Segmentation:
Segmentation is a memory management scheme which supports programmer’s view of memory. Programmers never think of their programs as a linear array of works. Rather, the think of their programs as a collection of logically related entities, such as subroutines or procedures, functions, global or local data areas, stack etc., as depicted in following figure.
Address mapping in Segment system:
An important component of address mapping in a segmented system is a segment table.
A virtual (logical) address consists of two parts: a segment number and an offset into that segment. The Segment number provides in the virtual address is used as in index into the segment table. Each row of the segment table contains a starting address (base address) of Segment and a size of the segment. The offset of the virtual address must be within (less than or equal to) the size of the segment. If the offset of virtual address is not within the range, it is trapped by the operating System otherwise the offset is added to the base address of the segment to produce physical address of the desired segment.
Wednesday, June 15, 2011
Monday, June 13, 2011
Difference between RDBMS and OODBMS
RDBMS is Relational Database Management system which is based on establishing relationship among tables is purely based on mathematics. OODBMS is Object Oriented Database Management System which is based on the concept of object. RDMBSs were never designed to allow for the nested structure. These types of applications are extensively found CAD/CAF, aerospace etc. OODMBS can easily support these applications. Moreover, it is much easier and natural to navigate through these complex structures in form of objects that model the real world in OODBMS rather than table, tuples and records in RDBMS. It is hard to confuse a relational database with an object-oriented database. The normalized relational model is based on the fairly elegant mathematical theory. Relational database drive a virtual structure at run time based on values from sets of data stored in tables. Databases construct views of the data by selecting data from multiple tables and loading it into a single table (ODBMs traverse the data from object to object.
Relational database have a limited number if simple, built-in data types, such as integer and string, and a limited number of built-in operations that can handle these data types. You can create complex data types in relational database, but you must do it on a linear basis, such as combing fields into records. And the operations on these new complex types are restricted, again, to those define for the basic types (as opposed to arbitrary data types or sub-classing with inheritance as found in OODMs).
The object model supports browsing of object class libraries, which allows the reuse, rather than the reinvention of, of commonly used data elements. Objects in an OODB survive multiple sessions they are persistent. If you delete an object stored in a relational database, other objects may be left with references to the deleted one and may now be incorrect. The integrity of the data thus becomes suspect and creates inconsistencies versions.
In the relational database, complex objects must be broken up and stored in separate tables. This can only be done in a sequential procedure with the next retrieval replying on the outcome of the previous. The relational database does not understand a global request and thus cannot optimize multiple requests; OODBs can issue a single message that contains multiple transactions.
The relational model, however, suffers at least one major disadvantage. It is difficult to express the semantics of complex objects with only a table model for data storage. Although relational databases are adequate for accounting or other typical transaction-processing applications where the data types are simple and few in number, the relational model offers limited help when data types become numerous and complex.
Object-oriented databases are favored for applications where the relationships among elements in the database carry the key information. That is, object-oriented models capture the structure of the data; relational models organize the data itself. If a record can be understood is isolation, then the relational databases is probably suitable. If a record makes sense only in the context of other records, then an object-oriented database is more appropriate.
Relational database have a limited number if simple, built-in data types, such as integer and string, and a limited number of built-in operations that can handle these data types. You can create complex data types in relational database, but you must do it on a linear basis, such as combing fields into records. And the operations on these new complex types are restricted, again, to those define for the basic types (as opposed to arbitrary data types or sub-classing with inheritance as found in OODMs).
The object model supports browsing of object class libraries, which allows the reuse, rather than the reinvention of, of commonly used data elements. Objects in an OODB survive multiple sessions they are persistent. If you delete an object stored in a relational database, other objects may be left with references to the deleted one and may now be incorrect. The integrity of the data thus becomes suspect and creates inconsistencies versions.
In the relational database, complex objects must be broken up and stored in separate tables. This can only be done in a sequential procedure with the next retrieval replying on the outcome of the previous. The relational database does not understand a global request and thus cannot optimize multiple requests; OODBs can issue a single message that contains multiple transactions.
The relational model, however, suffers at least one major disadvantage. It is difficult to express the semantics of complex objects with only a table model for data storage. Although relational databases are adequate for accounting or other typical transaction-processing applications where the data types are simple and few in number, the relational model offers limited help when data types become numerous and complex.
Object-oriented databases are favored for applications where the relationships among elements in the database carry the key information. That is, object-oriented models capture the structure of the data; relational models organize the data itself. If a record can be understood is isolation, then the relational databases is probably suitable. If a record makes sense only in the context of other records, then an object-oriented database is more appropriate.
Saturday, October 23, 2010
List the different operations that makeup method of sciences with the help of an example,explain these operations?
These are the various mental and physical operations that make up the methods of science:
1. Observation/Research
2. Hypothesis
3. Prediction
4. Experimentation
5. Conclusion
The observation is done first so that you know how you want to go about your research. The hypothesis is the answer you think you'll find. The prediction is your specific belief about the scientific idea: If my hypothesis is true, then I predict we will discover..... The experiment is the tool that you invent to answer the question, and the conclusion is the answer that the experiment gives. Don't worry, it isn't that complicated. Let's look at each one of these points individually so that you can understand the tools that scientists use when doing their own science projects and use them for your project.
1. OBSERVATION
This step could also be called "research." It is the first stage in understanding the problem you have chosen. After you decide on your area of science and the specific question you want to ask, you will need to research everything that you can find about the problem. You can collect information on your science fair topic from your own experiences, books, the internet, or even smaller "unofficial" experiments. This initial research should play a big part in the science fair idea that you finally choose. Let's take the example of the tomatoes in the garden. You like to garden, and notice that some tomatoes are bigger than others and wonder why. Because of this personal experience and an interest in the problem, you decide to learn more about what makes plants grow.
For this stage of the Scientific Method, it's important to use as many sources as you can find. The more information you have on your science fair project topic, the better the design of your experiment is going to be, and the better your science fair project is going to be overall. Also try to get information from your teachers or librarians, or professionals who know something about your science fair topic. They can help to guide you to a solid experimental setup.
2. HYPOTHESIS
The next stage of the Scientific Method is known as the "hypothesis." This word basically means "a possible solution to a problem, based on knowledge and research." The hypothesis is a simple statement that defines what you think the outcome of your experiment will be. All of the first stage of the Scientific Method -- the observation, or research stage -- is designed to help you express a problem in a single question ("Does the amount of sunlight in a garden affect tomato size?") and propose an answer to the question based on what you know. The experiment that you will design is done to test the hypothesis.
Using the example of the tomato experiment, here is an example of a hypothesis:
TOPIC: "Does the amount of sunlight a tomato plant receives affect the size of the tomatoes?"
HYPOTHESIS: "I believe that the more sunlight a tomato plant receives, the larger its tomatoes will grow. This hypothesis is based on:
(1) Tomato plants need sunshine to make food through photosynthesis, and logically, more sun means more food, and;
(2) Through informal, exploratory observations of plants in a garden, those with more sunlight appear to grow bigger.
3. PREDICTION
The hypothesis is your general statement of how you think the scientific phenomenon in question works. Your prediction lets you get specific -- how will you demonstrate that you hypothesis is true? The experiment that you will design is done to test the prediction.
An important thing to remember during this stage of the scientific method is that once you develop a hypothesis and a prediction, you shouldn't change it, even if the results of your experiment show that you were wrong. An incorrect prediction doesn't mean that you "failed." It just means that the experiment brought some new facts to light that maybe you hadn't thought about before. The judges at your science fair will not take points off simply because your results don't match up with your hypothesis.
Continuing our tomato plant example, a good prediction would be: Increasing the amount of sunlight tomato plants in my experiment receive will cause an increase in their size compared to identical plants that received the same care but less light.
4. EXPERIMENT
This is the part of the scientific method that tests your hypothesis. An experiment is a tool that you design to find out if your ideas about your topic are right or wrong. It is absolutely necessary to design a science fair experiment that will accurately test your hypothesis. The experiment is the most important part of the scientific method. It's the logical process that lets scientists learn about the world. In the next section, we'll discuss the ways that you can go about designing a science fair experiment idea.
5. CONCLUSION
The final step in the scientific method is the conclusion. This is a summary of the experiment's results, and how those results match up to your hypothesis.
You have two options for your conclusions: based on your results, either you can reject the hypothesis, or you can not reject the hypothesis. This is an important point. You can not PROVE the hypothesis with a single experiment, because there is a chance that you made an error somewhere along the way. What you can say is that your results SUPPORT the original hypothesis.
If your original hypothesis didn't match up with the final results of your experiment, don't change the hypothesis. Instead, try to explain what might have been wrong with your original hypothesis. What information did you not have originally that caused you to be wrong in your prediction? What are the reasons that the hypothesis and experimental results didn't match up?
Remember, a science fair experiment isn't a failure if it proves your hypothesis wrong or if your prediction isn't accurate. No one will take points off for that. A science fair experiment is only a failure if its design is flawed. A flawed experiment is one that (1) doesn't keep its variables under control, and (2) doesn't sufficiently answer the question that you asked of it.
1. Observation/Research
2. Hypothesis
3. Prediction
4. Experimentation
5. Conclusion
The observation is done first so that you know how you want to go about your research. The hypothesis is the answer you think you'll find. The prediction is your specific belief about the scientific idea: If my hypothesis is true, then I predict we will discover..... The experiment is the tool that you invent to answer the question, and the conclusion is the answer that the experiment gives. Don't worry, it isn't that complicated. Let's look at each one of these points individually so that you can understand the tools that scientists use when doing their own science projects and use them for your project.
1. OBSERVATION
This step could also be called "research." It is the first stage in understanding the problem you have chosen. After you decide on your area of science and the specific question you want to ask, you will need to research everything that you can find about the problem. You can collect information on your science fair topic from your own experiences, books, the internet, or even smaller "unofficial" experiments. This initial research should play a big part in the science fair idea that you finally choose. Let's take the example of the tomatoes in the garden. You like to garden, and notice that some tomatoes are bigger than others and wonder why. Because of this personal experience and an interest in the problem, you decide to learn more about what makes plants grow.
For this stage of the Scientific Method, it's important to use as many sources as you can find. The more information you have on your science fair project topic, the better the design of your experiment is going to be, and the better your science fair project is going to be overall. Also try to get information from your teachers or librarians, or professionals who know something about your science fair topic. They can help to guide you to a solid experimental setup.
2. HYPOTHESIS
The next stage of the Scientific Method is known as the "hypothesis." This word basically means "a possible solution to a problem, based on knowledge and research." The hypothesis is a simple statement that defines what you think the outcome of your experiment will be. All of the first stage of the Scientific Method -- the observation, or research stage -- is designed to help you express a problem in a single question ("Does the amount of sunlight in a garden affect tomato size?") and propose an answer to the question based on what you know. The experiment that you will design is done to test the hypothesis.
Using the example of the tomato experiment, here is an example of a hypothesis:
TOPIC: "Does the amount of sunlight a tomato plant receives affect the size of the tomatoes?"
HYPOTHESIS: "I believe that the more sunlight a tomato plant receives, the larger its tomatoes will grow. This hypothesis is based on:
(1) Tomato plants need sunshine to make food through photosynthesis, and logically, more sun means more food, and;
(2) Through informal, exploratory observations of plants in a garden, those with more sunlight appear to grow bigger.
3. PREDICTION
The hypothesis is your general statement of how you think the scientific phenomenon in question works. Your prediction lets you get specific -- how will you demonstrate that you hypothesis is true? The experiment that you will design is done to test the prediction.
An important thing to remember during this stage of the scientific method is that once you develop a hypothesis and a prediction, you shouldn't change it, even if the results of your experiment show that you were wrong. An incorrect prediction doesn't mean that you "failed." It just means that the experiment brought some new facts to light that maybe you hadn't thought about before. The judges at your science fair will not take points off simply because your results don't match up with your hypothesis.
Continuing our tomato plant example, a good prediction would be: Increasing the amount of sunlight tomato plants in my experiment receive will cause an increase in their size compared to identical plants that received the same care but less light.
4. EXPERIMENT
This is the part of the scientific method that tests your hypothesis. An experiment is a tool that you design to find out if your ideas about your topic are right or wrong. It is absolutely necessary to design a science fair experiment that will accurately test your hypothesis. The experiment is the most important part of the scientific method. It's the logical process that lets scientists learn about the world. In the next section, we'll discuss the ways that you can go about designing a science fair experiment idea.
5. CONCLUSION
The final step in the scientific method is the conclusion. This is a summary of the experiment's results, and how those results match up to your hypothesis.
You have two options for your conclusions: based on your results, either you can reject the hypothesis, or you can not reject the hypothesis. This is an important point. You can not PROVE the hypothesis with a single experiment, because there is a chance that you made an error somewhere along the way. What you can say is that your results SUPPORT the original hypothesis.
If your original hypothesis didn't match up with the final results of your experiment, don't change the hypothesis. Instead, try to explain what might have been wrong with your original hypothesis. What information did you not have originally that caused you to be wrong in your prediction? What are the reasons that the hypothesis and experimental results didn't match up?
Remember, a science fair experiment isn't a failure if it proves your hypothesis wrong or if your prediction isn't accurate. No one will take points off for that. A science fair experiment is only a failure if its design is flawed. A flawed experiment is one that (1) doesn't keep its variables under control, and (2) doesn't sufficiently answer the question that you asked of it.
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