1. David Chiu and Gagan Agrawal Department of Computer Science and Engineering The Ohio State University 1 Supporting Workflows through Data-driven Service Composition

2. Supporting Workflows through Data-driven Service Composition 2 Introduction Data is being generated as an astounding pace. Particularly in the geographical domain 2

3. Supporting Workflows through Data-driven Service Composition 3 Coping with Heterogeneity of Geographical Data Large-scale geographical systems –Harness a robust set of data (possibly from multiple data sources) –Computationally intensive tasks Service-oriented architectures (SOA) are currently employed for such systems –Integration of heterogeneous data –Interoperation of heterogeneous systems via Web services 3

4. Supporting Workflows through Data-driven Service Composition 4 Workflows via Service Composition These computationally intensive tasks are typically referred to as workflows or service-chains. Even the commonplace Address Mapper can be decomposed 4

5. Supporting Workflows through Data-driven Service Composition 5 Workflow Composition Problem

6. Supporting Workflows through Data-driven Service Composition 6 Two Flavors of Service Composition (1) Static - workflow schedules are preprogrammed Efficient Cannot handle new requests outside of its knowledge base Is not resource-aware: does not take advantage of newly introduced data and services Is not robust: does not employ discovery mechanisms if data or services are unavailable 6

7. Supporting Workflows through Data-driven Service Composition 7 Static Composition Scenario (1) Response to query A is preprogrammed as simply W A = { (S J, D P ) } 7

8. Supporting Workflows through Data-driven Service Composition 8 Static Composition Scenario (2) Service J becomes unavailable 8

9. Supporting Workflows through Data-driven Service Composition 9 Static Composition Scenario (3) Or, Data P becomes unavailable 9

10. Supporting Workflows through Data-driven Service Composition 10 Static Composition Scenario (4) Result: high level query cannot be answered 10

11. Supporting Workflows through Data-driven Service Composition 11 Two Flavors of Service Composition (2) Dynamic - workflow schedules are generated at runtime –New query requests can be learned by the system –Robust in the way that it will always try best-effort to answer queries –Slow, schedules are generated for each query 11

12. Supporting Workflows through Data-driven Service Composition 12 Dynamic Composition Scenario (1) A response to query A is constructed as W A = { (S J, D P ) } 12

13. Supporting Workflows through Data-driven Service Composition 13 Dynamic Composition Scenario (2) Service J becomes unavailable 13

14. Supporting Workflows through Data-driven Service Composition 14 Dynamic Composition Scenario (3) Reconstructed as W A = { (S H, D R ), (S v, ) } 14

15. Supporting Workflows through Data-driven Service Composition 15 Our Approach Data and services must be annotated with semantic data We describe an ontology between disparate datasets and services Based on ontological information and the availability of geo data, our system can compose workflows on-the-fly 15

16. Supporting Workflows through Data-driven Service Composition 16 System Architecture

17. Supporting Workflows through Data-driven Service Composition 17 Data-driven Service Composition Algorithm We first parse the user query into some “Geo Concept”, such as water level, coordinate, etc. Find the classes of data that can be used to derive the concept For each class, find services that can be used to manipulate data If service outputs as original concept, then we have a workflow, otherwise... recurse

18. Supporting Workflows through Data-driven Service Composition 18 Challenges Guaranteeing correctness of workflows Length of workflow must be tractable Adding cost metrics for fast-scheduling Scalability Generalizing the framework beyond geographical domain 18