Bluetooth

BLUETOOTH

What is Bluetooth?Bluetooth is an industrial specification for wireless personal area networks (PANs). Bluetooth provides a way to connect and exchange information between devices such as mobile phones, laptops, PCs, printers, digital cameras, and video game consoles over a secure, globally unlicensed short-range radio frequency. The Bluetooth specifications are developed and licensed by the Bluetooth Special Interest Group.Bluetooth was named after a late tenth century king, Harald Bluetooth, King of Denmark and Norway. He is known for his unification of previously warring tribes from Denmark (including now Swedish Scania, where the Bluetooth technology was invented), and Norway. Bluetooth likewise was intended to unify different technologies, such as computers and mobile phones.
Usage and application of BluetoothBluetooth is a standard and communications protocol primarily designed for low power consumption, with a short range (power-class-dependent: 1 meter, 10 meters, 100 meters) based on low-cost transceiver microchips in each device.
Bluetooth enables these devices to communicate with each other when they are in range. The devices use a radio communications system, so they do not have to be in line of sight of each other, and can even be in other rooms, as long as the received transmission is powerful enough.
Application listMore prevalent applications of Bluetooth include:
Wireless control of and communication between a mobile phone and a hands-free headset or car kit. This was one of the earliest applications to become popular.
Wireless networking between PCs in a confined space and where little bandwidth is required.
Wireless communications with PC input and output devices, the most common being the mouse, keyboard and printer.
Transfer of files between devices with OBEX.
Transfer of contact details, calendar appointments, and reminders between devices with OBEX.
Replacement of traditional wired serial communications in test equipment, GPS receivers, medical equipment, bar code scanners, and traffic control devices.
For controls where infrared was traditionally used.
Sending small advertisements from Bluetooth enabled advertising hoardings to other, discoverable, Bluetooth devices.
Two seventh-generation game consoles—Nintendo's Wii and Sony's PlayStation 3—use Bluetooth for their respective wireless controllers.
Dial-up internet access on personal computer or PDA using a data-capable mobile phone as a modem. Receiving commercial advertisements ("spam") via a kiosk, e.g. at a movie theatre or lobby
Computer system requirement
A personal computer must have a Bluetooth adapter in order to be able to communicate with other Bluetooth devices (such as mobile phones, mice and keyboards). While some desktop computers already contain an internal Bluetooth adapter, most require an external Bluetooth dongle. Most recent laptops come with a built-in Bluetooth adapter.
Unlike its predecessor, IrDA, which requires a separate adapter for each device, Bluetooth allows multiple devices to communicate with a computer over a single adapter.
Operating system supportØ Apple has supported Bluetooth since Mac OS X version 10.2 released in 2002.

Ø Microsoft supported for windows platforms, Windows XP Service Pack 2 and later releases have native support for Bluetooth. Previous versions required the users to install their Bluetooth adapter's own drivers, which was not directly supported by Microsoft. Microsoft's own Bluetooth dongles (that are packaged with their Bluetooth computer devices) have no external drivers and thus require at least Windows XP Service Pack 2.

Ø Linux provides two Bluetooth stacks, with the BlueZ stack included with most Linux kernels. It was originally developed by Qualcomm and Affix. BlueZ supports all core Bluetooth protocols and layers.
Specifications and features of Bluetooth
The Bluetooth specification was developed in 1994 by Jaap Haartsen and Sven Mattisson, who were working for Ericsson Mobile Platforms in Lund, Sweden. The specification is based on frequency-hopping spread spectrum technology.
The specifications were formalized by the Bluetooth Special Interest Group (SIG), organized by Mohd Syarifuddin. The SIG was formally announced on May 20, 1998. Today it has over 7000 companies worldwide. It was established by Ericsson, Sony Ericsson, IBM, Intel, Toshiba, and Nokia, and later joined by many other companies.
Bluetooth 2.0
This version, specified on 10th November 2004, is backward-compatible with 1.1. The main enhancement is the introduction of an Enhanced Data Rate (EDR) of 3.0 Mbit/s. This has the following effects:
Three times faster transmission speed—up to 10 times in certain cases (up to 2.1 Mbit/s).
Lower power consumption through a reduced duty cycle.
Simplification of multi-link scenarios due to more available bandwidth.
The practical data transfer rate is 2.1 megabits per second and the basic signaling rate is about 3 megabits per second.
The "Bluetooth 2.0 + EDR" specification given at the Bluetooth Special Interest Group (SIG) includes EDR and there is no specification "Bluetooth 2.0" as used by many vendors. The HTC TyTN pocket PC phone, shows "Bluetooth 2.0 without EDR" on its data sheet and another source states Bluetooth 2.0 without EDR is equivalent to version 1.2 with additional bug fixes. In many cases it is not clear whether a product claiming to support "Bluetooth 2.0" actually supports the EDR higher transfer rate.
Bluetooth 2.1
Bluetooth Core Specification Version 2.1 is fully backward-compatible with 1.1, and was adopted by the Bluetooth SIG on August 1, 2007. This specification includes the following features:
Extended inquiry response: provides more information during the inquiry procedure to allow better filtering of devices before connection. This information includes the name of the device, a list of services the device supports, as well as other information like the time of day, and pairing information.
Sniff subrating: reduces the power consumption when devices are in the sniff low-power mode, especially on links with asymmetric data flows. Human interface devices (HID) are expected to benefit the most, with mouse and keyboard devices increasing the battery life by a factor of 3 to 10.
Encryption Pause Resume: enables an encryption key to be refreshed, enabling much stronger encryption for connections that stay up for longer than 23.3 hours (one Bluetooth day).
Secure Simple Pairing: radically improves the pairing experience for Bluetooth devices, while increasing the use and strength of security. It is expected that this feature will significantly increase the use of Bluetooth.
NFC cooperation: automatic creation of secure Bluetooth connections when NFC radio interface is also available. For example, a headset should be paired with a Bluetooth 2.1 phone including NFC just by bringing the two devices close to each other (a few centimeters). Another example is automatic uploading of photos from a mobile phone or camera to a digital picture frame just by bringing the phone or camera close to the frame.
Future of Bluetooth
Broadcast Channel: enables Bluetooth information points. This will drive the adoption of Bluetooth into cell phones, and enable advertising models based around users pulling information from the information points, and not based around the object push model that is used in a limited way today.
Topology Management: enables the automatic configuration of the piconet topologies especially in scatternet situations that are becoming more common today. This should all be invisible to the users of the technology, while also making the technology just work.
Alternate MAC PHY: enables the use of alternative MAC and PHY's for transporting Bluetooth profile data. The Bluetooth Radio will still be used for device discovery, initial connection and profile configuration, however when lots of data needs to be sent, the high speed alternate MAC PHY's will be used to transport the data. This means that the proven low power connection models of Bluetooth are used when the system is idle, and the low power per bit radios are used when lots of data needs to be sent.
QoS improvements: enable audio and video data to be transmitted at a higher quality, especially when best effort traffic is being transmitted in the same piconet.
Bluetooth technology already plays a part in the rising Voice over IP (VOIP) scene, with Bluetooth headsets being used as wireless extensions to the PC audio system. As VOIP becomes more popular, and more suitable for general home or office users than wired phone lines, Bluetooth may be used in cordless handsets, with a base station connected to the Internet link.
High speed Bluetooth
On 28 March 2006, the Bluetooth Special Interest Group announced its selection of the WiMedia Alliance Multi-Band Orthogonal Frequency Division Multiplexing (MB-OFDM) version of UWB for integration with current Bluetooth wireless technology.
UWB integration will create a version of Bluetooth wireless technology with a high-speed/high-data-rate option. This new version of Bluetooth technology will meet the high-speed demands of synchronizing and transferring large amounts of data, as well as enabling high-quality video and audio applications for portable devices, multi-media projectors and television sets, and wireless VOIP.
At the same time, Bluetooth technology will continue catering to the needs of very low power applications such as mice, keyboards, and mono headsets, enabling devices to select the most appropriate physical radio for the application requirements, thereby offering the best of both worlds.
Bluetooth 3.0
The next version of Bluetooth after v2.1, code-named Seattle (the version number of which is TBD) has many of the same features, but is most notable for plans to adopt ultra-wideband (UWB) radio technology. This will allow Bluetooth use over UWB radio, enabling very fast data transfers of up to 480 Mbit/s, while building on the very low-power idle modes of Bluetooth.
Ultra Low Power Bluetooth
On June 12, 2007, Nokia and Bluetooth SIG announced that Wibree will be a part of the Bluetooth specification as an ultra low power Bluetooth technology. Expected use cases include watches displaying Caller ID information, sports sensors monitoring your heart rate during exercise, as well as medical devices. The Medical Devices Working Group is also creating a medical devices profile and associated protocols to enable this market.
Is Bluetooth and Wi-Fi same?
Bluetooth and Wi-Fi have slightly different applications in today's offices, homes, and on the move: setting up networks, printing, or transferring presentations and files from PDAs to computers. Both are versions of unlicensed spread spectrum technology.
Bluetooth differs from Wi-Fi in that the latter provides higher throughput and covers greater distances, but requires more expensive hardware and higher power consumption. They use the same frequency range, but employ different multiplexing schemes. While Bluetooth is a cable replacement for a variety of applications, Wi-Fi is a cable replacement only for local area network access. Bluetooth is often thought of as wireless USB, whereas Wi-Fi is wireless Ethernet, both operating at much lower bandwidth than the cable systems they are trying to replace. However, this analogy is not entirely accurate since any Bluetooth device can, in theory, host any other Bluetooth device—something that is not universal to USB devices; therefore it would resemble more a wireless FireWire.
Bluetooth
Bluetooth exists in many products, such as phones, printers, modems and headsets. The technology is useful when transferring information between two or more devices that are near each other in low-bandwidth situations. Bluetooth is commonly used to transfer sound data with phones (i.e. with a Bluetooth headset) or byte data with hand-held computers (transferring files).
Bluetooth simplifies the discovery and setup of services between devices. Bluetooth devices advertise all of the services they provide. This makes using services easier because there is no longer a need to setup network addresses or permissions as in many other networks.
Wi-FiWi-Fi is more like traditional Ethernet networks, and requires configuration to set up shared resources, transmit files, and to set up audio links (for example, headsets and hands-free devices). It uses the same radio frequencies as Bluetooth, but with higher power output resulting in a stronger connection. Wi-Fi is sometimes called "wireless Ethernet." This description is accurate as it also provides an indication of its relative strengths and weaknesses. Wi-Fi requires more setup, but is better suited for operating full-scale networks because it enables a faster connection, better range from the base station, and better security than Bluetooth.
Technical Specifications

Communication and connectionA master Bluetooth device can communicate with up to seven devices. This network group of up to eight devices is called a piconet.
A piconet is an ad-hoc computer network, using Bluetooth technology protocols to allow one master device to interconnect with up to seven active devices. Up to 255 further devices can be inactive, or parked, which the master device can bring into active status at any time.

At any given time, data can be transferred between the master and one other device, however, the devices can switch roles and the slave can become the master at any time. The master switches rapidly from one device to another in a round-robin fashion. (Simultaneous transmission from the master to multiple other devices is possible, but not used much.)

Bluetooth specification allows connecting two or more piconets together to form a scatternet, with some devices acting as a bridge by simultaneously playing the master role and the slave role in one piconet. These devices are planned for 2007.
Many USB Bluetooth adapters are available, some of which also include an IrDA adapter. Older (pre-2003) Bluetooth adapters, however, have limited services, offering only the Bluetooth Enumerator and a less-powerful Bluetooth Radio incarnation. Such devices can link computers with Bluetooth, but they do not offer much in the way of services that modern adapters do.
Setting up connections
Any Bluetooth device will transmit the following sets of information on demand:
Device name
Device class
List of servicesTechnical information, for example, device features, manufacturer, Bluetooth specification, clock offset.
Any device may perform an inquiry to find other devices to which to connect, and any device can be configured to respond to such inquiries. However, if the device trying to connect knows the address of the device, it always responds to direct connection requests and transmits the information shown in the list above if requested. Use of device services may require pairing or acceptance by its owner, but the connection itself can be started by any device and held until it goes out of range. Some devices can be connected to only one device at a time, and connecting to them prevents them from connecting to other devices and appearing in inquiries until they disconnect from the other device.

Every device has a unique 48-bit address. However these addresses are generally not shown in inquiries. Instead, friendly Bluetooth names are used, which can be set by the user. This name appears when another user scans for devices and in lists of paired devices.
Most phones have the Bluetooth name set to the manufacturer and model of the phone by default. Most phones and laptops show only the Bluetooth names and special programs that are required to get additional information about remote devices. This can be confusing as, for example, there could be several phones in range named T610 (see Bluejacking).
PairingPairs of devices may establish a trusted relationship by learning (by user input) a shared secret known as a passkey. A device that wants to communicate only with a trusted device can cryptographically authenticate the identity of the other device. Trusted devices may also encrypt the data that they exchange over the air so that no one can listen in. The encryption can, however, be turned off, and passkeys are stored on the device file system, not on the Bluetooth chip itself. Since the Bluetooth address is permanent, a pairing is preserved, even if the Bluetooth name is changed. Pairs can be deleted at any time by either device. Devices generally require pairing or prompt the owner before they allow a remote device to use any or most of their services. Some devices, such as Sony Ericsson phones, usually accept OBEX business cards and notes without any pairing or prompts.

Certain printers and access points allow any device to use its services by default, much like unsecured Wi-Fi networks. Pairing algorithms are sometimes manufacturer-specific for transmitters and receivers used in applications such as music and entertainment.

Air interface
The protocol operates in the license-free ISM band at 2.4-2.4835 GHz. To avoid interfering with other protocols that use the 2.45 GHz band, the Bluetooth protocol divides the band into 79 channels (each 1 MHz wide) and changes channels up to 1600 times per second. Implementations with versions 1.1 and 1.2 reach speeds of 723.1 kbit/s. Version 2.0 implementations feature Bluetooth Enhanced Data Rate (EDR) and reach 2.1 Mbit/s. Technically, version 2.0 devices have a higher power consumption, but the three times faster rate reduces the transmission times, effectively reducing power consumption to half that of 1.x devices (assuming equal traffic load).
SecurityBluetooth implements confidentiality, authentication and key derivation with custom algorithms based on the SAFER+ block cipher. In Bluetooth, key generation is generally based on a Bluetooth PIN, which has to be entered into both devices. This procedure might get modified slightly, if one of the devices has a fixed PIN, which is the case e.g. for headsets or similar devices with a restricted user interface. Foremost, an initialization key or master key is generated, using the E22 algorithm.
The E0 stream cipher is used for encrypting packets, granting confidentiality and is based on a shared cryptographic secret, namely a previously generated link key or master key. Those keys, used for subsequent encryption of data sent via the air interface, hardly rely on the Bluetooth PIN, which has been entered into one or both devices.

Data Mining

DATA MINING

Introduction
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?"

The Foundations of Data Mining
Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:
1) Massive data collection
2) Powerful multiprocessor computers
3) Data mining algorithms
Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996. In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods.

The Scope of Data Mining
Data mining derives its name from the similarities between searching for valuable business information in a large database — for example, finding linked products in gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:
Automated prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data — quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Data mining techniques
Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions.

Databases can be larger in both depth and breadth:
More columns. Analysts must often limit the number of variables they examine when doing hands-on analysis due to time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full depth of a database, without preselecting a subset of variables.
More rows. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population.
A recent Gartner Group Advanced Technology Research Note listed data mining and artificial intelligence at the top of the five key technology areas that "will clearly have a major impact across a wide range of industries within the next 3 to 5 years."2 Gartner also listed parallel architectures and data mining as two of the top 10 new technologies in which companies will invest during the next 5 years. According to a recent Gartner HPC Research Note, "With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability)."3
The most commonly used techniques in data mining are:
Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID).
Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.
Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbor technique.
Rule induction: The extraction of useful if-then rules from data based on statistical significance.
Many of these technologies have been in use for more than a decade in specialized analysis tools that work with relatively small volumes of data. These capabilities are now evolving to integrate directly with industry-standard data warehouse and OLAP platforms. The appendix to this white paper provides a glossary of data mining terms.

How Data Mining Works
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data.
How exactly is data mining able to tell you important things that you didn't know or what is going to happen next? The technique that is used to perform these feats in data mining is called modeling. Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don't. For instance, if you were looking for a sunken Spanish galleon on the high seas the first thing you might do is to research the times when Spanish treasure had been found by others in the past. You might note that these ships often tend to be found off the coast of Bermuda and that there are certain characteristics to the ocean currents, and certain routes that have likely been taken by the ship’s captains in that era. You note these similarities and build a model that includes the characteristics that are common to the locations of these sunken treasures. With these models in hand you sail off looking for treasure where your model indicates it most likely might be given a similar situation in the past. Hopefully, if you've got a good model, you find your treasure.
This act of model building is thus something that people have been doing for a long time, certainly before the advent of computers or data mining technology. What happens on computers, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don't know the answer. For example, say that you are the director of marketing for a telecommunications company and you'd like to acquire some new long distance phone customers. You could just randomly go out and mail coupons to the general population - just as you could randomly sail the seas looking for sunken treasure. In neither case would you achieve the results you desired and of course you have the opportunity to do much better than random - you could use your business experience stored in your database to build a model.
As the marketing director you have access to a lot of information about all of your customers: their age, sex, credit history and long distance calling usage. The good news is that you also have a lot of information about your prospective customers: their age, sex, credit history etc. Your problem is that you don't know the long distance calling usage of these prospects (since they are most likely now customers of your competition). You'd like to concentrate on those prospects that have large amounts of long distance usage. You can accomplish this by building a model.
The goal in prospecting is to make some calculated guesses about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information. For instance, a simple model for a telecommunications company might be:
98% of my customers who make more than $60,000/year spend more than $80/month on long distance
This model could then be applied to the prospect data to try to tell something about the proprietary information that this telecommunications company does not currently have access to. With this model in hand new customers can be selectively targeted.
Test marketing is an excellent source of data for this kind of modeling. Mining the results of a test market representing a broad but relatively small sample of prospects can provide a foundation for identifying good prospects in the overall market. Table 2 shows another common scenario for building models: predict what is going to happen in the future.

If someone told you that he had a model that could predict customer usage how would you know if he really had a good model? The first thing you might try would be to ask him to apply his model to your customer base - where you already knew the answer. With data mining, the best way to accomplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is complete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data.

Architecture for Data Mining
To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on.

The ideal starting point is a data warehouse containing a combination of internal data tracking all customer contact coupled with external market data about competitor activity. Background information on potential customers also provides an excellent basis for prospecting. This warehouse can be implemented in a variety of relational database systems: Sybase, Oracle, Redbrick, and so on, and should be optimized for flexible and fast data access.
An OLAP (On-Line Analytical Processing) server enables a more sophisticated end-user business model to be applied when navigating the data warehouse. The multidimensional structures allow the user to analyze the data as they want to view their business – summarizing by product line, region, and other key perspectives of their business. The Data Mining Server must be integrated with the data warehouse and the OLAP server to embed ROI-focused business analysis directly into this infrastructure. An advanced, process-centric metadata template defines the data mining objectives for specific business issues like campaign management, prospecting, and promotion optimization. Integration with the data warehouse enables operational decisions to be directly implemented and tracked. As the warehouse grows with new decisions and results, the organization can continually mine the best practices and apply them to future decisions.
This design represents a fundamental shift from conventional decision support systems. Rather than simply delivering data to the end user through query and reporting software, the Advanced Analysis Server applies users’ business models directly to the warehouse and returns a proactive analysis of the most relevant information. These results enhance the metadata in the OLAP Server by providing a dynamic metadata layer that represents a distilled view of the data. Reporting, visualization, and other analysis tools can then be applied to plan future actions and confirm the impact of those plans.

Profitable Applications
A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).
Some successful application areas include:
A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations.
A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.
A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.
A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.
Each of these examples has a clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.

Conclusion
Comprehensive data warehouses that integrate operational data with customer, supplier, and market information have resulted in an explosion of information. Competition requires timely and sophisticated analysis on an integrated view of the data. However, there is a growing gap between more powerful storage and retrieval systems and the users’ ability to effectively analyze and act on the information they contain. Both relational and OLAP technologies have tremendous capabilities for navigating massive data warehouses, but brute force navigation of data is not enough. A new technological leap is needed to structure and prioritize information for specific end-user problems. The data mining tools can make this leap. Quantifiable business benefits have been proven through the integration of data mining with current information systems, and new products are on the horizon that will bring this integration to an even wider audience of users.

Great Innovator :: Albert Einstein told

The important thing is not to stop questioning. Curiosity has its own reason for existing. One cannot help but be in awe when he contemplates the mysteries of eternity, of life, of the marvelous structure of reality. It is enough if one tries merely to comprehend a little of this mystery every day. Never lose a holy curiosity.