Understanding Price/Volume Curves
Speakers <ul><li>Michael Kamprath </li></ul><ul><ul><li>Sr. Technical Director, R&D Technology </li></ul></ul><ul><li>Trac...
agenda <ul><ul><li>Managing Ad Inventory </li></ul></ul><ul><ul><li>What is a P/V Curve? </li></ul></ul><ul><ul><li>How to...
Online Advertising Network Modeling <ul><li>The Advertising “Market” </li></ul><ul><ul><li>Asset = Inventory </li></ul></u...
Market State <ul><li>Understanding a market’s state is complicated by several factors </li></ul><ul><ul><li>Not all advert...
What is a Price/Volume curve? <ul><li>Represents market’s bidding environment. </li></ul><ul><li>Plots expected impression...
Understanding Bid Price and Volume <ul><li>Questions a P/V curve can answer: </li></ul><ul><ul><li>How to bid when enterin...
<ul><li>GOAL   600K impressions </li></ul>Using a P/V Curve Market A: East Coast $0.60 bid P/V Curve 0 200,000 400,000 600...
Using a P/V Curve Market A: East Coast $0.20 increase  P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.0...
Using a P/V Curve <ul><li>Goal: Find the Optimal Bid </li></ul>R =  revenue derived from showing ad B =  ad’s bid  N(B) = ...
Building a P/V Curve 0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 Cumulative P/V Curve IMPRESSIONS PER DAY CPM Bid DATA IMP NO...
Adjusting for Frequency Cap <ul><li>Frequency Cap </li></ul><ul><ul><li>Limits the number of times a unique viewer sees an...
Adjusting for Frequency Cap 0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS CPM Unadjusted Curve P/V Curve 0 1 2 3 4 ...
Engineering P/V Curves
Engineering P/V Curves <ul><li>Simple in theory, but difficult in practice </li></ul><ul><ul><li>Large amounts of data </l...
Engineering Challenge: Large Amounts of Data <ul><li>The Challenge: </li></ul><ul><ul><li>5TB-10TB impression data daily X...
Engineering Solution: Large Amounts of Data <ul><li>Data sampling reduces data size </li></ul><ul><li>Distributed computin...
Netezza Data Appliance SMP host Massively Parallel  Intelligent Storage Snippet Processing Unit (SPU) Processor & streamin...
Engineering Challenge: Complex Market Targeting USER AGE 20’S 30’S GEOGRAPHY CA NY
Engineering Solution: Full Boolean Targeting <ul><li>AOL Advertising algorithm plus Netezza OnStream </li></ul><ul><ul><li...
Snippet Processing Unit (SPU) 1M Gate FPGA Enterprise SATA Disk Drive 400 GB 440GX Power PC AOL Advertising Matching Algor...
Engineering Challenge: Frequency Availability Calculation <ul><li>Frequency histogram =  # users at given frequency at giv...
Frequency Histogram Example SAMPLE IMPRESSIONS FREQUENCY CAP: ONE IMPRESSION PER UNIQUE VIEWER UNIQUE ID PRICE A $0.01 A $...
Engineering Challenge: Frequency Availability Calculation <ul><li>The “Brute force” approach </li></ul><ul><ul><li>Find al...
Engineering Solution: Frequency Availability Calculation <ul><li>Use map/reduce approach </li></ul><ul><ul><li>Distribute ...
Frequency Histogram Processing in the Netezza Gigabit Ethernet Fabric Netezza Performance Server® System SPU C12 C13 F12 F...
Demonstrated Performance <ul><li>One curve per 1-5 minutes </li></ul><ul><ul><li>Multiple curves simultaneously </li></ul>...
Conclusions <ul><li>P/V curves provide the powerful analysis that Market model demands </li></ul><ul><li>Netezza data appl...
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AOL Advertising on Price/Volume Analytics for Advanced Bid Optimization and Inventory Management

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Managing the yield performance of large advertising networks, such as the Advertising.com network, requires a soundly structured approach. One approach is to interpret advertising inventory as a commodity and the network as an exchange. In such, analysis tools and techniques based on established practices of economic modeling can be used to help optimize yield and improve media planning. One such tool is the price/volume forecast generator. While interpreting a price/volume forecast is relatively straightforward, creating an accurate forecast is challenging both from an algorithm and engineering perspective. This presentation will provide an overview of AOL Advertising's experience on how to construct and use price/volume forecasts. This slide deck is also available for download from the ad:tech website here: http://www.ad-tech.com/ny/presentations/.

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  • -Before understanding P/V curves, it is useful to explain the context in which they are used. -At AOL Advertising, we approach managing our advertising network much like one would manage a market -- In this paradigm, we think of the asset being traded to be advertising inventory or page view impressions -- The supplier of the asset are the various publishers in our network -- Ad the entities that creates demand for the asset are advertisers - Similar to a financial market, advertising inventory is granted to advertisers with the highest bid for the inventory in a specific market -- An advertiser would develop their bids for various markets after considering their valuation of that inventory and their business objectives against that inventory - A market-based advertising network does not work the same as a reservations-based network -- In a reservation-based system, an advertiser buys inventory in advance, reserving the inventory exclusively to that advertiser -- In contrast, a market-based network does not reserve inventory to specific advertiser ahead of time. Instead, the advertiser with the highest bid in place at the time of the inventory’s page view impression is the one that will receive that particular impression - Given this difference, bidding intelligently becomes essential in order to achieve any specific business objectives with an advertising campaign in a market-based advertising network. -- One cannot bid intelligently unless they have insight into the state of the advertising market
  • - Understanding the state of any given market is complicated by several factors - The first is the fact that not all advertisers target the same thing, but there is overlap - For example, one advertiser might target people from California, while another might target middle-aged men - When there are many thousands of advertisers, understanding and analyzing every possible overlap becomes untenable - A word about market definition: in this presentation we use the term “market” and “target” interchangeably. The advertising network as a whole is a market, but from the perspective of an advertiser, the market they see is what they target. - An advertiser may not want all the inventory in their targeted market. - There are two main reasons an advertiser may not want all inventory: frequency cap and budget constraints. - Markets are dynamic - Both advertisers and publishers are constantly entering and leaving a market - An advertiser’s business objects may change over time and thus they change their bidding strategy - The nature of the inventory that publishers supply changes with time too Given all these complexities, how can an advertiser gain insight into the state of a market and thus bid intelligently?  Price/Volume curves
  • - P/V curve is a cumulative plot that shows the volume one can expect to win at a given bid price in a given market. - Each market can be different because of different types of competitors and inventory. - A P/V curve is a representation of a market’s bidding environment. - Understanding a market’s P/V relationship can provide insights into pricing and the available volume on the given market.
  • When entering a market, a P/V curve is used to determine required bid for desired volume What bid should be used to acquire 600K impressions in each market? In Market A, bid at $0.60 CPM In Market B, bid at $2.00 CPM
  • When a change is desired in impression volume, a P/V curve is used to determine what adjustment should be made to the bid To increase impressions from 600K to 700K in each market, what should be the bid adjustment? In Market A, increase the bid from $0.80 CPM to $1.00 CPM (a $0.20 CPM difference) in order to acquire 100K additional impressions above current level of 600K In Market B, increase the bid from $2.00 CPM to $2.03 CPM (a $0.03 CPM difference) in order to acquire 100K additional impressions above current level of 600K
  • If you bid $0, you get 0 impressions and 0 profit. If you bid such that your costs equal your revenue, you again have 0 profit. The bid that maximizes your profit is somewhere in between. A P/V curve can be used to find the bid that generates the most profit by using the following equation: R = ad’s value revenue created per impression (i.e., advertiser’s realized conversion value), referred to as RPM B = ad’s bid per impression N(B) = impression volume for bid B as predicted by P/V Curve max( (R – B) • N(B) ) For example, given Market A’s curve and an ad’s $5.00 RPM, the optimal bid is $1.16 CPM with a profit of $3,115.00 As the RPM increases , the optimal bid increases too. There is a point at which the RPM is such that the optimal bid is to own the entire market.
  • General approach Find all impressions belonging to desired market Analyze the distribution of winning bid prices Aggregate impressions by bid value. Accumulate impressions for each bid value, starting from lowest to highest bid.
  • To adjust the basic P/V curve for frequency cap, the frequency availability needs to be computed and applied for each bid value
  • Example: Frequency Cap is “1 impression per unique user” For each bid, we compute the frequency availability by finding ratio of frequency 1 impressions per unique viewer to total impressions. To build final P/V curve, we multiply each bid’s frequency availability to the corresponding raw impression volume
  • In practice, P/V curves are difficult to make en masse - Large amounts of source data to process - Business features such as market targeting must be accounted for - Frequency cap analysis computations are very intensive Furthermore, business demands that P/V curves are produced as quickly as possible - Enables real time “what if” type analysis by advertisers
  • Extremely Large Data Sets - Many large advertising networks can produce 5-10TB of impression data a day - Ideally 1 to 4 weeks worth of data is used when generating a P/V curve. - This data cannot be summarized if we want to maintain ability to perform arbitrary P/V analyses - While storage space is plentiful, time is not Naïve methods of interacting with such large data sets frequently perform poorly - Reading 1 week of data (35 TB) from a single hard drive at 3 Mb/s would take 25.9 hours. This is simply transfer time. Doing something interesting with the data would take more time.
  • Data sampling can be used to reduce data size - Since P/V curves are used for analysis and projections, exact impression counts are not important - However, the sampling rate must be balanced against statistical quality - Sampling should be on the unique viewer in order to enable frequency cap analysis Distributed computing clusters improve analysis performance - P/V analysis is a “data parallel” problem, which means many computers can be used simultaneously to solve - Netezza’s data warehouse appliance was selected by AOL Advertising to be the backbone of the P/V analysis system
  • AOL Advertising needed to enable arbitrarily complex market targeting when performing P/V analysis - Simple targeting does not permit conditional combinations of impression properties - Complex targeting permits some properties to be targeted depending on whether certain other properties are present. Traditional database querying (e.g., SQL) does not perform well when targeting is complex EXAMPLE: - Client wants to target ages 20-39 in California or ages 30-39 in New York. Since the age range 20-29 is no present in the target for both states, the target is said to be non-convex (red line shape). A convex target would have both age ranges equally targeted in both states (black line shape). - The complex target could be broken down into two simple targets, but real-world applications of this approach are operationally complex.
  • Solution was to leverage the same market targeting technology contained in AOL Advertising’s ad servers - Highly optimized matching algorithm that enables very fast matching of many complex market targets simultaneously Required leveraging Netezza’s OnStream feature - Embedded the AOL Advertising custom matching technology into each SPU on the Netezza appliance - Allows processing of multiple P/V curves with a single scan of the impression records - Leveraging OnStream feature yielded a 10x speed improvement on the Netezza over the SQL-only solution
  • A major part of the performance advantage derives from the system&apos;s MPP architecture – today allowing up to nearly 900 intelligent storage nodes to &amp;quot;divide and conquer&amp;quot; the workload and provide responses to a broad range of queries, from simple, tactical queries to operational queries running in near real-time to deep analytics. A second, and even more critical, performance advantage lies in the architecture of the intelligent storage nodes themselves and how the NPS system software makes use of them. In the Netezza appliance, these nodes are known as &amp;quot;Snippet Processing Units&amp;quot; (or &amp;quot;SPUs&amp;quot;) – each with its own embedded disk drive, CPU, memory and also a common off-the-shelf device known as a Field Programmable Gate Array (FPGA). The focus of the architecture is to enable streaming processing of the data: eliminating unneeded data as early as possible and processing the rest as rapidly as it can be read from the disk drives. That&apos;s where the FPGA comes in. The FPGA in a Netezza SPU has two primary roles. In the first, it acts as the disk controller, controlling all of the disk read and write activities on the SPU. In the second, the FPGA efficiently applies low-level database primitives, offloading significant work from other processing elements in the system. As table data streams from the disk on the SPU, the FPGA applies the appropriate column projection and row restriction rules. Then only data that satisfies the visibility, projection and restriction rules is sent from the FPGA to the memory and CPU on the SPU for additional processing, if necessary. This is illustrated in the sequence of builds: Suppose you have a very large FACT table and you want to execute a query. The shaded area in the table represents the columns defined your SELECT clause and the rows that match the WHERE clause. The FPGA will read from disk only the rows and columns that are needed from the disk—essentially filtering out all of the data that is not needed. The FPGA may also perform some other calculations and transformations on this data (such as uncompressing compressed data). In our example, further calculations and aggregations can be applied by the PowerPC chip such that ultimately, only a very small amount of highly manipulated data is dispatched up to the host. These calculations also include OnStream application code… And, of course, factor in that this same operation is being carried out in parallel against several hundred snippets of the same table and you begin to get a sense of why Netezza is orders of magnitude faster that the systems being replaced.
  • In order to generate the frequency availability curve, we need to produce a “frequency histogram” - A frequency histogram provides the number of unique viewers seen at a given frequency for a given price point. - Frequency availability is the ratio of impression available at the desired frequency to all impressions - A frequency histogram does not pertain to any specific frequency cap Frequency histograms are similar to Reach &amp; Frequency reports, with some differences: - Applies to an entire market rather than a specific advertiser’s delivery - Provides insight into how a market’s frequency profile changes with bid value
  • - Generating frequency histograms is suited to be framed in the map/reduce paradigm - The Netezza system allows the user to control how data is distributed amongst SPU’s - The solution is to leverage the Netezza’s map/reduce capabilities -- Distribute data by unique viewer so that all impressions attributed to an unique are on the same SPU -- Each SPU generates a frequency histogram for each unique viewer in its data set -- Each SPU then aggregates the unique viewer specific histograms to generate one histogram pertaining to all data on that SPU -- Final reduce step aggregates all the SPU histograms together into the final histogram.
  • The Netezza system used by AOL Advertising for P/V generation has 432 SPU’s, which in turn means each SPU solves 1/432 of the overall problem, which should be about 432 times faster than solving the problem with a single computer.
  • We are able to generate on average one P/V curve every 1-5 minutes - AOL Advertising is using the smallest Netezza data appliance - Our system can produce multiple curve simultaneously, allowing improved overall throughput We have demonstrated the ability to exclusively use P/V curves to control smooth campaign delivery
  • AOL Advertising on Price/Volume Analytics for Advanced Bid Optimization and Inventory Management

    1. 1. Understanding Price/Volume Curves
    2. 2. Speakers <ul><li>Michael Kamprath </li></ul><ul><ul><li>Sr. Technical Director, R&D Technology </li></ul></ul><ul><li>Tracy La </li></ul><ul><ul><li>Technical Manager, R&D </li></ul></ul><ul><li>Brad Terrell </li></ul><ul><ul><li>VP and General Manager, Digital Media, Netezza </li></ul></ul>
    3. 3. agenda <ul><ul><li>Managing Ad Inventory </li></ul></ul><ul><ul><li>What is a P/V Curve? </li></ul></ul><ul><ul><li>How to Use a P/V Curve </li></ul></ul><ul><ul><li>How to Make a P/V Curve </li></ul></ul><ul><ul><li>Building P/V Curves: Challenges and Solutions </li></ul></ul>
    4. 4. Online Advertising Network Modeling <ul><li>The Advertising “Market” </li></ul><ul><ul><li>Asset = Inventory </li></ul></ul><ul><ul><li>Supply = Publishers </li></ul></ul><ul><ul><li>Demand = Advertisers </li></ul></ul><ul><li>Auction determines how assets are assigned. </li></ul><ul><li>Market-based ≠ Reservation-based </li></ul>
    5. 5. Market State <ul><li>Understanding a market’s state is complicated by several factors </li></ul><ul><ul><li>Not all advertisers target the same consumers, but there is overlap </li></ul></ul><ul><ul><li>Advertisers may not want all inventory that fits their targeting criteria </li></ul></ul><ul><ul><li>Dynamic markets </li></ul></ul><ul><li>A P/V curve can provide insight. </li></ul>
    6. 6. What is a Price/Volume curve? <ul><li>Represents market’s bidding environment. </li></ul><ul><li>Plots expected impression volume for a range of bids in the targeted market. </li></ul>P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY
    7. 7. Understanding Bid Price and Volume <ul><li>Questions a P/V curve can answer: </li></ul><ul><ul><li>How to bid when entering a market </li></ul></ul><ul><ul><li>How to adjust bids when objectives change </li></ul></ul><ul><ul><li>How to find the bid that generates the most profit </li></ul></ul>P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY
    8. 8. <ul><li>GOAL 600K impressions </li></ul>Using a P/V Curve Market A: East Coast $0.60 bid P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY Market B: West Coast P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY $2.00 bid In Market B, bid at $2.00 CPM In Market A, bid at $0.60 CPM
    9. 9. Using a P/V Curve Market A: East Coast $0.20 increase P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY Market B: West Coast P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM IMPRESSIONS PER DAY $0.03 increase Goal: increase impressions from 600K to 700K In Market A, increase bid by $0.20 to acquire 100K additional impressions In Market B, increase bid by $0.03 to acquire 100K additional impressions
    10. 10. Using a P/V Curve <ul><li>Goal: Find the Optimal Bid </li></ul>R = revenue derived from showing ad B = ad’s bid N(B) = impression volume for bid B as predicted by P/V Curve Market A: East Coast P/V Curve 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 0.00 1.00 2.00 3.00 4.00 5.00 CPM Bid IMPRESSIONS PER DAY Profit Curve 0 500 1,000 1,500 2,000 2,500 3,000 3,500 0.00 1.00 2.00 3.00 4.00 5.00 CPM Bid PROFIT
    11. 11. Building a P/V Curve 0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 Cumulative P/V Curve IMPRESSIONS PER DAY CPM Bid DATA IMP NO UNIQUE ID BID PRICE 1 A1 0.01 2 A3 0.01 3 A2 0.01 4 A2 0.02 5 A5 0.03 6 A1 0.03 7 A4 0.03 8 A4 0.04 9 A4 0.04 10 A1 0.05
    12. 12. Adjusting for Frequency Cap <ul><li>Frequency Cap </li></ul><ul><ul><li>Limits the number of times a unique viewer sees an advertiser </li></ul></ul><ul><ul><li>Reduces number of eligible impressions for a given target </li></ul></ul><ul><li>Frequency Cap Availability </li></ul><ul><ul><li>Proportion of frequency cap eligible impressions within the target to all impressions in the target </li></ul></ul><ul><ul><li>Availability is different at each price point </li></ul></ul>0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS CPM Unadjusted Curve P/V Curve
    13. 13. Adjusting for Frequency Cap 0 2 4 6 8 10 12 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS CPM Unadjusted Curve P/V Curve 0 1 2 3 4 5 6 0.01 0.02 0.03 0.04 0.05 IMPRESSIONS PRICE Adjusted Curve Final P/V Curve DATA IMP NO UNIQUE ID BID PRICE 1 A1 0.01 2 A3 0.01 3 A2 0.01 4 A2 0.02 5 A5 0.03 6 A1 0.03 7 A4 0.03 8 A4 0.04 9 A4 0.04 10 A1 0.05 FC AVAILABILTY BID AVAILABILTY 0.01 1 0.02 0.75 0.03 0.714 0.04 0.556 0.05 0.5
    14. 14. Engineering P/V Curves
    15. 15. Engineering P/V Curves <ul><li>Simple in theory, but difficult in practice </li></ul><ul><ul><li>Large amounts of data </li></ul></ul><ul><ul><li>Complex Market Targeting </li></ul></ul><ul><ul><li>Frequency caps analysis </li></ul></ul><ul><ul><li>Time pressures </li></ul></ul>
    16. 16. Engineering Challenge: Large Amounts of Data <ul><li>The Challenge: </li></ul><ul><ul><li>5TB-10TB impression data daily X 1-4 weeks </li></ul></ul>
    17. 17. Engineering Solution: Large Amounts of Data <ul><li>Data sampling reduces data size </li></ul><ul><li>Distributed computing clusters improve performance </li></ul><ul><ul><li>AOL Advertising Selected Netezza data appliance </li></ul></ul><ul><ul><ul><li>User-friendly yet customizable </li></ul></ul></ul><ul><ul><ul><li>Manageable learning curve </li></ul></ul></ul><ul><ul><ul><li>Proven </li></ul></ul></ul>
    18. 18. Netezza Data Appliance SMP host Massively Parallel Intelligent Storage Snippet Processing Unit (SPU) Processor & streaming DB logic High-performance database engine streaming joins, aggregations, sorts, etc. SQL Compiler Query Plan Optimize Admin
    19. 19. Engineering Challenge: Complex Market Targeting USER AGE 20’S 30’S GEOGRAPHY CA NY
    20. 20. Engineering Solution: Full Boolean Targeting <ul><li>AOL Advertising algorithm plus Netezza OnStream </li></ul><ul><ul><li>Fast matching of complex targets </li></ul></ul><ul><ul><li>Multiple curves with a single record scan </li></ul></ul><ul><ul><li>10x faster than SQL-only </li></ul></ul>
    21. 21. Snippet Processing Unit (SPU) 1M Gate FPGA Enterprise SATA Disk Drive 400 GB 440GX Power PC AOL Advertising Matching Algorithm
    22. 22. Engineering Challenge: Frequency Availability Calculation <ul><li>Frequency histogram = # users at given frequency at given price </li></ul>
    23. 23. Frequency Histogram Example SAMPLE IMPRESSIONS FREQUENCY CAP: ONE IMPRESSION PER UNIQUE VIEWER UNIQUE ID PRICE A $0.01 A $0.02 A $0.02 A $0.05 A $0.06 B $0.02 B $0.03 B $0.04 B $0.05 B $0.05 B $0.05 FREQ PRICE $0.01 $0.02 $0.03 $0.04 $0.05 $0.06 1 1 1 0 0 0 0 2 0 0 1 0 0 0 3 0 1 1 2 0 0 4 0 0 0 0 1 0 5 0 0 0 0 0 1 6 0 0 0 0 1 1 1.0 0.8 0.6 0.4 0.2 0.0 $0.01 $0.02 $0.03 $0.04 $0.05 $0.06 FREQUENCY AVAILABILITY
    24. 24. Engineering Challenge: Frequency Availability Calculation <ul><li>The “Brute force” approach </li></ul><ul><ul><li>Find all the impressions for an individual unique viewer </li></ul></ul><ul><ul><li>Sort according to price </li></ul></ul><ul><ul><li>Generate a frequency histogram specific to the unique viewer </li></ul></ul><ul><ul><li>Repeat for each unique user and aggregate results </li></ul></ul><ul><li>“ Brute Force” approach does not scale well </li></ul><ul><ul><li>Multiple TBs of data </li></ul></ul><ul><ul><li>100s of millions of unique viewers </li></ul></ul>
    25. 25. Engineering Solution: Frequency Availability Calculation <ul><li>Use map/reduce approach </li></ul><ul><ul><li>Distribute data among SPUs </li></ul></ul><ul><ul><li>Generate frequency histograms </li></ul></ul><ul><ul><li>Aggregate </li></ul></ul>
    26. 26. Frequency Histogram Processing in the Netezza Gigabit Ethernet Fabric Netezza Performance Server® System SPU C12 C13 F12 F13 G12 G13 F14 G14 C21 F21 F22 G21 G22 C23 F23 G23 C30 C31 F31 G30 G31 C39 F38 F39 G38 G39 C40 F40 G40 C6 C7 F6 F7 G6 G7 C14 F30 C38 C22 D B H F O L S Q U C A G E M J R P V T SPU SPU SPU SPU N K SMP HOST A1 A2 A3 A4 A5 A B C D E F G H 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 FPGA FPGA FPGA FPGA FPGA C6 F7 C12 F13 C38 F39 G13 G22
    27. 27. Demonstrated Performance <ul><li>One curve per 1-5 minutes </li></ul><ul><ul><li>Multiple curves simultaneously </li></ul></ul><ul><li>Smooth campaign delivery </li></ul>
    28. 28. Conclusions <ul><li>P/V curves provide the powerful analysis that Market model demands </li></ul><ul><li>Netezza data appliance supports generation of P/V curves in high volumes </li></ul>
    29. 29. Q & A

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