Querying Past and Future in Web Applications
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1 Querying Past and Future in Web Applications Daniel Deutch Tel-Aviv University Tova Milo Customer HR System Logistics ERP Bank ecomm CRM Supplier
2 Outline Introduction & Motivation Querying Future [VLDB 06, ICDE 09, ICDT 09, VLDB 09] Querying Past [VLDB 07, VLDB 08, ICDT 09, VLDB 09] Related and Future work
3 Outline Introduction & Motivation Querying Future [VLDB 06, ICDE 09, ICDT 09, VLDB 09] Querying Past [VLDB 07, VLDB 08, ICDT 09, VLDB 09] Related and Future work
4 Introduction and Motivation Querying Web applications Web-based Business Processes (BPs) are very popular Querying the past past executions of a given application for improving business logic, optimization, personalized ads, past design patterns of applications to exploit when building a new application Querying (possible) future executions the above + verification
5 Introduction and Motivation Example Web Application
6 Introduction and Motivation Application Specification BP specifications (e.g. in BPEL) are compiled to running code => Queries over specification structure Modeled as nested DAGs Each DAG corresponds to a function\web-page Nodes model activities (activation and completion) Edges mark flow relation Atomic/compound activities Nesting models implementation relations \ links Guarding formulas (on external events) model choices Recursion is allowed
7 Introduction and Motivation Example Specification choosetravel F1 $searchtype = flights only Advertise Login Confirm Flights $airline= BA $airline= AF $choice= confirm $choice= reset F1 $airline= AA $searchtype = flights + hotels Advertise Login Confirm $searchtype = flights+hotels+cars Flights Hotels $hotel= Plaza $hotel= Marriott F2 F3 F4
8 Introduction and Motivation Example Execution Flow 07:00 choosetravel $searchtype= flight only 07:02 07:04 Login Login 07:05 Advertise Flights $airline = BA 07:10 07:30 Advertise... Flights 07:40 07:41 07:42 Confirm Confirm $choice = confirm... 07:43 choosetravel
9 Introduction and Motivation Sources of Uncertainty Partial Tracing, due to lack of storage, confidentiality, etc. External Effects, e.g. user choices, server response time The effect of external events is modeled by logical formulas, guarding implementations At run-time, formulas truth values determines the chosen implementations Thus the past is uncertain, and the future unknown
10 Outline Introduction & Motivation Querying Future Querying Past Related and Future work
11 Querying Future Queries on possible EX-flows Can a user reserve a flight without giving her credit card details? What is the probability that this would happen? How can this happen / what are the most common scenarios? What is the common behavior of users that search for an Air France flight+hotel deal but quit without making a reservation? Focus on the search-related sub-flow. What is the best way to get a cheap flight+hotel deal, or one that maximizes the FF millage/points?
12 Querying Future Query types Boolean (+ probability) Selection ( + top-k ranking) Projection (+ top-k ranking)
13 Querying Future Query types Boolean (+ probability) easy (hard) Selection ( + top-k ranking) easy (easy) Projection (+ top-k ranking) easy (hard)
14 Querying Future Query language Execution patterns Intuitive, similar in structure to execution flows Seek for occurrences (homomorphism) of the pattern within execution flow DAGs May contain transitive nodes and edges May contain a projection part
15 Querying Future Example Query choosetravel Start Start Flights Flights $Airline = BA choosetravel choosetravel Hotels Any choosetravel Hotels Any choosetravel Confirm choosetravel Confirm
16 Querying Future Weighting flows A weighted model cweight - choice Weight (product cost, shipping time, likelihood) Aggr (sum, multiplication) fweight - flow Weight (total cost, shipping time, ) Varying sensitivity to flow history (cweight) Varying level of monotonicity (fweight)
17 Querying Future Our Example Specification choosetravel choosetravel $searchtype = flights only $searchtype = flights + hotels $searchtype = flights+hotels+cars F1 Login Login Advertise Advertise Confirm Confirm $choice= reset $airline= AL Flights $airline= BA Flights $airline= AF $choice= confirm F1 Advertise Advertise Login Login Confirm Confirm Flights Flights $hotel= Crown Hotels eplaza Hotels $hotel= Marriott F2 F3 F4
18 Querying Future Example (Weight = Likelihood) We distinct three classes of probability distributions, according to the level of dependency of cweight. History independence (markovian): No dependencies between formulas. Bounded-history: Dependency in(at most) B last formula values. Unbounded-history
19 Querying Future Sample of Results (top-k selection) Complexity of query evaluation depends on cweight PTIME in the spec., exponential in history bound and query. NP hard in both. Undecidable for unbounded history (Instance) Optimality of our algo depends on fweight Strongly monotone: Optimal Semi-strongly monotone: Instance optimal No optimal exists Weakly monotone: Not (instance) optimal No instance optimal exists
20 Querying Future Sample of Results (Boolean) Harder: Need to sum up probability of (a possibly infinite number of) qualifying flows Computing exact probability is impossible Approximation is possible Technique: representing probabilities via set of linear equations EXPTIME in general (NP Hard) PTIME for non-recursive apps
21 Querying Future Sample of Results (top-k projection) Source of difficulty: need to consider a possibly infinite # of answers, and sum up probabilities of their possibly infinite # of origins Technique: Small world theorem allows to consider only a bounded number of answers, then use (Boolean queries) Oracle EXPTIME even for non recursive apps (NP Hard)
22 Querying Future ShopIT Shopping assistant
23 Querying Future ShopIT Shopping assistant
24 Querying Future ShopIT Shopping assistant
25 Outline Introduction & Motivation Querying Future Querying Past Related and Future work
26 Querying Past Types of Partial Traces Partial Tracing, due to lack of storage, confidentiality, Naïve tracing records all activities accurately Semi-Naïve tracing contains only partial information on the names of some activities Selective tracing may omit some activities occurrences Tracing systems (called types) are represented by a renaming function and a deletion set
27 Querying Past Example BP Trip Luxury Trip Luxury Search Search Search Search Hotel Credit1 Flight Credit1 LuxHotel Credit2 LuxFlight Credit2 Hotel Credit1 Flight Credit1 LuxHotel Credit2 LuxFlight Credit2 Print Print Print Print
28 Querying Past Naïve Traces Trip Search Trip Luxury Search Search Search Hotel Credit1 Flight Credit1 LuxHotel Credit2 LuxFlight Credit2 Hotel Credit1 Flight Credit1 LuxHotel Credit2 LuxFlight Credit2 Print Print Trip Print Trip Luxury Print
29 Querying Past Semi-Naïve Traces Trip Search Trip Luxury Search Search Search Hotel Credit Flight Credit Hotel Credit Flight Credit Hotel Credit Flight Credit Hotel Credit Flight Credit Print Print Trip Print Trip Luxury Print
30 Querying Past Selective Traces Trip Trip Search Search Search Search Hotel Credit Flight Credit Hotel Credit Flight Credit Hotel Credit Flight Credit Hotel Credit Flight Credit Print Print Print Print Trip Trip
31 Querying Past Lets talk about (top-k) queries Given a partial trace, what is its most likely origin? Or, more generally, given a pattern (query) of partial traces, what are the most likely origins of partial traces of this pattern? Good news: Many of the query evaluation algorithms extend to this context (even without knowing the tracing system )
32 Outline Introduction & Motivation Querying Future Querying Past Related and Future work
33 Related & Future Work (Small subset of) Related work (Probabilistic) Recursive State Machines with temporal logic as query language Probabilistic Relational DBs Probabilistic XML Graph grammars with MSO (or FO) as query language BP and Web applications mining
34 Related & Future Work Future work Practical applications: Web-sites design On-line advertisements Improved business logic Enriched Query Language Joins Data values Optimization Inference of specs/probability distributions
35 ( = Thanks! )
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