A Rule-Based Agent-Oriented Framework for Weakly-Structured Scientific Workflows

Transcription

1 A Rule-Based Agent-Oriented Framework for Weakly-Structured Scientific Workflows Zhili Zhao Supervisors Prof. Dr. Adrian Paschke Freie Universität Berlin Prof. Dr. Hans Weigand Tilburg University AG Corporate Semantic Web Freie Universität Berlin

2 Contributions An expressive rule-based workflow language Combines messaging reaction rules and derivation rules Domain-specific decision logic expression Combines logic programs with description logic Adaptive workflow execution Distributed inference agents Integrating orchestration and choreography Different ways of exception handling Asynchronous user interaction A Web-based human agent Manual tasks, workflow steering and exception handling 2

3 Outline I. Proposed Solution II. Proof-of-Concept III. Evaluation 3

4 Outline I. Proposed Solution II. Proof-of-Concept III. Evaluation 4

5 Rule-based Agent-Oriented Framework: RbAF Rule-based workflow specification combining messaging reaction rules and derivation rules Distributed inference agents as the execution environment 5

6 Upper-Level Workflow Ontology specialize Upper-level Workflow Ontology Defines general concepts and their relations Supports for task allocation, execution and exception handling Can be further specialized into domainspecific ontologies Protein Prediction Analysis Workflow Ontology 6

7 Describing Workflows with Messaging Reaction Rules parallel block sequential block iterative block Event message Messaging Reaction Rules Explicit sending and receiving messages Associated with conversation identifiers Complex Event Processing (CEP)-based process logic description 7

8 Asynchronous User Interaction Human Agent manages the life cycle of human tasks Human users operate the tasks via a Web-based client 8

9 Process of Exception Handling Replacing exceptional resources dynamically (automatic) Human interaction (manual) 9

10 Domain-Specific Policy Expression Logical derivation rules: q (consequent) p (antecedent) More expressive than simple Boolean expressions Decision Logic with Derivation rules Adapted from [Kindler, 2006] Semantic Web Data Integration Integrating Semantic Web data: Querying simple vocabularies with SPARQL Incorporating ontologies as typed rules Reasoning ontologies 10

11 CTR-Based Workflow Representation/1 CTR (Concurrent Transaction Logic) [Bonner et al., 1996] A general logic for state-changing actions Supports both serial and concurrent execution Every transaction is a sequence of state changes (i.e., a state path) Database Operation Semantics d Data oracle O (D): specifies queries to a particular state D Transition oracle O t (D 1, D 2 ): specifies state transitions from D 1 to D 2 Complex Transactions Serial transactions f j; concurrent transactions f j; nondeterministic transactions f j; 11

12 CTR-Based Workflow Representation/2 Workflow: a CTR goal, which is either: An atomic transaction f or, A complex transaction f j, f j and f j Primitive task: a predicate p(arg 1,, arg n ) Composite Task: a CTR rule p f (f is a CTR goal) Optional task: a CTR rule p f state (state is constant) Workflow Task Semantics O d (D) specifies queries to a particular workflow state D; O t (D 1, D 2 ) specifies tasks t (I, O) that consume input data I and generate Example: output data O, D 2 = D 1 U {O} {I}. Workflow: processa processb processa taska 1 taska 2 send(ch 1, startb 2 ) taska 3 processb taskb 1 receive(ch 1, startb 2 ) taskb 3 12

13 Outline I. Proposed Solution II. Proof-of-Concept III. Evaluation 13

14 Proof-of-concept Implementation: RAWLS Prova [Prova Rule Language, 2010]: a Semantic Web rule engine Combines the benefits of declarative and object-oriented programming Provides access to external data Mule ESB (Enterprise Service Bus) [Mule ESB, 2012] : Supports the agent integration and communication Reaction RuleML [Paschke, 2014]: Represents workflow requests and results ESB-Based System Architecture 14

15 Web-Based User Client Workflow Inspection and Invocation Exception Management Human Task Management RDF Data Management 15

16 Outline I. Proposed Solution II. Proof-of-Concept III. Evaluation 16

17 Control-Flow-Based Evaluation Control-flow pattern examples: B A A B C C A Sequence Exclusive Choice C B Synchronization Multiple Instances without Synchronization Multi-Choice Deferred Choice Data: 35 control-flow patterns delivered by the Workflow Patterns Initiative (excludes Cancellation and Force Completion patterns) [Russell et al, 2006] Target Systems: Kepler, Taverna, Triana and RAWLS 17

18 Control-Flow-Based Evaluation Results A = Kepler, B = Taverna, C = Triana, D = RAWLS Basic Control Flow Patterns A B C D 1. Sequence Parallel Split Synchronization Exclusive Choice Simple Merge Advanced Branching and Synchronization Patterns A B C D 6. Multi-Choice Structured Synchronizing Merge Multi-Merge Structured Discriminator Blocking Discriminator Structured Partial Join Blocking Partial Join Generalized AND-Join Local Synchronizing Merge General Synchronizing Merge Thread Merge Thread Split Termination Patterns A B C D 18. Implicit Termination Explicit Termination State-Based Patterns Trigger Patterns A B C D 25. Transient Trigger Persistent Trigger Multiple Instance Patterns A B C D 30. Multiple Instances without Synchronization 31. Multiple Instances with a Priori Design-Time Knowledge 32. Multiple Instances with a Priori Run-Time Knowledge 33. Multiple Instances without a Priori Run-Time Knowledge 34. Static Partial Join for Multiple Instances 35. Dynamic Partial Join for Multiple Instances A B C D 20. Deferred Choice Interleaved Parallel Routing Milestone Critical Section Interleaved Routing Iteration Patterns A B C D 27. Arbitrary Cycles Structured Loop Recursion A B C D Results The RAWLS: Benefits from messaging reaction rules and the CEP Supports advanced synchronization, state-based, trigger and iteration patterns Interleaved routing-related patterns are not supported 1. + = supported, - = not supported; 2. The evaluation results of Kepler, Taverna and Triana are from [Migliorini et al., 2011] 18

19 Dataflow-Based Evaluation Dataflow pattern examples: Task to Task (Global data store) Task to Environment (Push Oriented) Task Precondition Data Existence Multiple Instance Data Data Transfer by Value Event-Based Task Trigger Data: 40 dataflow patterns delivered by the Workflow Patterns Initiative [Russell et al, 2004] Target Systems: Kepler, Taverna, Triana and RAWLS 19

20 Dataflow-Based Evaluation Results A = Kepler, B = Taverna, C = Triana, D = RAWLS Data Visibility A B C D 1. Task Data Block Data Scope Data Multiple Instance Data Case Data Folder Data Workflow Data Environment Data Internal Data Interaction A B C D 9. Task to Task Block Task to Sub-Workflow Decomposition Sub-Workflow Decomposition to Block Task to Multiple Instance Task from Multiple Instance Task Case to Case External Data Interaction A B C D 15. Task to Environment - Push-Oriented Environment to Task - Pull Oriented Environment to Task - Push-Oriented Task to Environment - Pull-Oriented Case to Environment - Push-Oriented Environment to Case - Pull-Oriented Environment to Case - Push-Oriented Case to Environment - Pull Oriented Workflow to Environment - Push-Oriented Environment to Workflow - Pull Oriented Environment to Workflow - Push-Oriented Workflow to Environment - Pull-Oriented Data Transfer Patterns A B C D 27. Data Transfer by Value Incoming Data Transfer by Value Outgoing Data Transfer - Copy In/Copy Out Data Transfer by Reference Unlocked Data Transfer by Reference - With Lock Data Transformation Input Data Transformation Output Data-Based Routing A B C D 34. Task Precondition - Data Existence Task Precondition - Data Value Task Postcondition - Data Existence Task Postcondition - Data Value Event - based Task Trigger Data - based Task Trigger Data - based Routing Kepler Taverna Triana RAWLS Results The RAWLS: Benefits from messaging reaction rules and their combination with derivation rules Supports almost all data interaction, databased routing and data transfer patterns Data visibility patterns are not well supported (The evaluation results of Kepler, Taverna and Triana are from [Migliorini et al., 2011]) 20

21 Distributed System Property-based Empirical Evaluation Distributed System Property Implementation Cycles in execution graph Arbitrary Cycles, Structured Loop and Recursion are supported (an infinite loop detector is also provided) Deterministic or non-deterministic Consistency Parallel and concurrent execution Non-deterministic execution which determines specific resources dynamically No consistency checking at design time; some basic inconsistencies are supported Supported by messaging reaction rules Distributed computation Distributed agents connected by ESB Synchronous or asynchronous communication Asynchronous communication to support long running tasks and human interaction 21

22 Summary and Outlook Summary Proposed a rule-based agent-oriented architecture An expressive rule-based language to describe the WsSWFs Flexible execution environment via distributed inference agents Outlook Improving usability by high level modeling workflow language Usability testing with case studies Recording workflow provenance information Important for workflow verification, optimization and reproduction 22

23 Comments or Questions? Thank you! AG Corporate Semantic Web Freie Universität Berlin

24 Selected References/1 Wooldridge, Michael, An Introduction to MultiAgent Systems. Wiley Publishing, 2nd ed., UniProt Core Vocabulary. Accessed: A. Paschke and A. Kozlenkov, Rule-Based Event Processing and Reaction Rules, in Rule Interchange and Applications (G. Governatori, J. Hall, and A. Paschke, eds.), vol of Lecture Notes in Computer Science, pp , Springer Berlin Heidelberg, A. Paschke, A Semantic Rule and Event Driven Approach for Agile Decision-Centric Business Process Management, in Towards a Service-Based Internet (W. Abramowicz, I. Llorente, M. Surridge, A. Zisman, and J. Vayssiére, eds.), vol of Lecture Notes in Computer Science, pp , Springer Berlin Heidelberg, A. Paschke and H. Boley, Rules Capturing Events and Reactivity, in Handbook of Research on Emerging Rule-Based Languages and Technologies (A. Giurca, D. Gasevic, and K. Taveter, eds.), pp , IGI Publishing, May in Computer Science, pp , Springer Berlin Heidelberg, Agent Communication Langage Specifications. Accessed: J. Mendling, Event-Driven Process Chains (EPC), in Metrics for Process Models, vol. 6 of Lecture Notes in Business Information Processing, pp , Springer Berlin Heidelberg, Workow Patterns. Accessed: A. Paschke and K. Teymourian, Rule-Based Business Process Execution with BPEL+, in Proceedings of I-KNOW and I-SEMANTICS 2009 (A. Paschke, H. Weigand, W. Behrendt, K. Tochtermann, and T. Pellegrini, eds.), (Graz, Austria), pp , Verlag der Technischen Universität Graz, September A. Paschke, Reaction RuleML 1.0 for Rules, Events and Actions in Semantic Complex Event Processing, in Proceedings of the 8th International Web Rule Symposium, LNCS, Springer, 2014 D. Luckham, W. R. Schulte, J. Adkins, P. Bizarro, H.-A. Jacobsen, A. Mavashev, B. M. Michelson, P. Niblett, and D. Tucker, Event Processing Glossary - Version 2.0, tech. rep., Event Processing Technical Society, 2011 A. Paschke, Rules and Logic Programming for the Web, in Reasoning Web. Semantic Technologies for the Web of Data (A. Polleres, C. d'amato, M. Arenas, S. Handschuh, P. Kroner, S. Ossowski, and P. Patel-Schneider, eds.), vol of Lecture Notes in Computer Science, pp , Springer Berlin Heidelberg, D. Hay, Defining Business Rules What Are They Really. Final Report, SPARQL-DL API. Accessed: Web Services Human Task (WS-HumanTask) Specification Version Accessed: A. J. Bonner and M. Kifer, Concurrency and Communication in Transaction Logic., in Proceedings of the Joint International Conference and Symposium on Logic Programming, pp ,

25 Selected References/2 E. Kindler, On the Semantics of EPCs: Resolving the Vicious Circle, Data & Knowledge Engineering, vol. 56, no. 1, pp , N. Russell, Arthur, W. M. P. van der Aalst, and N. Mulyar, Workflow Control-Flow Patterns: A Revised View, tech. rep., BPMcenter.org, N. Russell, A. H. M. ter Hofstede, D. Edmond, and W. M. P. van der Aalst, Workow Data Patterns: Identication, Representation and Tool Support, in Proceedings of the 24th International Conference on Conceptual Modeling, ER'05, (Berlin, Heidelberg), pp , Springer-Verlag, Prova Rule Language. Accessed: Mule ESB. Accessed: OpenRDF Sesame. Accessed: M. Alviano, F. Calimeri, Faber Wolfgang, G. Ianni, and N. Leone, Function Symbols in ASP: Overview and Perspectives, in Nonmonotonic reasoning. Essays celebrating its 30th anniversary. Papers from the conference (NonMon30), Lexington, KY, USA, 2010., pp. 1-24, London: College Publications, S. Migliorini, M. Gambini, M. La Rosa, and A. H. ter Hofstede, Pattern-Based Evaluation of Scientific Workflow Management Systems, tech. rep., University of Verona, A. Paschke, Reaction RuleML 1.0 for Rules, Events and Actions in Semantic Complex Event Processing, in Proceedings of the 8th International Web Rule Symposium, LNCS, Springer,