computational complexity

A problem's intractability remains the same for all principal models of computations and all reasonable input encoding schemes for the problem under consideration

3)Explain class P problems

Class P is a class of decision problems that can be solved in polynomial time by(deterministic) algorithms. This class of problems is called polynomial.

4)Explain undecidable problems

If the decision problem cannot be solved in polynomial time, and if the decision problems cannot be solved at all by any algorithm. Such problems are called Undecidable.

5) Explain the halting problem

Given a computer program and an input to it,determine whether the program will halt on that input or continue working indefinitely on it.