Access Controls An access control system

Misconception

In computing, a protocol is a set of rules which is used by computers to communicate with each other across a network. A protocol is a convention or standard that controls or enables the connection, communication, and data transfer between computing endpoints. In its simplest form, a protocol can be defined as the rules governing the syntax, semantics, and synchronization of communication. Protocols may be implemented by hardware, software, or a combination of the two. At the lowest level, a protocol defines the behavior of a hardware connection.

While protocols can vary greatly in purpose and sophistication, most specify one or more of the following properties:^{[citation needed]}

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  Detection of the underlying physical connection (wired or wireless), or the existence of the other endpoint or node
  
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  Handshaking
  
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  Negotiation of various connection characteristics
  
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  How to start and end a message
  
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  Procedures on formatting a message
  
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  What to do with corrupted or improperly formatted messages (error correction)
  
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  How to detect unexpected loss of the connection, and what to do next
  
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  Termination of the session and/or connection.
  

Although other network technologies exist, Ethernet has achieved near-ubiquity since the mid-1990s. Every Ethernet network card has a unique 48-bit serial number called a MAC address, which is stored in ROM carried on the card. Every computer on an Ethernet network must have a card with a unique MAC address. Normally it is safe to assume that no two network cards will share the same address, because card vendors purchase blocks of addresses from the Institute of Electrical and Electronics Engineers (IEEE) and assign a unique address to each card at the time of manufacture.

Whereas network cards used to be expansion cards that plug into a computer bus, the low cost and ubiquity of the Ethernet standard means that most newer computers have a network interface built into the motherboard. These either have Ethernet capabilities integrated into the motherboard chipset or implemented via a low cost dedicated Ethernet chip, connected through the PCI (or the newest PCI Express) bus. A separate network card is not required unless multiple interfaces are needed or some other type of network is used. Newer motherboards may even have dual network (Ethernet) interfaces built-in.

The card implements the electronic circuitry required to communicate using a specific physical layer and data link layer standard such as Ethernet or token ring. This provides a base for a full network protocol stack, allowing communication among small groups of computers on the same LAN and large-scale network communications through routable protocols, such as IP.

In data communication, a physical network node may either be a data circuit-terminating equipment (DCE) such as a modem, hub, bridge or switch; or a data terminal equipment (DTE) such as a digital telephone handset, a printer or a host computer, for example a router, a workstation or a server.

If the network in question is a LAN or WAN, every LAN or WAN node (that are at least data link layer devices) must have a MAC address. Examples are computers, packet switches and ADSL modem (with Ethernet interface). Note that a hub constitutes a physical network node, but not a LAN node in this sense, since a hubbed network logically is a bus network. Analogusly, a repeater or PSTN modem (with serial interface) are physical network nodes but not LAN nodes in this sense.

If the network in question is the Internet, many physical network nodes are host computers, also known as Internet nodes, identified by an IP address, and all hosts are physical network nodes. However, datalink layer devices such as switches, bridges and WLAN access points do not have an IP host address (except sometimes for administrative purposes), and are not considered as Internet nodes, but as physical network nodes or LAN nodes.

If the network in question is a distributed system, the nodes are clients, servers or peers. In a peer-to-peer or overlay network, nodes that actively route data for the other networked devices as well as themselves are called supernodes.

Normalization

In the field of relational database design, normalization is a systematic way of ensuring that a database structure is suitable for general-purpose querying and free of certain undesirable characteristics—insertion, update, and deletion anomalies—that could lead to a loss of data integrity.^[1] E.F. Codd, the inventor of the relational model, introduced the concept of normalization and what we now know as the First Normal Form (1NF) in 1970.^[2] Codd went on to define the Second Normal Form (2NF) and Third Normal Form (3NF) in 1971,^[3] and Codd and Raymond F. Boyce defined the Boyce-Codd Normal Form in 1974.^[4] Higher normal forms were defined by other theorists in subsequent years, the most recent being the Sixth Normal Form (6NF) introduced by Chris Date, Hugh Darwen, and Nikos Lorentzos in 2002.^[5]

Informally, a relational database table (the computerized representation of a relation) is often described as "normalized" if it is in the Third Normal Form.^[6] Most 3NF tables are free of insertion, update, and deletion anomalies, i.e. in most cases 3NF tables adhere to BCNF, 4NF, and 5NF (but typically not 6NF).

A standard piece of database design guidance is that the designer should create a fully normalized design; selective denormalization can subsequently be performed for performance reasons.^[7] However, some modeling disciplines, such as the dimensional modeling approach to data warehouse design, explicitly recommend non-normalized designs, i.e. designs that in large part do not adhere to 3NF.^[8]

Object-oriented programming has roots that can be traced to the 1960s. As hardware and software became increasingly complex, quality was often compromised. Researchers studied ways to maintain software quality and developed object-oriented programming in part to address common problems by strongly emphasizing discrete, reusable units of programming logic. The methodology focuses on data rather than processes, with programs composed of self-sufficient modules (objects) each containing all the information needed to manipulate its own data structure. This is in contrast to the existing modular programming which had been dominant for many years that focused on the function of a module, rather than specifically the data, but equally provided for code reuse, and self-sufficient reusable units of programming logic, enabling collaboration through the use of linked modules (subroutines). This more conventional approach, which still persists, tends to consider data and behavior separately.

An object-oriented program may thus be viewed as a collection of interacting objects, as opposed to the conventional model, in which a program is seen as a list of tasks (subroutines) to perform. In OOP, each object is capable of receiving messages, processing data, and sending messages to other objects and can be viewed as an independent 'machine' with a distinct role or responsibility. The actions (or "operators") on these objects are closely associated with the object. For example, the data structures tend to 'carry their own operators around with them' (or at least "inherit" them from a similar object or class).

Online Analytical Processing (OLAP)

Online analytical processing, or OLAP (pronounced /ˈoʊlæp/), is an approach to quickly answer multi-dimensional analytical queries.^[1] OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.^[2] The typical applications of OLAP are in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas. The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).^[3]

Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time. They borrow aspects of navigational databases and hierarchical databases that are faster than relational databases.^[4]

The output of an OLAP query is typically displayed in a matrix (or pivot) format. The dimensions form the rows and columns of the matrix; the measures form the values.

At the core of any OLAP system is the concept of an OLAP cube (also called a multidimensional cube or a hypercube). It consists of numeric facts called measures which are categorized by dimensions. The cube metadata is typically created from a star schema or snowflake schema of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.

Each measure can be thought of as having a set of labels, or meta-data associated with it. A dimension is what describes these labels; it provides information about the measure.

A simple example would be a cube that contains a store's sales as a measure, and Date/Time as a dimension. Each Sale has a Date/Time label that describes more about that sale.

Any number of dimensions can be added to the structure such as Store, Cashier, or Customer by adding a column to the fact table. This allows an analyst to view the measures along any combination of the dimensions.

Online Realtime Processing (OLRT)

There are a number of differences between real-time and batch processing. These are outlined below:

Each transaction in real-time processing is unique. It is not part of a group of transactions, even though those transactions are processed in the same manner. Transactions in real-time processing are stand-alone both in the entry to the system and also in the handling of output.

Real-time processing requires the master file to be available more often for updating and reference than batch processing. The database is not accessible all of the time for batch processing.

Real-time processing has fewer errors than batch processing, as transaction data is validated and entered immediately. With batch processing, the data is organised and stored before the master file is updated. Errors can occur during these steps.

Infrequent errors may occur in real-time processing; however, they are often tolerated. It is not practical to shut down the system for infrequent errors.

More computer operators are required in real-time processing, as the operations are not centralised. It is more difficult to maintain a real-time processing system than a batch processing system.