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Tuesday, 14 February 2012

Entity-Relationship-Attribute Level







The analysis at the third, or Entity-Relationship-Attribute, level combines the work of the framework level with that of the classification model and the data event maps by adding characteristic groupings determined inductively into a classification structure determined deductively, and by adding characteristics and or attributes to both the entities and the relationships. A characteristic or attribute is represented by a circle attached directly to the entity or the relationship which it describes. The circle contains the name of the attribute. Attributes might be: identification information, residence information, physical description, inventory status, packaging information, hobbies, clothing sizes, etc.

The attribute names for an employee entity, might be very similar to the section or item headings on an employment application, or the section or item headings on the permanent employee record form. For a customer, they might be very similar to the section headings on a new account opening form, or on the customer record form.

For an entity, each attribute represents some grouping of data which is necessary, from a business perspective, to describe a physical or logical characteristic of the entity, or to describe some activity of the entity. For a relationship, each attribute represents some grouping of data which is necessary from a business perspective, to describe, qualify or maintain the named relationship between two entities.



The Entity-Relationship-Attribute model is an expansion of the Entity-Relationship model. Until this point the models have only identified the entities and relationships by name and context. For a given entity or relationship little is known about them other than their name, the obvious fact of their existence, and the fact that the firm is interested in them.

At the Entity-Relationship-Attribute level, entities and relationships are described in terms of their attributes, or characteristics. In other words, beyond knowing that the entity exists, we must also know what the entity looks like, how it is identified, and what it does. These descriptors or characteristics are called attributes. An attribute is thus any distinct aspect of the entity or relationship that is necessary to describe the entity or to qualify the relationship. The full description of an entity or relationship consists of the full set of attributes which describe that entity or relationship.

For an entity attribute to be significant it must relate directly to the entity, be completely dependent on the entity for its existence and meaning, and it must be definable in terms of one or more data elements. It is immaterial as to whether there are one or more data elements in an attribute, as long as the attribute applies to all instances of the entity being represented. Seen another way, an attribute is some distinct category of mutually related data, the sum of which describes something of interest about the entity. The identifiers (unique or otherwise) of an entity are a special form of attribute.


Entity attributes represent:

A physical characteristic of that entity - size, shape, weight or color
An historical attribute - date of birth or date of hire
A location attribute - place of residence, place of work, or place of birth - A non-physical characteristic - price
An identifier - name, title
An occupational characteristic - current position, skill possessed, training received, educational courses, etc.
The intermediate or final results of some processing activities related to the entity
Data which relates to some current state or condition of the entity, or to some past or future state or condition of the entity
Data which relates to some current action taken by or against the entity, or to some past or future action taken by or against the entity
For an relationship attribute to be significant it must relate directly to the relationship, be completely dependent on the relationship for its existence and meaning, and it must be definable in terms of one or more data elements. It is immaterial as to whether there are one or more data elements in an attribute, as long as the attribute applies to all instances of the entity or relationship being represented.

Seen another way, an attribute is some distinct category of mutually related data, the sum of which describes something of interest, or some qualifier about the relationship between two entities. A relationship attribute must be dependent upon the connection between both entities and should be incapable of existence in the absence of that relationship. The minimum attributes of a relationship are the necessary identifiers of each entity of the related pair.

Relationship attributes represent some descriptor or qualifier of the relationship such as:

An historical attribute - date of marriage, or date of sale, date of storage
An location attribute - place of storage, place of work, or place of birth
A non-physical characteristic - price at time of sale, discount at time of sale, grade in course
Some meaningful data which is not an attribute of either entity participating in the relationship, but pertains only to the relationship between them. This data is sometimes called intersection data.
It is possible for the same named attribute to be used to describe many different entities and relationships. Identifier attributes in particular describe both the entities and the relationships between them.


Regards,

Ruchika Mandore  [ MCA ] 
Software Engineer
AeroSoft Corp

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Get Best Study material for following 

COMPUTER STORAGE DEVICES MANUFACTURERS AND SUPPLIERS








Card Reader
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Computer data storage, often called storage or memory, refers to computer components and recording media that retain digital data. Data storage is a core function and fundamental component of computers.
In contemporary usage, 'memory' usually refers to semiconductor storage read-write random-access memory, typically DRAM (Dynamic-RAM). Memory can refer to other forms of fast but temporary storage. Storage refers to storage devices and their media not directly accessible by the CPU, (secondary or tertiary storage), typically hard disk drives, optical disc drives, and other devices slower than RAM but not volatile (retaining contents when powered down).[1] Historically, memory has been called core, main memory, real storage or internal memory while storage devices have been referred to as secondary storage, external memory or auxiliary/peripheral storage.
The distinctions are fundamental to the architecture of computers. The distinctions also reflect an important and significant technical difference between memory and mass storage devices, which has been blurred by the historical usage of the term storage. Nevertheless, this article uses the traditional nomenclature.
Many different forms of storage, based on various natural phenomena, have been invented. So far, no practical universal storage medium exists, and all forms of storage have some drawbacks. Therefore a computer system usually contains several kinds of storage, each with an individual purpose.











Regards,

Ruchika Mandore  [ MCA ] 
Software Engineer
AeroSoft Corp

On Line Assistence  :
Gtalk                    :   ruchika.aerosoft@gmail.com
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Sunday, 12 February 2012

Architecture of a DBMS


Descripe the architecture of a DBMS in terms of 3 schema architecture?





We now discuss a conceptual framework for a DBMS. Several different frameworks have been suggested over the last several years. For example, a framework may be developed based on the functions that the various components of a DBMS must provide to its users. It may also be based on different views of data that are possible within a DBMS. We consider the latter approach.
A commonly used views of data approach is the three-level architecture suggested by ANSI/SPARC (American National Standards Institute/Standards Planning and Requirements Committee). ANSI/SPARC produced an interim report in 1972 followed by a final report in 1977. The reports proposed an architectural framework for databases. Under this approach, a database is considered as containing data about an enterprise. The three levels of the architecture are three different views of the data:
External - individual user view
Conceptual - community user view
Internal - physical or storage view


The three level database architecture allows a clear separation of the information meaning (conceptual view) from the external data representation and from the physical data structure layout. A database system that is able to separate the three different views of data is likely to be flexible and adaptable. This flexibility and adaptability is data independence that we have discussed earlier.
We now briefly discuss the three different views.
The external level is the view that the individual user of the database has. This view is often a restricted view of the database and the same database may provide a number of different views for different classes of users. In general, the end users and even the applications programmers are only interested in a subset of the database. For example, a department head may only be interested in the departmental finances and student enrolments but not the library information. The librarian would not be expected to have any interest in the information about academic staff. The payroll office would have no interest in student enrolments.
The conceptual view is the information model of the enterprise and contains the view of the whole enterprise without any concern for the physical implemenation. This view is normally more stable than the other two views. In a database, it may be desirable to change the internal view to improve performance while there has been no change in the conceptual view of the database. The conceptual view is the overall community view of the database and it includes all the information that is going to be represented in the database. The conceptual view is defined by the conceptual schema which includes definitions of each of the various types of data.
The internal view is the view about the actual physical storage of data. It tells us what data is stored in the database and how. At least the following aspects are considered at this level:
Storage allocation e.g. B-trees, hashing etc.
Access paths e.g. specification of primary and secondary keys, indexes and pointers and sequencing.
Miscellaneous e.g. data compression and encryption techniques, optimisation of the internal structures.


Efficiency considerations are the most important at this level and the data structures are chosen to provide an efficient database. The internal view does not deal with the physical devices directly. Instead it views a physical device as a collection of physical pages and allocates space in terms of logical pages.
The separation of the conceptual view from the internal view enables us to provide a logical description of the database without the need to specify physical structures. This is often called physical data independence. Separating the external views from the conceptual view enables us to change the conceptual view without affecting the external views. This separation is sometimes called logical data independence.
Assuming the three level view of the database, a number of mappings are needed to enable the users working with one of the external views. For example, the payroll office may have an external view of the database that consists of the following information only:
Staff number, name and address.
Staff tax information e.g. number of dependents.
Staff bank information where salary is deposited.
Staff employment status, salary level,leave information etc.


The conceptual view of the database may contain academic staff, general staff, casual staff etc. A mapping will need to be created where all the staff in the different categories are combined into one category for the payroll office. The conceptual view would include information about each staff's position, the date employment started, full-time or part-time, etc etc. This will need to be mapped to the salary level for the salary office. Also, if there is some change in the conceptual view, the external view can stay the same if the mapping is changed.










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Regards,

Ruchika Mandore  [ MCA ] 
Software Engineer
AeroSoft Corp

On Line Assistence  :
Gtalk                    :   ruchika.aerosoft@gmail.com
Y! Messenger             :   ruchika.aerosoft@yahoo.com
Rediff Bol               :   ruchika.aerosoft@rediffmail.com
MSN                      :   ruchika.aerosoft@hotmail.com

Saturday, 11 February 2012

E-R Model Concept







The ER data modeling techniques is based on the perception of a real world that consists of a set of basic objects called entities, and of relationships among these objects. In ER modeling, data is described as entities, relationships, and attributes. In the following section, entities and attributes are discussed. Later, entity types, their key attributes, relationship types, their structural constraints, and weak entity types are discussed. In the last, we will apply ER modeling to our case study problem "Library management system".

ENTITIES AND ATTRIBUTES

One of the basic components of ER model is entity. An entity is any distinguishable object about which information is stored. These objects can be person, place, thing, event or a concept. Entities contain descriptive information. Each entity is distinct.
An entity may be physical or abstract. A person, a book, car, house, employee etc. are all physical entities whereas a company, job, or a university course, are abstract entities.



Physical and Abstract Entity
Fig 7.6 - Physical and Abstract Entity
Another classification of entities can be independent or dependent (strong or weak) entity.
ntities are classified as independent or dependent (in some methodologies, the terms used are strong and weak, respectively). An independent entity is one, which does not rely on another entity for identification. A dependent entity is one that relies on another entity for identification. An independent entity exists on its own whereas dependent entity exists on the existence of some other entity. For example take an organization scenario. Here department is independent entity. Department manager is a dependent entity. It exists for existing depts. There won't be any department manager for which there is no dept.
Some entity types may not have any key attributes of their own. These are called weak entity types. Entities belonging to a weak entity type are identified by being related to specific entities from another entity type in combination with some of their attribute values. For example, take the license entity. It can't exist unless it is related to a person entity.

Attributes

After you identify an entity, then you describe it in real terms, or through its attributes. Attributes are basically properties of entity. We can use attributes for identifying and expressing entities. For example, Dept entity can have DeptName, DeptId, and DeptManager as its attributes. A car entity can have modelno, brandname, and color as its attributes.
A particular instance of an attribute is a value. For example, "Bhaskar" is one value of the attribute Name. Employee number 8005 uniquely identifies an employee in a company.
The value of one or more attributes can uniquely identify an entity.
Entity and its attributes
Fig 7.7 - Entity and its attributes
In the above figure, employee is the entity. EmpNo, Name, Designation and Department are its attributes.
An entity set may have several attributes. Formally each entity can be described by set of <attribute, data value> pairs.
Employee entity and its attribute values
Fig 7.8 - Employee entity and its attribute values


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Thursday, 9 February 2012

Planning is Key in Corporate Fraud Risk Management






Joseph Dooley is a forensic accountant who spent 21 years with the FBI. Most of that time he investigated white-collar crime—including financial institution fraud and asset misappropriation—and he also started the bureau’s computer crimes squad. The fallout from fraud can have a range of consequences for an organization, from financial losses and reputational damage to government penalties. 




During economically tough times, says Dooley, mounting pressure on both individuals and organizations can lead to acts of fraud. Now managing director at digital risk-management firm Stroz Friedberg, where he heads the forensic accounting practice, Dooley tells CorpCounsel.com how an organization can both prevent and respond to acts of fraud. An edited version of that conversation follows.

CorpCounsel: How is the law department of an organization involved in a fraud case?
Joseph Dooley: The general counsel’s office would typically be involved in every response to a fraud scheme—and what I mean by response is an investigation of some kind. And the general counsel’s office should be involved from the beginning in the prevention of fraud by working with management to develop appropriate polices and procedures; a code of conduct and related standards; the rules with respect to employee and third-party due diligence—are we as an organization going to conduct a background investigation of every vendor and employee that we engage for our organization, and what are the legal ramifications for that? 

CC: It’s legal to conduct employee background checks?
JD: Each state has its own laws on how far an organization can go with respect to conducting background checks. The general counsel’s office for any organization should be familiar with the relevant laws that exist within the state that they operate in before they go down that path. But most organizations do some kind of background check on their employees before they’re hired. That’s definitely a critical part of a prevention program.

CC: How do you incorporate forensic accounting into monitoring for fraud?
JD: Years ago, accountants and forensic accountants would sample data. We would look at a certain set of transactions and see if there’s any type of fraud, waste, or errors that occur with respect to transactions. Nowadays, we have the technological ability to look at 100 percent of the data.

For example, we can look at transactions that flow out of a procurement department. We can look at payments to certain vendors, and bring in third-party or other internal information, such as a taxpayer identification number or social security number. You can compare those numbers against your HR data. Do we have vendors that share the same social security number as one of our employees? Do we have vendors that share the same address as one of our employees?

CC: How should an organization respond to fraud if it does occur?
JD: No matter how robust your fraud risk-management program is, every organization is going to get hit with some type of fraud. You should think about a response plan before you need to execute that plan.

Once you need to execute the plan—what are the protocols for your organization? Who’s going to be in charge of the investigation? That will depend on a number of factors. Is it a low-level fraud, conducted by the accounts payable clerk or the accounts receivable clerk? Or is it a financial reporting fraud conducted by the chief financial officer? That would determine who gets assigned to that investigation. If it’s senior management, your audit committee should be aware of and fully engaged in that investigation. 

Then, is it a criminal matter that we would consider self-reporting to the Justice Department and the SEC? Again, those are issues that an organization should discuss with its outside counsel. Is it something we’re going to handle internally? Do we need third-party assistance, or are we going to handle it ourselves?

CC: And once the dust has settled? 
JD: You look at fraud and say: What happened? How did they breach our defenses? You adjust and modify whatever controls you have in place. You add that to your communication and training of your employees, and you maintain a consistent disciplinary process against employees, even if it’s your top salesman. Otherwise, your employees will see that and think: If I’m a big seller, I get a pass. So consistent disciplinary procedures are critical, and it sets the tone within an organization.