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09-Aug-2015
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Transcript of Query personalization

1. 1. Query Personalization (Subscriber Friendly)
2. 2. Typical Pub/Sub All subscriptions are considered equally Just matching a publication whenever theres a satisfied subscription Top-k Pub/Sub Users can express some events are more important than others by ranking subscriptions A publication is scored against a satisfied subscription space Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&T Item = Smartphone Carrier = AT&T Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&T Item = Smartphone Carrier = AT&T
3. 3. How a publication is covered by a subscription? Lets assume, oSubscription (S) = {b1 b2 .. bq} oPublication (P) = {a1 a2 . ap} oP is covered by S, iff bi S, then aj P a1,a2,a3,.....,ap b1,b2,,bq Not covered a1,a2,a3,.....,ap b1,b2,b3 b1,b2,b3,b4 b1,b2,..bj Covered!
4. 4. Worst case scenario Bob subscribed to all matching subscriptions Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&T Item = Smartphone Carrier = AT&T OS = Android OS = Android OS = Android Carrier = AT&T Item = Smartphone OS = Android {} |P| = n = 3 |S| = 2 = 23 Item = Smartphone Carrier = AT&T OS = Android Subscription Space
5. 5. How a subscription is covered by a subscription? Can be represented using a preference graph Given two subscriptions and , covers , iff, for each publication p s.t. covers p, it holds that covers p Node Subscription Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&TOS = Android OS = Android Carrier = AT&T Item = Smartphone OS = Android Item = Smartphone Carrier = AT&T OS = Android
6. 6. How to assign preference over subscription? Quantitative approach Assign interest to each subscription Qualitative approach Specify the interest between two subscriptions Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&T 0.7 0.5 0.9 Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&T > <
7. 7. Interesting Question How can we compare quantitative & qualitative models, which are used by a specific user? For the moment, Lets go with quantitative approach
8. 8. Worst case scenario Bob subscribed to all matching subscriptions Item = Smartphone Carrier = AT&T OS = Android Item = Smartphone Item = Smartphone Carrier = AT&T Carrier = AT&TOS = Android OS = Android Carrier = AT&T Item = Smartphone OS = Android Item = Smartphone Carrier = AT&T OS = Android 0.7 0.5 0.9 0.8 0.6 0.9 0.8 Pref_score = Aggregation_op (score1,.score8); s.t. Aggregation_op {Max, Min, Average} Subscription Space
9. 9. Preference graph performance Can prune useless subscriptions when walking along the graph for a publication matching But in the worst case when nodes grow exponentially, It becomes bottleneck, when We have many users associated with each subscription The subscriptions are supported by many operators Attr {=,!=,>,