What about streaming data?
|
|
- Ashley Chambers
- 6 years ago
- Views:
Transcription
1 What about streaming data? 1
2 The Stream Model Data enters at a rapid rate from one or more input ports Such data are called stream tuples The system cannot store the entire (infinite) stream Distribution changes over time How do you make critical calculations (in real time) about the stream using a limited amount of (secondary) memory? 2
3 General Stream Processing Model Streams Entering... 1, 5, 2, 7, 0, 9, 3... a, r, v, t, y, h, b... 0, 0, 1, 0, 1, 1, 0 time Ad hoc Queries Standing Queries Processor Output Limited Working Storage Archival Storage 3
4 Applications In general, stream processing is important for applications where New data arrives frequently Important queries tend to ask about the most recent data, or summaries of data Mining query streams Google wants to know what queries are more frequent today than yesterday Mining click streams Yahoo wants to know which of its pages are getting an unusual number of hits in the past hour Mining social network news feeds Look for trending topics on Twitter, Facebook 4
5 Sliding Windows A useful model of stream processing is that queries are about a window of length N the N most recent elements received (or last N time units) Interesting case: N is still so large that it cannot be stored in memory, or even on disk Or, there are so many streams that windows for all cannot be stored 5
6 Sliding Windows: Single Stream q w e r t y u i o p a s d f g h j k l z x c v b n m q w e r t y u i o p a s d f g h j k l z x c v b n m Past Future Window size = 5 Step = 21 6
7 Example: Averages Stream of integers Window size = N Standing query: What is the average of the integers in the window? For the first N inputs average = sum/count Afterward, when a new input i arrives Update average: average + (i j)/n where j is the oldest value in the window 7
8 Let s start simple: Counting bits Problem Given a stream of 0 s and 1 s Answer queries of the form how many 1 s in the last k bits? where k N Obvious solution Store the most recent N bits (i.e., window size = N) When a new bit arrives, discard the N +1 st bit Real Problem Slow need to scan k bits to count What if we cannot afford to store N bits? E.g., we are processing 1 billion streams and N = 1 billion Estimate with an approximate answer N = 6 8
9 Example: Exponentially Increasing Blocks 9
10 How Good Is This? Store O(log 22 N) bits per stream (<< N bits) O(log N) counts of log N bits Gives approximate answer How accurate? If 1 s are evenly distributed, never off by more than 50% Otherwise error can be unbounded! E.g, all the 1 s are in the unknown block! 10
11 DGIM* Method Store O(log 22 N) bits per stream (<< N bits) O(log N) counts of log N bits Gives approximate answer Never off by more than 50% Error factor can be reduced to any fraction > 0, with more complicated algorithm and proportionally more stored bits Trick: Block size is based on count of 1s instead of fix sized blocks *Datar, Gionis, Indyk, and Motwani (Stanford) 11
12 DGIM: Timestamps Each bit in the stream has a timestamp, starting 1, 2, Record timestamps modulo N (the window size), so we can represent any relevant timestamp in O(log 2 N) bits If N = 1M, then we need 20 bits 12
13 DGIM: Buckets A bucket is a record consisting of The timestamp of its right (most recent) end O(log 2 N) bits The number of 1 s between its beginning and end Number of 1 s must be a power of 2 O(log 2 log 2 N) bits 13
14 Representing a Stream by Buckets Either one or two buckets with the same power of 2 number of 1 s Buckets do not overlap in timestamps Buckets are sorted by size (# of 1 s). Earlier buckets are not smaller than later buckets Buckets disappear when their (right) end time is > N time units in the past O(log 2 N) buckets 14
15 Updating Buckets When a new bit comes in, drop the last (oldest) bucket if its (right) end time is prior to N time units before the current time If the current bit is 0, no other changes are needed 15
16 Updating Buckets If the current bit is 1: Create a new bucket of size 1, for just this bit End timestamp = current time If there are now three buckets of size 1, combine the oldest two into a bucket of size 2 If there are now three buckets of size 2, combine the oldest two into a bucket of size 4 And so on 16
17 Example 17
18 Querying To estimate the number of 1 s in the most recent N bits: Sum the size of all buckets but the last size means the number of 1 s in the bucket Add half the size of the last bucket NOTE: We do not know how many 1 s of the last bucket are still within the wanted window 18
19 Example: Bucketized stream How many 1 s? ½*16 = 36 19
20 How good is the scheme? Suppose the last bucket has size 2 r Assuming 2 r 1 (i.e., half) of its 1 s are still within the window, we make an error of at most 2 r 1 Since there is at least one bucket of each of the sizes less than 2 r, the true sum is at least r 1 = 2 r 1 actually 2 r as the first bit of the last bucket is always 1 Thus, error is at most 50% 20
21 Extensions How many 1 s in the last k where k < N? Find earliest bucket B that overlaps with k Number of 1 s is the sum of sizes of more recent buckets + ½ size of B How about counting integers (sum of the last k elements)? 21
22 Counting positive integers Stream of positive integers We want the sum of the last k elements Amazon: Avg price of last k sales Solution If you know all integers have at most m bits (i.e. integer value upto 2 m ) Treat each of the m bits of each integer as a separate stream Use DGIM to count 1s in each bit stream The sum is = 22
23 Filtering data streams Consider a web crawler It keeps, centrally, a list of all the URLs it has found so far It assigns these URLs to any of a number of parallel tasks These tasks stream back the URLs they find in the links they discover on a page It needs to filter out those URLs it has seen before 23
24 Filtering data streams Recall: Each element of data stream is a tuple Given a list of keys S, determine which tuples of stream are in S Naïve solution: Hash table But suppose we do not have enough memory to store all of S in a hash table E.g., we might be processing millions of filters on the same stream 24
25 Naïve solution Given a set of keys S that we want to filter Create a bit array B of n bits Initialize to all 0s Choose a hash function h with range [0,n) Hash each member of s S to one of n buckets, and set that bit to 1, i.e., B[h(s)] = 1 Hash each element a of the stream and output only those that hash to bit that was set to 1 Output a if B[h(a)] == 1 25
26 Naïve solution S = {A1, A2, A3} H(A1) = 7 H(A2) = 0 H(A3) = Bit array H(a1) = 1. Since bit at position 1 is 0, drop a1. H(a2) = 7. Since bit at position 7 is 1, output a2. 26
27 Naïve solution False positives? Will invalid item passed through the filter? If the item is NOT in S, it may still be output Why? Collision False negatives? Will valid items be filtered out? If item is in S, we surely output it 27
28 Analysis on False Positives: Throwing darts If we throw m darts into n equally likely targets, what is the probability that a target gets at least one dart? In our case: Targets = bits/buckets Darts = hash values of items Equals 1/e as n 1 ( 1 1/n ) n (m/n) m = n(m/n) = 1 e m/n Prob some target X not hit by a dart Prob at least one dart hits target X 28
29 Analysis: Throwing darts Fraction of 1 s in the array B = probability of false positives = 1 e m/n Example: 10 9 darts, targets Fraction of 1 s in B = 1 e 1/8 = Lower than 1/8 = (why?) 29
30 Bloom filter Consider: S = m; B = n Use k independent hash functions h 1,, h k Initialization Set B to all 0 s Hash each element s S k times Run time using each hash function h i,set B[h i (s)] = 1 (for each i= 1,.., k) When a stream element with key x arrives If B[h i (x)] == 1 for all i= 1,.., k then declare that x is in S That is, x hashes to a bucket set to 1 for every hash function h i (x) Otherwise, discard the element x 30
31 Bloom filter S = {A1, A2, A3} H1(A1) = 7 H1(A2) = 0 H1(A3) = 2 H = {H1, H2} H2(A1) = 2 H2(A2) = 1 H2(A3) = Bit array H1(a1) = 1 H2(a1) = 3 Since bit at position 3 is 0, drop a1 H1(a2) = 7 H2(a2) = 5 Since bits at positions 5 and 7 are both 1, output a2 (potentially a false positive) 31
32 Bloom filter: Analysis What fraction of the bit vector B are 1 s? Throwing k mdarts at n targets So fraction of 1 s is (1 e km/n ) But we have k independent hash functions and we only let the element x through if all k hash element x to a bucket of value 1 So false positive probability = (1 e km/n ) k 32
33 Bloom Filter Bloom filters guarantee no false negatives and use limited memory Great for pre processing before more expensive checks Suitable for hardware implementation Hash function computations can be parallelized Is it better to have 1 big B or k small B s? It is the same: is (1 e km/n ) k vs (1 e m/(n/k) ) k But keeping 1 big B is simpler 33
34 So far, We have looked at some operations on data streams when memory is limited Counting bits Counting integers Filtering streams 34
35 Online (Web) advertising: Streaming queries, dynamic answers 35
36 Multi billion dollar industry! Online advertisements 36
37 Banner Ads 37
38 Pay Per Impression Pay per 1000 impressions (PPM): advertiser pays each time ad is displayed Models existing standards from magazine, radio, television Main business model for banner ads to date Untargeted to demographically targeted Low clickthrough rates Low ROI for advertisers Banner blindness : effectiveness drops with user experience Barrier to entry for small advertisers Contracts negotiated on a case by case basis with large minimums (typically, a few thousand dollars per month) 38
39 Pay Per Click Pay per click (PPC): advertiser pays only when user clicks on ad Common in search advertising Middle ground between PPM and PPA Does not require host to trust advertiser Provides incentives for host to improve ad displays 39
40 Sponsored (Performance based) advertising Advertisements sold automatically through auctions: Advertisers bids (indicating value for clicks) on search keywords Low barrier to entry Increased transparency of mechanism Keyword bidding allowed increased targeting opportunities When someone searches for that keyword, a mechanism selects the best ad to display Advertiser is charged only if the ad is clicked on Example: Google s Adwords 40
41 Ads vs. search results 41
42 42
43 Algorithmic Challenges Interesting problems What ads to show for a search? If I m an advertiser, which search terms should I bid on and how much to bid? Not our focus 43
44 Scenario A stream of queries arrives at the search engine q1, q2, q3, Several advertisers bid on each query When query q i arrives, search engine must pick a subset of advertisers whose ads are shown Goal: maximize search engine s revenues Clearly we need an online algorithm! 44
45 How to maximize revenue? Greedy solution: Initial GoTo model (First price auction) Advertise based on highest bidders Upon a click, advertiser is charged a price equal to his bid Used first by Overture/Yahoo! Complications Each ad has a different likelihood of being clicked Advertiser 1 bids $2, click probability = 0.1 Advertiser 2 bids $1, click probability = 0.5 Clickthrough rate measured historically 45
46 How to maximize revenue? Google model: Second price auction Advertisers ranked according to bid and clickthrough rate (CTR) Use the expected revenue per click Upon a click, advertiser is charged minimum amount required to maintain position in ranking 46
47 The Adwords Innovation Advertiser Bid CTR Bid * CTR A $1.00 1% 1 cent B $0.75 2% 1.5 cents C $ % cents Click through rate Expected revenue 47
48 The Adwords Innovation Advertiser Bid CTR Bid * CTR B $0.75 2% 1.5 cents C $ % cents A $1.00 1% 1 cent Instead of sorting advertisers by bid, sort by expected revenue! 48
49 More complications Each advertiser has a limited budget Search engine guarantees that the advertiser will not be charged more than their daily budget Solution: Ensure budget is not exhausted too quickly unnecessarily 49
50 Simplified model (for now) All bids are 0 or 1 Each advertiser has the same budget B One ad per query All ads are equally likely to be clicked Let s try the greedy algorithm Arbitrarily pick an eligible advertiser (bid 1) for each keyword 50
51 Bad scenario for greedy Two advertisers A and B A bids on query x, B bids on x and y Both have budgets of $4 Query stream: x xxxy yyy Worst case greedy choice: B BBB Optimal: A AAAB BBB Competitive ratio min all possible inputs Revenue greedy /Revenue opt =½ 51
52 Adwords Problem Given A set of bids by advertisers for search queries A click through rate for each advertise query pair A budget for each advertiser (say for 1 day, 1 month, ) A limit on the number of ads to be displayed with each search query, N Respond to each search query with a set of advertisers such that The size of the set is no larger than N Each advertiser has bid on the search query Each advertiser has enough budget left to pay for the ad if it is clicked 52
53 BALANCE algorithm [Mehta, Saberi, Vazirani, and Vazirani] For each query, pick the advertiser with the largest unspent budget Break ties arbitrarily but deterministically A. Mehta, A. Saberi, U. Vazirani, V. Vazirani, Adwords and Generalized On line Matching, Journal of the ACM (2007). Conference version appeared in IEEE Symposium on Foundations of Computer Science (2005). 53
54 Example: BALANCE Two advertisers A and B A bids on query x, B bids on x and y Both have budgets of $4 Query stream: x xxxy yyy BALANCE choice: A B A B BB Optimal: A AAAB BBB Competitive ratio = ¾ For BALANCE with 2 advertisers 54
55 Analyzing BALANCE Consider simple case: two advertisers, A 1 and A 2, each with budget B (assume B 1) Assume optimal solution exhausts both advertisers budgets BALANCE must exhaust at least one advertiser s budget If not, we can allocate more queries Assume BALANCE exhausts A 2 s budget 55
56 Analyzing Balance B Queries allocated to A 1 in optimal solution Queries allocated to A 2 in optimal solution A 1 A 2 Opt revenue = 2B BALANCE revenue = B+y x B Goal: Show that y B/2 y A 1 A 2 x BAL: B+B/2 = 3B/2 BAL/OPT = 3/4 Competitive Ratio = 3/4 56
57 Analyzing Balance B Queries allocated to A 1 in optimal solution Queries allocated to A 2 in optimal solution A 1 A 2 Case 1: BALANCE assigns at least B/2 blue queries to A1. So, y B/2 x y A 1 A 2 x B Case 2: BALANCE assigns more than B/2 blue queries to A 2 At that time, A 2 s unspent budget must have been at least as big as A 1 s That means at least as many queries have been assigned to A 1 as to A 2 At this point, we have already assigned at least B/2 queries to A 2. S, y B/2 57
58 General Result: > 2 advertisers Worst competitive ratio of BALANCE is 1 1/e = approx Interestingly, no online algorithm has a better competitive ratio! 58
59 General version of problem So far, all bids = 1, all budgets equal (=B) Arbitrary bids, budgets Consider query q, advertiser i Bid = x i Budget = b i BALANCE can be terrible Consider two advertisers A 1 and A 2 A 1 : x 1 = 1, b 1 = 110 A 2 : x 2 = 10, b 2 = 100 Suppose we see 10 instances of q BALANCE always selects A 1 and earns 10 Optimal earns 100! Competitive ratio: 0.1!! Generalized BALANCE scheme with competitive ratio of 1 1/e Modify BALANCE to be based on fraction of unspent budget, and some others. 59
60 Actual problem is even more sophisticated Upon each search, Interested advertisers are selected from database using keyword matching algorithm Budget allocation algorithm retains interested advertisers with sufficient budget Advertisers compete for ad slots in allocation mechanism Upon click, advertiser charged with pricing scheme CTR updated according to CTR learning algorithm for future auctions 60
61 Conclusion Data streaming applications are becoming increasingly important Online algorithms are needed We have looked at data stream processing in general, and web advertising in particular 61
62 Acknowledgements The slides is adapted from numerous sources (not all listed here): Jure and Ullman (Stanford) Bergemann (Yale) 62
CS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 2 Classic model of algorithms You get to see the entire input, then compute some function of it In this context,
More informationClassic model of algorithms
Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit
More informationJeffrey D. Ullman Stanford University/Infolab. Slides mostly developed by Anand Rajaraman
Jeffrey D. Ullman Stanford University/Infolab Slides mostly developed by Anand Rajaraman 2 Classic model of (offline) algorithms: You get to see the entire input, then compute some function of it. Online
More informationCS 345 Data Mining. Online algorithms Search advertising
CS 345 Data Mining Online algorithms Search advertising Online algorithms Classic model of algorithms You get to see the entire input, then compute some function of it In this context, offline algorithm
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 3/5/18 Jure Leskovec, Stanford C246: Mining Massive Datasets 2 High dim. data Graph data Infinite data Machine
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University.
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 2 Classic model of algorithms You get to see the entire input, then compute some function of it In this context,
More informationIntroduction to Data Mining
Introduction to Data Mining Lecture #16: Advertising Seoul National University 1 In This Lecture Learn the online bipartite matching problem, the greedy algorithm of it, and the notion of competitive ratio
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 3/8/2015 Jure Leskovec, Stanford C246: Mining Massive Datasets 2 High dim. data Graph data Infinite data Machine
More informationCS425: Algorithms for Web Scale Data
CS425: Algorithms for Web Scale Data Most of the slides are from the Mining of Massive Datasets book. These slides have been modified for CS425. The original slides can be accessed at: www.mmds.org J.
More informationMining of Massive Datasets Jure Leskovec, AnandRajaraman, Jeff Ullman Stanford University
Note to other teachers and users of these slides: We would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit
More informationOnline Advertising and Ad Auctions at Google
Online Advertising and Ad Auctions at Google Vahab Mirrokni Google Research, New York Traditional Advertising At the beginning: Traditional Ads Posters, Magazines, Newspapers, Billboards. What is being
More informationA Survey of Sponsored Search Advertising in Large Commercial Search Engines. George Trimponias, CSE
A Survey of Sponsored Search Advertising in Large Commercial Search Engines George Trimponias, CSE Outline Introduction Structure of Sponsored Search Advertising (SSA) Practical Issues in SSA Historical
More informationAdvertising on the Web
Chapter 8 Advertising on the Web One of the big surprises of the 21st century has been the ability of all sorts of interesting Web applications to support themselves through advertising, rather than subscription.
More informationKeyword Analysis. Section 1: Google Forecast. Example, inc
Section 1: Google Forecast Below is a forecast for some example leather keywords that we recommend focus on. Before the forecast is viewed, keep in mind, that Google bases its numbers on average performance
More informationIntroduction to computational advertising
Introduction to computational advertising Bogdan Cautis page 0 Outline From IR to IS Advertising on the Web The evolution of Web advertising Terminology History Advertising setting and problems Display
More informationSponsored Search: Theory and Practice
Sponsored Search: Theory and Practice Jan Pedersen Chief Scientist Yahoo! Search and Marketplace Group 1 10 August 2006 Outline Why online advertising is important A brief history of sponsored search An
More informationMarketing and CS. Ranking Ad s on Search Engines. Enticing you to buy a product. Target customers. Traditional vs Modern Media.
Enticing you to buy a product Marketing and CS 1. What is the content of the ad? 2. Where to advertise? TV, radio, newspaper, magazine, internet, 3. Who is the target audience/customers? Philip Chan Which
More informationSearch engine advertising
Search engine advertising Hal Varian Online advertising Banner ads (Doubleclick) Standardized ad shapes with images Loosely related to content Context linked ads (Google AdSense) Related to content on
More informationP P C G L O S S A R Y PPC GLOSSARY
The following is a glossary of terms which you will see as you explore the world of PPC. A ACCELERATED AD DELIVERY A method of ad delivery which endeavours to show an ad as often as possible until the
More informationInternet Advertising and Generalized Second Price Auctions
Internet Advertising and Generalized Second Price Auctions Daniel R. 1 1 Department of Economics University of Maryland, College Park. November, 2017 / Econ415 What is sold and why. Who buys. : Progression
More informationSponsored Search Markets
COMP323 Introduction to Computational Game Theory Sponsored Search Markets Paul G. Spirakis Department of Computer Science University of Liverpool Paul G. Spirakis (U. Liverpool) Sponsored Search Markets
More informationThe Beginners Guide : Google Adwords. e-book. A Key Principles publication
The Beginners Guide : Google Adwords e-book A Key Principles publication The Beginners Guide: Google Adwords By Jackie Key, Managing Director, Key Principles About the Author Jackie Key is the Managing
More informationCS246: Mining Massive Datasets Jure Leskovec, Stanford University
CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu Web advertising We discussed how to match advertisers to queries in real- time But we did not discuss how to
More informationCase study I: Advertising on the Web
1 Case study I: Advertisig o the Web Geoveva Vargas-Solar http://www.vargas-solar.com/big-data-aalytics Frech Coucil of Scietific Research, LIG & LAFMIA Labs Motevideo, 22 d November 4 th December, 2015
More informationGOOGLE ADWORDS. Optimizing Online Advertising x The Analytics Edge
TM GOOGLE ADWORDS Optimizing Online Advertising 15.071x The Analytics Edge Google Inc. Provides products and services related to the Internet Mission: to organize the world s information and make it universally
More informationHABIT 2: Know and Love Quality Score
HABIT 2: Know and Love Quality Score IMPROVING QUALITY SCORE: THE VALUE OF BEING MORE RELEVANT RAISING QUALITY SCORE TO INCREASE EXPOSURE, LOWER COSTS, AND GENERATE MORE CONVERSIONS WHY SHOULD YOU CARE
More informationProvided By WealthyAffiliate.com
The Content within this guide is protected by Copyright and is the sole property of Kyle & Carson of Niche Marketing Inc. This guide may not be modified, copied, or sold. You may however, give this guide
More informationarxiv: v2 [cs.ir] 25 Mar 2014
A Novel Method to Calculate Click Through Rate for Sponsored Search arxiv:1403.5771v2 [cs.ir] 25 Mar 2014 Rahul Gupta, Gitansh Khirbat and Sanjay Singh August 26, 2018 Abstract Sponsored search adopts
More informationData Science Challenges for Online Advertising A Survey on Methods and Applications from a Machine Learning Perspective
Data Science Challenges for Online Advertising A Survey on Methods and Applications from a Machine Learning Perspective IWD2016 Dublin, March 2016 Online Advertising Landscape [Introduction to Computational
More informationPRIMISTA ONLINE MARKETING MADE EASY
PRIMISTA ONLINE MARKETING MADE EASY Agenda Presentation Topics: 1. Introduction to Targeted Marketing 2. Ad Distribution Network 3. Primary Benefits of Targeted Marketing 4. Online Advertising Stats and
More informationOPTIMAL PROSPECTING E-COMMERCE ADVERTISERS FOR BY ZALSTER.COM
OPTIMAL PROSPECTING FOR E-COMMERCE ADVERTISERS BY ZALSTER.COM v 1.0 - released 11/7-2017 CONTENT: 1. AUDIENCE STRATEGIES 2. CAMPAIGN STRUCTURE 3. BIDDING & BUDGETING 4. AD CREATIVE STRATEGIES Before you
More informationOPTIMAL PROSPECTING APPS FOR BY ZALSTER.COM
OPTIMAL PROSPECTING FOR APPS BY ZALSTER.COM v 1.0 - released 11/7-2017 CONTENT: 1. AUDIENCE STRATEGIES 2. CAMPAIGN STRUCTURE 3. BIDDING & BUDGETING 4. AD CREATIVE STRATEGIES Before you start, make sure
More informationCSE 158 Lecture 14. Web Mining and Recommender Systems. AdWords
CSE 58 Lecture 4 Web Mining and Recommender Systems AdWords Advertising. We can t recommend everybody the same thing (even if they all want it!) So far, we have an algorithm that takes budgets into account,
More informationIntroduction. We ll be going through some key notions in paid advertising through the four chapters of our guide.
1 Introduction Paid Advertising is a fantastic way to drive more traffic to your site and boost revenue, but it can turn out to be expensive if you aren t careful. So, how do you make the most out of your
More informationWhat Makes Google Tick?
What Makes Google Tick? Greg Taylor Oxford Internet Institute, University of Oxford, Oxford, UK This short article is an educational piece for aspiring economists, written for and published in Economic
More informationModified Truthful Greedy Mechanisms for Dynamic Virtual Machine Provisioning and Allocation in Clouds
RESEARCH ARTICLE International Journal of Computer Techniques Volume 4 Issue 4, July August 2017 Modified Truthful Greedy Mechanisms for Dynamic Virtual Machine Provisioning and Allocation in Clouds 1
More informationCSC304: Algorithmic Game Theory and Mechanism Design Fall 2016
CSC304: Algorithmic Game Theory and Mechanism Design Fall 2016 Allan Borodin (instructor) Tyrone Strangway and Young Wu (TAs) November 2, 2016 1 / 14 Lecture 15 Announcements Office hours: Tuesdays 3:30-4:30
More informationSell More, Pay Less: Drive Conversions with Unrelated Keywords
851 SW 6th Ave., Suite 1600 Portland, OR 97204 1.503.294.7025 fax: 1.503.294.7 130 Webtrends Sales 1.888.932.8736 sales@webtrends.com Europe, Middle East, Africa +44 (0) 1784 415 700 emea@webtrends.com
More informationIntroduction. If you are curious of what to expect, then read on
Introduction If you are reading this, then you re probably preparing to take the Advertising Fundamentals exam and are not quite sure of what s in store for you? or you feel a little under confident about
More informationIntermediate Google AdWords Instructors: Alice Kassinger
Intermediate Google AdWords Instructors: Alice Kassinger Class Objectives: Understand how Ads show on the results page Analyze the progress and success of your Ads Learn how to improve your Ad Rank with
More informationReverse-engineering AdWords Quality Score factors
Reverse-engineering AdWords Quality Score factors Google recently launched a new version of the AdWords API that allows users to download Quality Score information at scale, so contributor Brad Geddes
More informationARE YOU SPENDING YOUR PPC BUDGET WISELY? BEST PRACTICES AND CREATIVE TIPS FOR PPC BUDGET MANAGEMENT
ARE YOU SPENDING YOUR PPC BUDGET WISELY? BEST PRACTICES AND CREATIVE TIPS FOR PPC BUDGET MANAGEMENT In pay-per-click marketing, as with so many things in life, you have to spend money to make money. But
More informationIntroduction to computational advertising. Bogdan Cautis
Introduction to computational advertising Bogdan Cautis page 0 Outline From IR to IS Advertising on the Web The evolution of Web advertising Terminology History Advertising setting and problems Display
More informationData Drives Social Performance
Data Drives Social Performance The Benchmark Study on Organic Publishing to Social Networks August 2014 52 Vanderbilt Ave, 12th Floor New York, NY 10017 212.883.9844 www.socialflow.com 2014 SocialFlow
More informationAdvertisement Allocation for Generalized Second Pricing Schemes
Advertisement Allocation for Generalized Second Pricing Schemes Ashish Goel a, Mohammad Mahdian b, Hamid Nazerzadeh,c, Amin Saberi a a Management Science and Engineering Department, Stanford University
More informationBidding for Sponsored Link Advertisements at Internet
Bidding for Sponsored Link Advertisements at Internet Search Engines Benjamin Edelman Portions with Michael Ostrovsky and Michael Schwarz Industrial Organization Student Seminar September 2006 Project
More informationADWORDS IS AN AUTOMATED ONLINE AUCTION. WITHIN A CAMPAIGN, YOU IDENTIFY KEYWORDS THAT TRIGGER YOUR ADS TO APPEAR IN SPECIFIC SEARCH RESULTS.!
1. What is AdWords? ADWORDS IS AN AUTOMATED ONLINE AUCTION. WITHIN A CAMPAIGN, YOU IDENTIFY KEYWORDS THAT TRIGGER YOUR ADS TO APPEAR IN SPECIFIC SEARCH RESULTS. This type of campaign is called a Search
More informationHow to Use PPC Advertising to Grow Your Pool Business!
How to Use PPC Advertising to Grow Your Pool Business! Welcome From print materials to online marketing, there is no shortage of ways to spend your marketing budget. And whether your annual budget is $1000
More informationStrategic Analytics Framework
Strategic Analytics Framework It s all about taking actions based on insight not about data Evolution of Analytics From old school to new school PAGEVIEWS Term weblog coined 1998 Google started 1998 Netscape
More informationOCTOBOARD INTRO. Put your metrics around these practical questions and make sense out of your Google Ads Analytics!
OCTOBOARD INTRO Your journey to Google AdWords success doesn t stop once you master the art of creating a compelling Google ad. The real journey starts with Data and ends with Insights. You need loads
More informationOnline advertising. A brief introduction to Ad-networks Prepared by: Anna Alikhani
Online advertising A brief introduction to Ad-networks Prepared by: Anna Alikhani Ad Networking or Banner Dealing Business Overview in Iran It seems that the market is ruled with old dinosaurs, with the
More informationwho we are A few of the companies we are proud to call partners.
who we are A few of the companies we are proud to call partners. PLATINUM LEVEL CLIENT certifications & recognitions Built on earned expertise at the individual level. Fish Where The Fish Are Start by
More informationExploitation and Exploration in a Performance based Contextual Advertising System
Exploitation and Exploration in a Performance based Contextual Advertising System Wei Li, Xuerui Wang, Ruofei Zhang, Ying Cui Jianchang Mao and Rong Jin Performance-based online advertising Performance
More informationIntroduction. What is Cryptocurrency?
Table of Contents 1. Introduction 1.1. Cryptocurrency 1.2. ERC20 Standard 1.3. P2P Networks 2. Adverx (AVX) 2.1. Token Specifications 2.2. Token Allocation 3. Real-time bidding (RTB) 3.1. How RTB works
More informationCS364B: Frontiers in Mechanism Design Lecture #17: Part I: Demand Reduction in Multi-Unit Auctions Revisited
CS364B: Frontiers in Mechanism Design Lecture #17: Part I: Demand Reduction in Multi-Unit Auctions Revisited Tim Roughgarden March 5, 014 1 Recall: Multi-Unit Auctions The last several lectures focused
More informationVCG in Theory and Practice
1 2 3 4 VCG in Theory and Practice Hal R. Varian Christopher Harris Google, Inc. May 2013 Revised: December 26, 2013 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 It is now common to sell online ads using
More informationMalaysia, Batu Pahat, Johor, Malaysia. Keywords: RFID, Bloom filter, data filtering, sensor, supply chain
An Efficient Approach to Processing Massive RFID Data in Supply Chain Hairulnizam Mahdin 1, a, Mohd Farhan Md Fudzee 2,b, Azizul Azhar Ramli 3, c, Mohd Aizi Salamat 4,d and Shahreen Kasim 5,e 1,2,3,4,5
More informationThe Guide to Influencer Marketing Automation
The Guide to Influencer Marketing Automation Here s an unfortunate fact: Consumers trust Congress more than brand advertising 1. Sad but true, though hardly surprising. Consumers know that marketers monitor
More informationIntroduction to Online Advertising
Introduction to Online Advertising 2018 Peter Velikonja Head of Research, Koios LLC Online Advertising Compared to print or television advertising, online advertising is relatively inexpensive; print and
More informationMarch. 2 It is even more sophisticated than this since Google has quite a bit of information about the
Position Auctions One application of auctions which has become increasingly important over the last few years is their application to online advertising. Hallerman 1 estimates that online advertising revenues
More informationMobile Marketing and PPC. What SMBs Should Be Doing, NOW! A WordStream Guide
Mobile Marketing and PPC What SMBs Should Be Doing, NOW! A WordStream Guide Mobile Marketing and PPC What SMBs Should Be Doing, NOW! Did you know that just under half of all Internet searches are performed
More informationLocal Adwords Academy Class Notes - Lesson #9
Local Adwords Academy Class Notes - Lesson #9 YouTube Ads This lesson is about YouTube Ads. This is something that you can apply for many years to come. In previous lessons we have covered - Search Ads
More informationWhat Marketing DNA Tells You About Managing Google AdWords
What Marketing DNA Tells You About Managing Google AdWords Get More Visitors for Less Money On The World s Most Amazing Advertising Machine and do it YOUR Way By Perry Marshall www. www.perrymarshall.com
More informationBUS 168 Chapter 6 - E-commerce Marketing Concepts: Social, Mobile, Local
Consumers Online: The Internet Audience and Consumer Behavior Around 84% of U.S. adults use the Internet in 2015 Intensity and scope of use both increasing Some demographic groups have much higher percentages
More informationDIGITAL MARKETING DATA SHEET CHANNELADVISOR DIGITAL MARKETING ENABLES RETAILERS TO: And many more
DIGITAL MARKETING DATA SHEET With more and more digital marketing channels introduced each year, it can be hard to keep track of every place you re advertising let alone how each of your products is performing
More informationMathCelebrity Google Adsense Study Guide
MathCelebrity Google Adsense Study Guide MathCelebrity May 25, 2014 Contents 1 Overview 3 2 Prelim Test 3 3 Introduction 3 4 Understanding CPC and CPM 3 4.1 CPC and CPM.......................................................
More informationAMS. Amazon Marketing Services
AMS Amazon Marketing Services Table of Contents What is Amazon Marketing Services (AMS)? 3 Understanding AMS Ad Products 5 Why Use AMS? 6 Path to Product Page 8 Campaign ROI: Manage AMS for Strong Sales
More informationGoogle AdWords Remarketing
Google AdWords Remarketing AdWords remarketing is not only great for driving visitors back to your website to convert but is also great at improving your branding which in effect increases conversion and
More informationGoogle AdWords Remarketing
Google AdWords Remarketing AdWords remarketing is not only great for driving visitors back to your website to convert but is also great at improving your branding which in effect increases conversion and
More informationHAVE YOU NOTICED A CHANGE IN RANKINGS? GOOGLE S SHAKING THINGS UP WITH ITS NEW LOCAL SEARCH FILTERS
1 HAVE YOU NOTICED A CHANGE IN RANKINGS? GOOGLE S SHAKING THINGS UP WITH ITS NEW LOCAL SEARCH FILTERS 2 BOOST THE BOTTOM LINE OF YOUR BUSINESS WITH GOOGLE S NEW INSTANT CHAT FEATURE 3 GOOGLE HAS DECIDED
More informationA Bid for Every Auction. Implementing and Improving AdWords Automated Bidding
A Bid for Every Auction Implementing and Improving AdWords Automated Bidding Ten Automated Bidding Takeaways The Advantages of Auction-time Bidding 1 Bid to the user s context, as close to auction-time
More informationMATH 240: Problem Set 4
MATH 240: Problem Set 4 You may either complete this problem set alone or in partners. If you complete it in partners, only one copy needs to be submitted. Show all your work. This problem set is due on:
More informationThe 20-Minute PPC Work Week
Your hub for all things ppc The 20-Minute PPC Work Week Making the Most of Your PPC Account in Minimal Time 3 Habit 1: Regular Account Activity The 20-Minute PPC Work Week Making the Most of Your PPC Account
More informationA Guide to Using Google Ads. v 1.3 Updated October 11, 2018
v 1.3 Updated October 11, 2018 Table of Contents 1. Introduction......................................................................3 2. Glossary of Terms.................................................................
More informationLinkedIn and Google Adwords Practical Tips
LinkedIn and Google Adwords Daniella Boni Marketing Director Tom Mulligan Director, Product Management and RFP Callan Capital Brandes Investment Partners LinkedIn and Google Adwords Reaching Your Audience
More informationPAID SEARCH. be seen. PAID SEARCH FEATURES INCLUDE:
DATA SHEET be seen. PAID SEARCH ChannelAdvisor s Paid Search solution redefines search engine marketing, giving you the ability to manage paid search campaigns for Google, Yahoo! and Bing from one interface.
More informationDrive More Phone Calls. With Digital Display Advertising ` ForLawFirmsOnly Marketing, Inc.
Drive More Phone Calls With Digital Display Advertising ForLawFirmsOnly Marketing, Inc. 855-943-8736 marketing@forlawfirmsonly.com Traffic Strategies That Convert Clients We want to help you acquire more
More informationSecrets to get Dirt Cheap Traffic from Bing And Facebook
Secrets to get Dirt Cheap Traffic from Bing And Facebook CPA Arbitrage Code 2 Disclaimer While all attempts have been made to verify the information provided in this publication, the publisher does not
More informationTitle: A Mechanism for Fair Distribution of Resources with Application to Sponsored Search
Title: A Mechanism for Fair Distribution of Resources with Application to Sponsored Search Authors: Evgenia Christoforou, IMDEA Networks Institute, Madrid, Spain and Universidad Carlos III, Madrid, Spain
More informationDigital Advertising 101. Jeremy Kagan, Associate Professor, Digital Marketing & CEO of Pricing Engine Inc.
Digital Advertising 101 Jeremy Kagan, Associate Professor, Digital Marketing & CEO of Pricing Engine Inc. jeremy@pricingengine.com Twitter: @KaganJ Online Marketing: Why do we care? GROWING EFFECTIVE DIFFERENT
More informationEpic PPC Fails and how to Avoid Them
Although PPC and search marketing are both vital to a company s success, it s amazing to see the massive mistakes some brands still make today. Epic PPC Fails and how to Avoid Them Although PPC and search
More informationText Analysis and Search Analytics
Text Analysis and Search Analytics Text Analysis Outline Text analysis in the online environment Steps in text analysis Analysis of Otto s reviews Search Analytics Text Analysis a lot of unstructured data
More informationCS269I: Incentives in Computer Science Lecture #16: Revenue-Maximizing Auctions
CS269I: Incentives in Computer Science Lecture #16: Revenue-Maximizing Auctions Tim Roughgarden November 16, 2016 1 Revenue Maximization and Bayesian Analysis Thus far, we ve focused on the design of auctions
More informationHow Much Does Google AdWords Cost?
GUIDE How Much Does Google AdWords Cost? 1 Introduction How much does Google AdWords cost? It s a reasonable question, and one we hear all the time, especially from newcomers to paid search. After all,
More informationUNDERSTANDING GOOGLE S AD EXTENSIONS
Google is constantly testing, innovating and rolling out new ways to present advertisements to online shoppers in an effort to walk that fine line between helping searchers find what they need and keeping
More informationWhat is SEM? Paid search, Google ads, and pay-per-click advertising
What is SEM? Paid search, Google ads, and pay-per-click advertising are a few phrases you have probably heard that are interchangeable with the term search engine marketing or SEM. SEM refers to paid ads
More informationA Study of Compact Reserve Pricing Languages
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) A Study of Compact Reserve Pricing Languages MohammadHossein Bateni, Hossein Esfandiari, Vahab Mirrokni, Saeed Seddighin
More informationRemarketing. Google AdWords. What is Google AdWords Remarketing?
Google AdWords Remarketing AdWords remarketing is not only great for driving visitors back to your website to convert but is also great at improving your branding which in effect increases conversion and
More informationDigital Capabilities Packet
Digital Capabilities Packet Position your company on the first page of major search engines when persons are actively searching for products or services you offer! Keys to Success Relevance: Achieve higher
More informationPricing in Search Advertising Susan Athey, Harvard University and Microsoft
Pricing in Search Advertising Susan Athey, Harvard University and Microsoft Disclaimer: The views in this presentation are my own and do not represent any institution. Summary Pricing in internet search
More informationDeclare Your Independence Key Website Tactics to Increase Direct Online Bookings.
Declare Your Independence Key Website Tactics to Increase Direct Online Bookings www.icnd.net #1 Watch Your Competitors Myth #4: Last Click Attribution Never Assume You re Doing it Right Wal-Mart Founder:
More informationLecture 3: Incentive Compatibility, Revenue of the Vickrey auction, Sponsored Search
Lecture 3: Incentive Compatibility, Revenue of the Vickrey auction, Sponsored Search (Last lecture) Lemma: An auction satisfies incentive compatibility if and only if it is threshold based. We proved necessity
More informationProgress Report: Predicting Which Recommended Content Users Click Stanley Jacob, Lingjie Kong
Progress Report: Predicting Which Recommended Content Users Click Stanley Jacob, Lingjie Kong Machine learning models can be used to predict which recommended content users will click on a given website.
More informationCampaigns - 5 things you need to know. 27 Signs You Need A New Agency. What the AdWords Update Means for Your Paid Search Strategy
27 Signs You Need Google s Enhanced A New Agency Campaigns - 5 things you need to know What the AdWords Update Means for Your Paid Search Strategy Does Your Agency Know What They re Doing? Working with
More informationAre You Ready For The New Google Shopping?
Are You Ready For The New Google Shopping? cpcstrategy.com/blog www.cpcstrategy.com Overview Since its inception Google Shopping has been free to participate in. All an advertiser had to do get their products
More informationOnline Advertising. Ayça Turhan Hacettepe University Department Of Business Administration
Online Advertising Ayça Turhan Hacettepe University Department Of Business Administration Online Advertising In simple terms, it is advertising on the internet. It can be anywhere on internet and can consist
More informationCompetition and Fraud in Online Advertising Markets
Competition and Fraud in Online Advertising Markets Bob Mungamuru 1 and Stephen Weis 2 1 Stanford University, Stanford, CA, USA 94305 2 Google Inc., Mountain View, CA, USA 94043 Abstract. An economic model
More informationCOMP/MATH 553 Algorithmic Game Theory Lecture 8: Combinatorial Auctions & Spectrum Auctions. Sep 29, Yang Cai
COMP/MATH 553 Algorithmic Game Theory Lecture 8: Combinatorial Auctions & Spectrum Auctions Sep 29, 2014 Yang Cai An overview of today s class Vickrey-Clarke-Groves Mechanism Combinatorial Auctions Case
More informationHow to Increase Clicks Using Social Media Advertising. Ashley Ward CEO & Social Media Director Madhouse Marketing
How to Increase Clicks Using Social Media Advertising Ashley Ward CEO & Social Media Director Madhouse Marketing Ashley Who? Background: Social Media Social Media Advertising CEO IPA Enthusiast www.madhouse.marketing
More informationPay Per Click Advertising
Pay Per Click Advertising What is Pay Per Click Advertising? Pay Per Click (PPC) is a model of advertising where you pay each time your ad is clicked, rather than earning clicks organically (like SEO).
More information