Data-Centric Workflow and Business Processes Victor Vianu and - PowerPoint PPT Presentation

Data-Centric Workflow and Business Processes Victor Vianu and Jianwen Su

Outline n Introduction Jianwen n Theoretical work on analysis of data-driven workflows Victor n Survey of issues in practical data-driven workflows Jianwen n Discussions of Further Research Challenges Jianwen & Victor Foundations of Data Management 20160413 2

Business Processes & Workflow Management n A BP is an assembly of (human) tasks to accomplish an objective v Eg: Obtaining a Permit Certi/icate Application Delivery Init review Review Fee Approval BP workflow Workflow Management (WfM) System n Each workflow model matches a BP model n Each workflow activity is a software program ( ) that interfaces one task in the BP n A WfM system manages executions, resources, documents, etc. n Will be sloppy: BP ≈ Process ≈ Work/low BPMS ≈ WfMS Foundations of Data Management 20160413 3

Rising Demand of WfMSs n Ubiquitous of BPs / workflows v old (traditional business apps) to new (e-science, healthcare, digital governmental apps) v all sizes (by # of tasks, # of enactments, …) v the number of workflow apps rapidly increasing n Digitization/IT causes pressure to scale, automate v E-documents, ability to get a lot of inputs: enhanced productivity à raised expectation of workflow v Rapidly changing environments: shortens idea-to-system time n Rapidly growing market: “ Suites ” alone $2.7B 2015 (Gartner) n Workflow management remains an art v design, development (implementation), making changes are mostly ad hoc and rely on human creativity Foundations of Data Management 20160413 4

5 Findings [2009 NSF Workshop on Data-Centric Workflows] http://dcw2009.cs.ucsb.edu/ n The need for workflow management is ubiquitous n Current workflow technologies provide inadequate support for a variety of essential functionalities n For transactional workflow, a key inhibitor is the lack of intuitively clear ways to combining the various aspects of workflow n There is a “ long tail ” phenomenon in applications that need and/or use workflow management technologies n Application areas of business, digital government, healthcare, and scientific workflow face many common/overlapping problems, but are developing paradigms, techniques and tools largely in isolation Foundations of Data Management 20160413 5

Key Application Challenges Research Challenges n Complexity n Unifying holistic conceptual model for Wf/BP management CHEVI v Detailed complexity, dynamic complexity n Design and runtime issues CEVI n Facilitate Human interactions n Reasoning CVI v Design stake holders, n Workflow analytics/discovery/ client/performer improvement CHEV n Extra-functional aspects n Provenance CE v Compliance, guarantees, n Process mining CHEV security/privacy, … n Interoperation CI n Variation, evolution, and long tail v E.g., legacy systems, … n Workflow Interoperation http://dcw2009.cs.ucsb.edu/ Foundations of Data Management 20160413 6

Five Classes of Biz Process Models n Data agnostic : data mostly not present v WF (Petri) nets, BPMN, UML activity diagrams, … n Data-aware : data (variables) present but missing storage and management v BPEL, YAWL, BPMN 2.0 (?), … n Storage-aware : persistent stores but mappings to/from biz process data managed ad hoc v jBPM, Activiti, JTang, … n Artifact-centric : entity (biz data) and lifecycle v GSM (Barcelona), (D)EZ-Flow, … n Universal artifact : add automation for modeling all five types, data-storage mapping v Universal Artifacts (UA) Foundations of Data Management 20160415 20160413 7

Outline n Introduction Jianwen n Theoretical work on analysis of data-driven workflows Victor n Survey of issues in practical data-driven workflows Jianwen n Discussions of Further Research Challenges Jianwen & Victor Foundations of Data Management 20160413 8

Outline n Introduction Jianwen n Theoretical work on analysis of data-driven workflows Victor n Survey of issues in practical data-driven workflows Jianwen v Independence of Data and Execution Management v Workflow Management v Workflow Interoperation n Discussions of Further Research Challenges Jianwen & Victor Foundations of Data Management 20160413 9

Enterprise System Architectures 1970’s n Evolve around information/database systems Application OS n Multiple applications with overlapping data Warehouse HR 1980’s Management Application WH data OS Application DBMS /iles POM DB OS DBMS Purchase OS Order Manag. DBMS HR DB OS [Weske 2012] Foundations of Data Management 20160413 10

Enterprise Resource Planning (ERP) n Composed of (extended) database systems and application specific software for applications 1990’s product marketing inventory application application ... planning & sales management system system since 2000’s standard Database DBMS ... ... System technology Enterprise Resource Supply Chain Customer Relation Planning Management Management n Typically too complex to integrate, or interoperate : very challenging Foundations of Data Management 20160413 11

Key Obstacle: Ad Hoc Workflow Management in ERP n Most enterprise applications include business processes/workflows n WfMSs: handcrafted, out-sourced, or standalone (jBPM, Activiti, …) product marketing inventory application application ... planning & sales management system system Workflow standard Database DBMS Management: System technology ad hoc, Enterprise handcrafted, Resource many context dependent Planning decisions n BPs are key assets/functions of enteprises Foundations of Data Management 20160413 12

Typical Workflow Management System Architecture WfMS Task wrapper Task wrapper Execution Local Enterprise Engine data store . . . database Task Includes all data required wrapper for control flow decisions, correlations, … [van der Aalst-van Hee 2004] n Used in YAWL, jBPM, Activiti, Barcelona, … , and possibly systems from major vendors n During execution, data can be held in each of the shaded shapes à creates many problems Foundations of Data Management 20160413 13

WfMS Example: Enterprise Database Fails Task wrapper Task wrapper Execution Enterprise Local Engine data store . . . database n DBMS does recovery, but data may not be Task wrapper consistent with data in the local store, engine, and wrappers Update ShippingAddress ShippingAddress: undefined Enterprise database n Other failures similar: local data store v Engine (workflow “ log ” ?) v Wrapper (more trouble if keeping persistent data, e.g., MVC) n Other difficulties: Process change, compliance, … [S.-Yang EVL-BP15] Foundations of Data Management 20160413 14

Independence of Data Management and Execution Management [Sun-S.-Yang BPM14 TMIS16] Execution Independence the freedom of changing the process execution system while leaving conceptual BP models unchanged and vice versa n Clean separation of responsibilities v WfMS: Execution v DBMS: Data n Allows Divide-and-Conquer for management functions v Helps in many aspects Foundations of Data Management 20160415 20160413 15

Intersections of Databases & Software Engineering n Could benefit a wide range of data-intensive or data-centric software applications Currently lacks: n Models, frameworks, and principles n Theoretical foundations n Tools and techniques Foundations of Data Management 20160413 16

Conceptualizing Running Workflows (or Other Software) Work/low instances Database . . . n Each workflow (BP) instance consists of a universal artifact and a lifecycle n Data mappings are ad hoc in current development practice n Primitive mapping support: ADO.NET Entity Framework [Melnik-Adya-Bernstein TODS08] n Foundations of Data Management 20160413 17

Example: Enterprise Database (& Lifecycle) tUser tRepair tServiceInfo tRepairPerson tMaterialInfo tReview + tLastName tRepairID tServiceID tServiceID_P tMaterialID tReviewID ? * tFirstName tCustomerLN tRerpairID_SI tRerpairpersonLN tServiceID_MI tServiceID_R * * tPhone tCustomerFN tTime tRepairpersonFN tMaterial tReviewResult * tAddress tReason . . . . . . . . . . . . . . . tDate . . . n Includes keys, foreign keys, and a cardinality specification on each foreign key Repair Application Repairperson Assignment On-site Repair w (ID) w (Service ID) w (Material ID) w (Customer Name) w (Repairperson Name) w (Material) r (Customer Address) w (Repairperson Phone) . . . . . . . . . Application Review Document Archive Post-repair Visit . . . . . . . . . Foundations of Data Management 20160413 18

Example: The (Universal) Artifact aRepair Tuple and aID aRepairInfo aCustomer (nested) set constructs unique aReason aDate aCust_Name aCust_Addr aCust_Last_Name aCust_First_Name aService_Info aService aTime aRepairPerson aMaterial_Info aReview_Info unique aMaterial Info aRP LastName aRP FirstName aRP Phone aMaterial aReviewID aResult unique in aMaterial Info unique Foundations of Data Management 20160413 19

Data Mapping Idea [Sun-S.-Wu-Yang ICDE14] RepairInfo Addr = Addr . Customer . RID RIID CustName @RepairInfo (RIID) . CustName R101 David @User ( UserName ) . Address R102 Peter @ User UserName Address @ Peter A2 Repair David A1 . . . RID Customer Services James A3 R101 ServiceInfo . . . CName Addr SID Date SIID Date RepairID { } . . . David A1 S01 11/29 S01 11/15 R101 . . . S03 12/17 S02 11/29 R102 S03 12/17 R101 Foundations of Data Management 20160415 20160413 20