DSS

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DSS
http//www.thearling.com/text/dmwhite/dmwhite.htm
www.kmining.com/info_definitions.html
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Data Mining

• Data mining, the extraction of hidden predictive
  information from large databases
• Data mining tools predict future trends and
  behaviors, allowing businesses to make proactive,
  knowledge-driven decisions
• 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

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Data Mining

• Forecasting sales
• Targeting mailings toward specific customers
• Determining which products are likely to be sold
  together
• Finding sequences in the order that customers add
  products to a shopping cart

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www.microsoft.com/.../tr/data-mining-addins.aspx
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In the evolution from business data to business
information (each new step has built upon the
previous one)
Steps in the Evolution of Data Mining
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• 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.

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Data mining technology can generate new business
opportunities by providing these capabilities

• Automated prediction of trends and behaviors. 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. 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.

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How Data Mining Works

• 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 ships 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.

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• 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 who have large amounts of long distance
  usage. You can accomplish this by building a
  model.

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• To best apply these data mining 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

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illustrates an architecture for advanced analysis
in a large data warehouse
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Some successful application areas include

1. 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.

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1. 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.

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1. 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.

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1. 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 have 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.
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OLAP

• On-line analytical processing. Refers to
  array-oriented database applications that allow
  users to view, navigate through, manipulate, and
  analyze multidimensional databases.

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http//www.filebuzz.com/software_screenshot/full/6
1412-RadarCube_OLAP_ASP_NET_Direct.jpg
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http//farm3.static.flickr.com/2154/2497588470_07f
9d36ca6.jpg
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OLAP

• Until the mid-nineties, performing OLAP analysis
  was an extremely costly process mainly restricted
  to larger organizations (the major OLAP vendor
  are Hyperion, Cognos, Business Objects,
  MicroStrategy). This has changed as the major
  database vendor have started to incorporate OLAP
  modules within their database offering -
  Microsoft SQL Server 2000 with Analysis Services,
  Oracle with Express and Darwin, and IBM with DB2.

Cont...
http//www.dwreview.com/OLAP/Introduction_OLAP.htm
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OLAP

• OLAPs are designed to give an overview analysis
  of what happened. Hence the data storage (i.e.
  data modeling) has to be set up differently. The
  most common method is called the star design.
• The central table in an OLAP start data model is
  called the fact table. The surrounding tables are
  called the dimensions. Using the above data
  model, it is possible to build reports that
  answer questions such as
• The supervisor that gave the most discounts.
• The quantity shipped on a particular date, month,
  year or quarter.
• In which zip code did product A sell the most.
• To obtain answers, such as the ones above, from a
  data model OLAP cubes are created (or
  multi-dimensional expressions).

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OLAP Example
http//www.rittmanmead.com/2005/04/positioning-ora
clebi-discoverer-for-olap-2/
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OLAP Example
http//www.rittmanmead.com/2005/04/positioning-ora
clebi-discoverer-for-olap-2/
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OLAP Example
http//www.cimconcepts.com/svcs_rpt.shtml
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Data Mining vs OLAP

• Both data mining and OLAP are two of the common
  Business Intelligence (BI) technologies. Business
  intelligence refers to computer-based methods for
  identifying and extracting useful information
  from business data.
• Data mining deals with extracting interesting
  patterns from large sets of data. It combines
  many methods from artificial intelligence,
  statistics and database management.
• OLAP is a compilation of ways to query
  multi-dimensional databases.

Cont...
http//www.differencebetween.com/difference-betwee
n-data-mining-and-vs-olap/ixzz1JjXKPFqe
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• Data mining usually deals with following four
  tasks clustering, classification, regression,
  and association. Clustering is identifying
  similar groups from unstructured data.
  Classification is learning rules that can be
  applied to new data and will typically include
  following steps preprocessing of data, designing
  modeling, learning/feature selection and
  evaluation/validation. Regression is finding
  functions with minimal error to model data. And
  association is looking for relationships between
  variables. Data mining is usually used to answer
  questions like what are the main products that
  might help to obtain high profit next year in
  Wal-Mart.
• Typically OLAP is used for marketing, budgeting,
  forecasting and similar applications. a matrix is
  used to display the output of an OLAP. The rows
  and columns are formed by the dimensions of the
  query. They often use methods of aggregation on
  multiple tables to obtain summaries. For example,
  it can be used to find out about the sales of
  this year in Wal-Mart compared to last year? What
  is the prediction on the sales in the next
  quarter? What can be said about the trend by
  looking at the percentage change?

Cont...
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• Although it is obvious that Data mining and OLAP
  are similar because they operate on data to gain
  intelligence, the main difference comes from how
  they operate on data. OLAP tools provides
  multidimensional data analysis and they provide
  summaries of the data but contrastingly, data
  mining focuses on ratios, patterns and influences
  in the set of data. That is an OLAP deal with
  aggregation, which boils down to the operation of
  data via addition but data mining corresponds
  to division. Other notable difference is that
  while data mining tools model data and return
  actionable rules, OLAP will conduct comparison
  and contrast techniques along business dimension
  in real time.

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GIS
http//www.boluarastirma.gov.tr/index.php?sayfaic
erikid44main_menu37
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GIS

• A geographic information system (GIS) allows us
  to view, understand, question, interpret, and
  visualize data in many ways that reveal
  relationships, patterns, and trends in the form
  of maps, globes, reports, and charts.

http//gis.com/
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What Can You Do with GIS?

• 1. Map Where Things Are
• Mapping where things are lets you find places
  that have the features you're looking for, and to
  see where to take action. Finding
  patternsLooking at the distribution of features
  on the map instead of just an individual feature,
  you can see patterns emerge.

Cont...
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What Can You Do with GIS?

• 2. Map Quantities
• People map quantities, like where the most and
  least are, to find places that meet their
  criteria and take action, or to see the
  relationships between places. For example, a
  catalog company selling children's clothes would
  want to find ZIP Codes not only around their
  store, but those ZIP Codes with many young
  families with relatively high income. Or, public
  health officials might not only want to map
  physicians, but also map the numbers of
  physicians per 1,000 people in each census tract
  to see which areas are adequately served, and
  which are not.

Cont...
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What Can You Do with GIS?

• 3. Map Densities
• While you can see concentrations by simply
  mapping the locations of features, in areas with
  many features it may be difficult to see which
  areas have a higher concentration than others. A
  density map lets you measure the number of
  features using a uniform areal unit, such as
  acres or square miles, so you can clearly see the
  distribution. Mapping density is especially
  useful when mapping areas, such as census tracts
  or counties, which vary greatly in size. On maps
  showing the number of people per census tract,
  the larger tracts might have more people than
  smaller ones. But some smaller tracts might have
  more people per square milea higher density.

Cont...
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What Can You Do with GIS?

• 4. Find What's Inside
• Use GIS to monitor what's happening and to take
  specific action by mapping what's inside a
  specific area. For example, a district attorney
  would monitor drug-related arrests to find out if
  an arrest is within 1,000 feet of a school--if
  so, stiffer penalties apply.

Cont...
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What Can You Do with GIS?

• 5. Find What's Nearby (Map Change)
• Map the change in an area to anticipate future
  conditions, decide on a course of action, or to
  evaluate the results of an action or policy.
• By mapping where and how things move over a
  period of time, you can gain insight into how
  they behave. For example, a meteorologist might
  study the paths of hurricanes to predict where
  and when they might occur in the future.
• Map change to anticipate future needs. For
  example, a police chief might study how crime
  patterns change from month to month to help
  decide where officers should be assigned.
• Map conditions before and after an action or
  event to see the impact. A retail analyst might
  map the change in store sales before and after a
  regional ad campaign to see where the ads were
  most effective.

Cont...
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GIS example
http//demo.dtsagile.com/wildfire/
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GIS example
http//www.esri.com/industries.html