verso una gestione dei processi aziendali basata sulla realtà
TRANSCRIPT
Verso una gestione dei processi aziendali
basata sulla realtà
Marco MontaliFacoltà di Scienze e Tecnologie Informatiche
Libera Università di Bolzano
Disclaimer
• La realtà verrà necessariamente “semplificata”, talvolta portata all’estremo • Le critiche/provocazioni andranno viste in “positivo”.
Non per criticare lo stato attuale, ma per indicare strade da seguire nel prossimo futuro!2
Le due realtàGestione dell’organizzazione
Realtà aziendale studiata, analizzata, pianificata attraverso l’uso di modelli e di dati ottenuti sull’andamento dell’organizzazione
Lavoro quotidiano nell’organizzazione
Realtà aziendale esperita direttamente
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Problema classicoGestione dell’organizzazione
Lavoro quotidiano nell’organizzazione
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Il nostro obiettivoGestione dell’organizzazione
Lavoro quotidiano nell’organizzazione
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IT
Il grande dilemma
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manager/ analista
responsabile IT
esperto di dominio
realtà
?? ? ??
Il grande dilemma
1. Come far interagire i diversi attori in modo da comprendere la realtà, costruire una visione comune, e condividere gli obiettivi strategici?
2. Come ripensare queste interazioni alla luce di nuovi dati ottenuti dalla realtà?
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Cosa vedremo…• Introduzione progressiva di modelli di gestione
orientati ai processi nella gestione delle organizzazioni
• Il punto di discontinuità: data science
• Il tentativo azzardato di eliminare i modelli
• La riconciliazione tra dati e modelli • Accesso ai dati tramite modelli concettuali • Process mining
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Modello
Rappresentazione di un oggetto o di un fenomeno, che corrisponde alla cosa modellata per il fatto di riprodurne alcune caratteristiche o comportamenti fondamentali
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Modello
Rappresentazione di un oggetto o di un fenomeno, che corrisponde alla cosa modellata per il fatto di riprodurne alcune caratteristiche o comportamenti fondamentali
• Quali caratteristiche/comportamenti sono importanti? • Come verificare che questi siano riprodotti?
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Modelli concettuali organizzativiIl “collante” fondamentale per colLavorare!
• La modellazione concettuale è “l’attività di descrivere formalmente alcuni aspetti del mondo fisico e sociale che ci circonda, al fine di comprendere e comunicare” (John Mylopoulos, 1992)
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Modelli concettuali organizzativiIl “collante” fondamentale per colLavorare!
• “Una volta rappresentato come artefatto concreto, un modello supporta la comunicazione, l’apprendimento e l’analisi degli aspetti rilevanti del dominio di interesse. […] la rappresentazione diagrammata di un modello creata da uno sconosciuto diventa un medium per preservare e comunicare una certa visione del mondo, e può fungere da perno per il ragionamento e il problem solving, nonché l’acquisizione di nuova conoscenza (magari producendo nuove, folgoranti idee!) su quella visione del mondo” (Giancarlo Guizzardi, 2005)
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Conceptual Modeling Languages: CriteriaClarity: how easy the language can be understood and used (by di�erentstakeholders).
• Graphical vs textual notations.• The language must be unambiguous: formal
foundation.• The more expressive the language, the more
di�cult is to retain clarity.• Less expressive languages require complex
combinations of their few constructs.• Abstraction: remove unnecessary details. Use requirements to drive
abstraction.• Simplicity: Prefer simple schemas. Follow Occam’s razor with a
critical approach.• Orthogonality: minimization of the overlapping of language
constructs. Their (in)dependence must reflect the one of thecorresponding domain aspects.
Marco Montali (unibz) DPM - 2.IS Development A.Y. 2014/2015 13 / 21
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Un po’ di storia
La fabbrica degli spilli (1751-1766)
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La fabbrica degli spilli (1751-1766)
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F.W. Taylor (~1911)• Punto di partenza: le organizzazioni non sono organizzate secondo
principi solidi
• Impossibile misurarne l’efficienza e la qualità degli output
• Idea: applicare il metodo scientifico alla gestione delle organizzazioni • Decomposizione del lavoro in unità elementari • Analisi scientifica di ogni unità fissando confini e regole d’azione ben
definite • Acquisizione, training e controllo del personale secondo tali regole • Divisione di competenze: il management controlla, il personale agisce
• Risultato: divisione del lavoro (catena di montaggio), analisi dettagliata delle singole attività ma non del loro coordinamento
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Attività nella catena del valore
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Business Functions and Refinement into Activities
Hierarchy of businessfunctions follows theaggregation abstraction.Leaf business functionsare the activities.
Marco Montali (unibz) DPM - 7.BPM A.Y. 2014/2015 22 / 41
Hammer&Champy (1990s)• Punto di partenza: limiti della divisione del lavoro in attività isolate
• Visione end-to-end assente nel business, troppo burocratico e frammentato
• Idea: fondare il business sui processi e la loro re-ingegnerizzazione
• “Non è più necessario né desiderabile per le aziende organizzare il proprio lavoro attorno ai principi di divisione del lavoro di Adam Smith. Il lavoro orientato alle attività è obsoleto nel mondo odierno, competitivo e dinamico. Al contrario, le aziende devono organizzare il lavoro attorno al concetto di processo” (Hammer&Champy)
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Coordinamento di attività
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Functional Decomposition and Business Processes
To understand how the
enterprise works, and tomodel how it is expected
to work, dynamicconstraints over theexecution of activities aremade explicit in terms ofBPs: control-flow.
Marco Montali (unibz) DPM - 7.BPM A.Y. 2014/2015 26 / 41
Processo di business• Un insieme di attività eseguite al fine di raggiungere un
obiettivo aziendale per un cliente o mercato specifico (Davenport, 1992)
• Una collezione di attività che prendono uno o più input per trasformarli in un risultato che è di valore per il cliente (Hammer&Champy, 1993)
• Un insieme di attività eseguite in modo coordinato all’interno di un dominio organizzativo e tecnico. Tali attività realizzano, congiuntamente, un obiettivo strategico. Ogni processo è eseguito all’interno di un’organizzazione, ma può interagire con processi di altre organizzazioni (Weske, 2011)
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Business Process Management
Un insieme di concetti, metodologie e tecniche per supportare la modellazione, amministrazione, configurazione, esecuzione, e analisi dei processi di business
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Ciclo di vita dei processi
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Business Process Lifecycle
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8 1 Introduction
Fig. 1.3 The BPM life-cycle showing the different uses of process models
As Fig. 1.3 shows, process models play a dominant role in the (re)design andconfiguration/implementation phases, whereas data plays a dominant role in theenactment/monitoring and diagnosis/requirements phases. The figure also lists thedifferent ways in which process models are used (as identified in Sect. 1.2). Untilrecently, there were few connections between the data produced while executingthe process and the actual process design. In fact, in most organizations the diag-nosis/requirements phase is not supported in a systematic and continuous manner.Only severe problems or major external changes will trigger another iteration of thelife-cycle, and factual information about the current process is not actively used inredesign decisions. Process mining offers the possibility to truly “close” the BPMlife-cycle. Data recorded by information systems can be used to provide a betterview on the actual processes, i.e., deviations can be analyzed and the quality ofmodels can be improved.
Process mining is a relative young research discipline that sits between machinelearning and data mining on the one hand and process modeling and analysis onthe other hand. The idea of process mining is to discover, monitor and improve realprocesses (i.e., not assumed processes) by extracting knowledge from event logsreadily available in today’s systems.
Figure 1.4 shows that process mining establishes links between the actual pro-cesses and their data on the one hand and process models on the other hand.As explained in Sect. 1.1, the digital universe and the physical universe becomemore and more aligned. Today’s information systems log enormous amounts ofevents. Classical WFM systems (e.g., Staffware and COSA), BPM systems (e.g.,BPM|one by Pallas Athena, SmartBPM by Pegasystems, FileNet, Global 360, andTeamwork by Lombardi Software), ERP systems (e.g., SAP Business Suite, Ora-cle E-Business Suite, and Microsoft Dynamics NAV), PDM systems (e.g., Wind-chill), CRM systems (e.g., Microsoft Dynamics CRM and SalesForce), middleware(e.g., IBM’s WebSphere and Cordys Business Operations Platform), and hospitalinformation systems (e.g., Chipsoft and Siemens Soarian) provide detailed infor-mation about the activities that have been executed. Figure 1.4 refers to such dataas event logs. All of the PAISs just mentioned directly provide such event logs.
©W.M.P. van der Aalst. Process Mining. Springer, 2011.
L’approccio classico
26
8 1 Introduction
Fig. 1.3 The BPM life-cycle showing the different uses of process models
As Fig. 1.3 shows, process models play a dominant role in the (re)design andconfiguration/implementation phases, whereas data plays a dominant role in theenactment/monitoring and diagnosis/requirements phases. The figure also lists thedifferent ways in which process models are used (as identified in Sect. 1.2). Untilrecently, there were few connections between the data produced while executingthe process and the actual process design. In fact, in most organizations the diag-nosis/requirements phase is not supported in a systematic and continuous manner.Only severe problems or major external changes will trigger another iteration of thelife-cycle, and factual information about the current process is not actively used inredesign decisions. Process mining offers the possibility to truly “close” the BPMlife-cycle. Data recorded by information systems can be used to provide a betterview on the actual processes, i.e., deviations can be analyzed and the quality ofmodels can be improved.
Process mining is a relative young research discipline that sits between machinelearning and data mining on the one hand and process modeling and analysis onthe other hand. The idea of process mining is to discover, monitor and improve realprocesses (i.e., not assumed processes) by extracting knowledge from event logsreadily available in today’s systems.
Figure 1.4 shows that process mining establishes links between the actual pro-cesses and their data on the one hand and process models on the other hand.As explained in Sect. 1.1, the digital universe and the physical universe becomemore and more aligned. Today’s information systems log enormous amounts ofevents. Classical WFM systems (e.g., Staffware and COSA), BPM systems (e.g.,BPM|one by Pallas Athena, SmartBPM by Pegasystems, FileNet, Global 360, andTeamwork by Lombardi Software), ERP systems (e.g., SAP Business Suite, Ora-cle E-Business Suite, and Microsoft Dynamics NAV), PDM systems (e.g., Wind-chill), CRM systems (e.g., Microsoft Dynamics CRM and SalesForce), middleware(e.g., IBM’s WebSphere and Cordys Business Operations Platform), and hospitalinformation systems (e.g., Chipsoft and Siemens Soarian) provide detailed infor-mation about the activities that have been executed. Figure 1.4 refers to such dataas event logs. All of the PAISs just mentioned directly provide such event logs.
Quali modelli?
Modelli strutturali• Rappresentano la struttura del dominio di interesse • Indicano le entità e le relazioni rilevanti per l’attività dell’organizzazione • Esempi “generici”: i modelli sottostanti i sistemi CRM/ERP
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Quali modelli?
Modelli dinamici (di processo)• Visione end-to-end del business • Attività e meccanismi di coordinamento nel tempo• Es.: order-to-cash, procure-to-pay, issue-to-resolution, …
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Input Output
I modelli di processo sono adeguati?
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BPM!30
BPM?31
Il tema della flessibilità
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Creazione di modelliTipica metodologia
• Modellazione • Interazione continua con esperti di dominio e clienti • Modularizzazione + raffinamento iterativo • Processo ripetuto per il sistema “as-is” e il sistema “to-be” • Importante di per sé
• Creazione di un “contratto basato sui modelli” • Uso dei modelli in fase di audit
• Progetto • modelli consegnati all’IT
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Tipica metodologia
• Modellazione • Interazione continua con esperti di dominio e clienti • Modularizzazione + raffinamento iterativo • Processo ripetuto per il sistema “as-is” e il sistema “to-be” • Importante di per sé
• Creazione di un “contratto basato sui modelli” • Uso dei modelli in fase di audit
• Progetto • modelli consegnati all’IT
Creazione di modelli
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I modelli sono corretti? Verifica Riflettono la realtà? Validazione
Gli artefatti computazionale corrispondono ai modelli? Allineamento concettuale
35
Ma ad oggi?• Processi solo parzialmente automatizzati
• I sistemi lasciano quasi sempre la possibilità di trovare una scappatoia
• Se non lo fanno, il rischio è che le persone agiscano fuori dal sistema per poi utilizzarlo a posteriori
• Incremento processi knowledge-intensive, inter-organizzativi, distribuiti —> solo parzialmente controllabili!
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Processo clinico modellato
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Processo clinico reale
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ith
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3to
L’impasse
• Esperti di dominio: visione locale
• Management: visione globale ma spesso non allineata alla realtà
• Come facciamo a riportare la realtà in azienda?
39
Economia digitale
Validare i modelli rispetto alla realtà richiede innanzitutto di ottenere una “impronta digitale” della realtà
40
Dati nei sistemi controllati
• Tracciatura di tutti gli eventi importanti fondamentale per
• Gestione interna
• Calcolo di indicatori/metriche (KPI)
• Legge (compliance e audit esterni)
41
Dati “fuori controllo”
Tracciati da qualche parte! La nostra è una società digitale!
42
43
Il nuovo trend44
8 1 Introduction
Fig. 1.3 The BPM life-cycle showing the different uses of process models
As Fig. 1.3 shows, process models play a dominant role in the (re)design andconfiguration/implementation phases, whereas data plays a dominant role in theenactment/monitoring and diagnosis/requirements phases. The figure also lists thedifferent ways in which process models are used (as identified in Sect. 1.2). Untilrecently, there were few connections between the data produced while executingthe process and the actual process design. In fact, in most organizations the diag-nosis/requirements phase is not supported in a systematic and continuous manner.Only severe problems or major external changes will trigger another iteration of thelife-cycle, and factual information about the current process is not actively used inredesign decisions. Process mining offers the possibility to truly “close” the BPMlife-cycle. Data recorded by information systems can be used to provide a betterview on the actual processes, i.e., deviations can be analyzed and the quality ofmodels can be improved.
Process mining is a relative young research discipline that sits between machinelearning and data mining on the one hand and process modeling and analysis onthe other hand. The idea of process mining is to discover, monitor and improve realprocesses (i.e., not assumed processes) by extracting knowledge from event logsreadily available in today’s systems.
Figure 1.4 shows that process mining establishes links between the actual pro-cesses and their data on the one hand and process models on the other hand.As explained in Sect. 1.1, the digital universe and the physical universe becomemore and more aligned. Today’s information systems log enormous amounts ofevents. Classical WFM systems (e.g., Staffware and COSA), BPM systems (e.g.,BPM|one by Pallas Athena, SmartBPM by Pegasystems, FileNet, Global 360, andTeamwork by Lombardi Software), ERP systems (e.g., SAP Business Suite, Ora-cle E-Business Suite, and Microsoft Dynamics NAV), PDM systems (e.g., Wind-chill), CRM systems (e.g., Microsoft Dynamics CRM and SalesForce), middleware(e.g., IBM’s WebSphere and Cordys Business Operations Platform), and hospitalinformation systems (e.g., Chipsoft and Siemens Soarian) provide detailed infor-mation about the activities that have been executed. Figure 1.4 refers to such dataas event logs. All of the PAISs just mentioned directly provide such event logs.
Data Science
• Nuova area all’intersezione tra informatica, management, statistica, apprendimento automatico
• Richiede nuovi esperti in big data management&analytics, data mining, machine learning, data visualization, aspetti legali ed etici legati alla gestione dei dati, …
45
46
http://www.delphianalytics.net/more-data-than-analysts-the-real-big-data-problem/
Alcuni esempi
47
I modelli servono?
48
Mars Orbiter• Dispositivo per lo studio del
clima marziano • Costo missione:
327.600.000 € • Perso al momento di
entrare in orbita su Marte
49
Why Data is not Enough
• Developed to study the martian climateand atmosphere.
• Mission cost: $ 327.6M.• During the orbital insertion maneuver, it
went out of radio contact permanently.• Why? Metric Mixup.
I Software on orbiter: Newtons;Software on earth: Pound-force.Conversion factor: ≥ 4.5.
I Same data, di�erent interpretations.I Lack of testing (and budget). Danger of
re-use.
Marco Montali (unibz) DPM - 1.Intro A.Y. 2014/2015 18 / 43
Causalità vs Casualità
50
Causalità vs Casualità
51
52
Verso il completamento del ciclo
53
8 1 Introduction
Fig. 1.3 The BPM life-cycle showing the different uses of process models
As Fig. 1.3 shows, process models play a dominant role in the (re)design andconfiguration/implementation phases, whereas data plays a dominant role in theenactment/monitoring and diagnosis/requirements phases. The figure also lists thedifferent ways in which process models are used (as identified in Sect. 1.2). Untilrecently, there were few connections between the data produced while executingthe process and the actual process design. In fact, in most organizations the diag-nosis/requirements phase is not supported in a systematic and continuous manner.Only severe problems or major external changes will trigger another iteration of thelife-cycle, and factual information about the current process is not actively used inredesign decisions. Process mining offers the possibility to truly “close” the BPMlife-cycle. Data recorded by information systems can be used to provide a betterview on the actual processes, i.e., deviations can be analyzed and the quality ofmodels can be improved.
Process mining is a relative young research discipline that sits between machinelearning and data mining on the one hand and process modeling and analysis onthe other hand. The idea of process mining is to discover, monitor and improve realprocesses (i.e., not assumed processes) by extracting knowledge from event logsreadily available in today’s systems.
Figure 1.4 shows that process mining establishes links between the actual pro-cesses and their data on the one hand and process models on the other hand.As explained in Sect. 1.1, the digital universe and the physical universe becomemore and more aligned. Today’s information systems log enormous amounts ofevents. Classical WFM systems (e.g., Staffware and COSA), BPM systems (e.g.,BPM|one by Pallas Athena, SmartBPM by Pegasystems, FileNet, Global 360, andTeamwork by Lombardi Software), ERP systems (e.g., SAP Business Suite, Ora-cle E-Business Suite, and Microsoft Dynamics NAV), PDM systems (e.g., Wind-chill), CRM systems (e.g., Microsoft Dynamics CRM and SalesForce), middleware(e.g., IBM’s WebSphere and Cordys Business Operations Platform), and hospitalinformation systems (e.g., Chipsoft and Siemens Soarian) provide detailed infor-mation about the activities that have been executed. Figure 1.4 refers to such dataas event logs. All of the PAISs just mentioned directly provide such event logs.
E i dati?
54
OBDI framework Query answering Ontology languages Mappings Identity Conclusions
Example from the oil domain
Ontology: WellborewbName: StringaltName: String
Licencenumber: String
hasLicence I
Data sources:D1, D2, D3 contain information about wellbores.
D4 contains information about licences.D1 D2 D3 D4
id1 name
a1 ’A’
a2 ’B’
a3 ’H’
id2 name Well
b1 null 1
b2 ’C’ 2
b6 ’B’ 3
id3 aName
c3 ’U1’
c4 ’U2’
c5 ’U6’
id4 lNum
9 ’Z1’
8 ’Z2’
7 ’Z3’
Mappings:Wellbore and wbName are defined using D1 and D2.
altName is defined using D3.
hasLicense is defined using D4.
Moreover, URIs for wellbores from source Dk are generated as wbk(id).Diego Calvanese (FUB) Ontologies for Data Integration FOfAI 2015, Buenos Aires – 27/7/2015 (41/52)
Albori dei sistemi informativi
55
OS
Application
1970
OS
GUI
DBMS
Application
OS
DBMS
Application
Database Database
t1980 1990
fromM.Weske:BusinessProcessManagement,©Springer-VerlagBerlinHeidelberg2007
Sistemi verticali e integrazione
56
OS
DBMS
Human Resources Application
HR DB
OS
DBMS
Purchase Order Management
POM DB
OS
Warehouse Management
Warehouse file system data store
Redundancy of data
OS
Application
1970
OS
GUI
DBMS
Application
OS
DBMS
Application
Database Database
t1980 1990
fromM.Weske:BusinessProcessManagement,©Springer-VerlagBerlinHeidelberg2007
Sistemi ERP
57
OS
DBMS
Client nClient 2Client 1
ERP Server Application
ERP Database
. . .
OS
Application
1970
OS
GUI
DBMS
Application
OS
DBMS
Application
Database Database
t1980 1990
fromM.Weske:BusinessProcessManagement,©Springer-VerlagBerlinHeidelberg2007
Evoluzione a silos• Necessità di estendere ERP coprendo altre funzionalità
fondamentali (CRM, supply chain)
58
OS
DBMS
Application Logic of CRM System
GUI
OS
DBMS
OS
DBMS
Application Logic of SCM System
Application Logic of ERP System
GUI GUI
SCM Database
ERP Database
CRM Database
OS
Application
1970
OS
GUI
DBMS
Application
OS
DBMS
Application
Database Database
t1980 1990
fromM.Weske:BusinessProcessManagement,©Springer-VerlagBerlinHeidelberg2007
Integrazione punto a punto
• Problema di flessibilità • Frammentazione concettuale
59
ERP System
CRM SystemSCM System
Inventory Management
Data WarehouseHuman Resources
Application
OS
Application
1970
OS
GUI
DBMS
Application
OS
DBMS
Application
Database Database
t1980 1990
fromM.Weske:BusinessProcessManagement,©Springer-VerlagBerlinHeidelberg2007
Da qui…
Collegamento con i dati
60
OBDI framework Query answering Ontology languages Mappings Identity Conclusions
Capturing UML class diagrams/ER schemas in DL-Lite
Researchername: Stringsalary: Integer
Manager
PrincInv Coordinator
ProjectprojectName: String
1..?
supvsdByH
1..1
1..?
worksForH
1..?
1..1
managesN
1..1{disjoint}
Manager v ResearcherPrincInv v Manager
Coordinator v ManagerPrincInv v ¬Coordinator
Researcher v 9salary9salary� v xsd:int
(funct salary)
9worksFor v Researcher9worksFor� v ProjectResearcher v 9worksFor
Project v 9worksFor�
9manages v Coordinator9manages� v ProjectCoordinator v 9manages
Project v 9manages�
manages v worksFor(funct manages)(funct manages�)
· · ·DL-Lite cannot capture covering constraints.To do so, would require disjunction.
Diego Calvanese (FUB) Ontologies for Data Integration FOfAI 2015, Buenos Aires – 27/7/2015 (25/52)
…a qui!
Collegamento con i dati
61
Risultato
Crompton (2008): gli esperti di dominio spendono (troppo) tempo nel cercare i dati utili per prendere decisioni ed eseguire le proprie attività
• Ingegneri del settore petrolifero/gas: 30-70% del proprio lavoro in ricerca e analisi qualità dei dati
62
Optique• Progetto EU Optique: Scalable, End-User Access to
Big Data) • http://optique-project.eu • Obiettivo: sviluppo di tecniche per l’accesso
“concettuale” a grossi moli di dati • Casi di studio: Statoil (trivellazioni), Siemens (turbine)
• In Statoil, esperti geologi e geofisici sviluppano modelli stratigrafici di aree inesplorate, sulla base dei dati acquisiti su operazioni di trivellazione fatte in aree vicine
63
Fatti su Statoil• 1000 TB di dati memorizzati in DB relazionali (standard
SQL)
• Schemi non allineati
• Più di 2000 tabelle, sparpagliate in vari DBMS
• 900 esperti nella divisione “Statoil Exploration”
• 30-70% del tempo speso in ricerca dei dati
• Fino a 4 gg necessari per formulare le interrogazioni richieste e farle tradurre in SQL dagli esperti IT
64
Esempio di interrogazione
65
OBDI framework Query answering Ontology languages Mappings Identity Conclusions
How much time/money is spent searching for data?
A user query at Statoil
Show all norwegian wellbores with some aditional attributes(wellbore id, completion date, oldest penetrated age,result). Limitto all wellbores with a core and show attributes like (wellbore id,core number, top core depth, base core depth, intersectingstratigraphy). Limit to all wellbores with core in Brentgruppen andshow key atributes in a table. After connecting to EPDS (slegge)we could for instance limit futher to cores in Brent with measuredpermeability and where it is larger than a given value, for instance 1mD. We could also find out whether there are cores in Brent whichare not stored in EPDS (based on NPD info) and where there couldbe permeability values. Some of the missing data we possibly own,other not.
At Statoil, it takes up to 4 days to formulate a query in SQL.
Statoil loses up to 50.000.000e per year because of this!!
Diego Calvanese (FUB) Ontologies for Data Integration FOfAI 2015, Buenos Aires – 27/7/2015 (5/52)
66
OBDI framework Query answering Ontology languages Mappings Identity Conclusions
How much time/money is spent searching for data?
A user query at Statoil
Show all norwegian wellbores with some aditional attributes(wellbore id, completion date, oldest penetrated age,result). Limitto all wellbores with a core and show attributes like (wellbore id,core number, top core depth, base core depth, intersectingstratigraphy). Limit to all wellbores with core in Brentgruppen andshow key atributes in a table. After connecting to EPDS (slegge)we could for instance limit futher to cores in Brent with measuredpermeability and where it is larger than a given value, for instance 1mD. We could also find out whether there are cores in Brent whichare not stored in EPDS (based on NPD info) and where there couldbe permeability values. Some of the missing data we possibly own,other not.
SELECT [...]FROMdb_name.table1 table1,db_name.table2 table2a,db_name.table2 table2b,db_name.table3 table3a,db_name.table3 table3b,db_name.table3 table3c,db_name.table3 table3d,db_name.table4 table4a,db_name.table4 table4b,db_name.table4 table4c,db_name.table4 table4d,db_name.table4 table4e,db_name.table4 table4f,db_name.table5 table5a,db_name.table5 table5b,db_name.table6 table6a,db_name.table6 table6b,db_name.table7 table7a,db_name.table7 table7b,db_name.table8 table8,db_name.table9 table9,db_name.table10 table10a,db_name.table10 table10b,db_name.table10 table10c,db_name.table11 table11,db_name.table12 table12,db_name.table13 table13,db_name.table14 table14,db_name.table15 table15,db_name.table16 table16WHERE [...]
table2a.attr1=‘keyword’ ANDtable3a.attr2=table10c.attr1 ANDtable3a.attr6=table6a.attr3 ANDtable3a.attr9=‘keyword’ ANDtable4a.attr10 IN (‘keyword’) ANDtable4a.attr1 IN (‘keyword’) ANDtable5a.kinds=table4a.attr13 ANDtable5b.kinds=table4c.attr74 ANDtable5b.name=‘keyword’ AND(table6a.attr19=table10c.attr17 OR(table6a.attr2 IS NULL ANDtable10c.attr4 IS NULL)) ANDtable6a.attr14=table5b.attr14 ANDtable6a.attr2=‘keyword’ AND(table6b.attr14=table10c.attr8 OR(table6b.attr4 IS NULL ANDtable10c.attr7 IS NULL)) ANDtable6b.attr19=table5a.attr55 ANDtable6b.attr2=‘keyword’ ANDtable7a.attr19=table2b.attr19 ANDtable7a.attr17=table15.attr19 ANDtable4b.attr11=‘keyword’ ANDtable8.attr19=table7a.attr80 ANDtable8.attr19=table13.attr20 ANDtable8.attr4=‘keyword’ ANDtable9.attr10=table16.attr11 ANDtable3b.attr19=table10c.attr18 ANDtable3b.attr22=table12.attr63 ANDtable3b.attr66=‘keyword’ ANDtable10a.attr54=table7a.attr8 ANDtable10a.attr70=table10c.attr10 ANDtable10a.attr16=table4d.attr11 ANDtable4c.attr99=‘keyword’ ANDtable4c.attr1=‘keyword’ AND
table11.attr10=table5a.attr10 ANDtable11.attr40=‘keyword’ ANDtable11.attr50=‘keyword’ ANDtable2b.attr1=table1.attr8 ANDtable2b.attr9 IN (‘keyword’) ANDtable2b.attr2 LIKE ‘keyword’% ANDtable12.attr9 IN (‘keyword’) ANDtable7b.attr1=table2a.attr10 ANDtable3c.attr13=table10c.attr1 ANDtable3c.attr10=table6b.attr20 ANDtable3c.attr13=‘keyword’ ANDtable10b.attr16=table10a.attr7 ANDtable10b.attr11=table7b.attr8 ANDtable10b.attr13=table4b.attr89 ANDtable13.attr1=table2b.attr10 ANDtable13.attr20=’‘keyword’’ ANDtable13.attr15=‘keyword’ ANDtable3d.attr49=table12.attr18 ANDtable3d.attr18=table10c.attr11 ANDtable3d.attr14=‘keyword’ ANDtable4d.attr17 IN (‘keyword’) ANDtable4d.attr19 IN (‘keyword’) ANDtable16.attr28=table11.attr56 ANDtable16.attr16=table10b.attr78 ANDtable16.attr5=table14.attr56 ANDtable4e.attr34 IN (‘keyword’) ANDtable4e.attr48 IN (‘keyword’) ANDtable4f.attr89=table5b.attr7 ANDtable4f.attr45 IN (‘keyword’) ANDtable4f.attr1=‘keyword’ ANDtable10c.attr2=table4e.attr19 AND(table10c.attr78=table12.attr56 OR(table10c.attr55 IS NULL ANDtable12.attr17 IS NULL))
At Statoil, it takes up to 4 days to formulate a query in SQL.
Statoil loses up to 50.000.000e per year because of this!!
Diego Calvanese (FUB) Ontologies for Data Integration FOfAI 2015, Buenos Aires – 27/7/2015 (5/52)
67
OBDI framework Query answering Ontology languages Mappings Identity Conclusions
How much time/money is spent searching for data?
A user query at Statoil
Show all norwegian wellbores with some aditional attributes(wellbore id, completion date, oldest penetrated age,result). Limitto all wellbores with a core and show attributes like (wellbore id,core number, top core depth, base core depth, intersectingstratigraphy). Limit to all wellbores with core in Brentgruppen andshow key atributes in a table. After connecting to EPDS (slegge)we could for instance limit futher to cores in Brent with measuredpermeability and where it is larger than a given value, for instance 1mD. We could also find out whether there are cores in Brent whichare not stored in EPDS (based on NPD info) and where there couldbe permeability values. Some of the missing data we possibly own,other not.
SELECT [...]FROMdb_name.table1 table1,db_name.table2 table2a,db_name.table2 table2b,db_name.table3 table3a,db_name.table3 table3b,db_name.table3 table3c,db_name.table3 table3d,db_name.table4 table4a,db_name.table4 table4b,db_name.table4 table4c,db_name.table4 table4d,db_name.table4 table4e,db_name.table4 table4f,db_name.table5 table5a,db_name.table5 table5b,db_name.table6 table6a,db_name.table6 table6b,db_name.table7 table7a,db_name.table7 table7b,db_name.table8 table8,db_name.table9 table9,db_name.table10 table10a,db_name.table10 table10b,db_name.table10 table10c,db_name.table11 table11,db_name.table12 table12,db_name.table13 table13,db_name.table14 table14,db_name.table15 table15,db_name.table16 table16WHERE [...]
table2a.attr1=‘keyword’ ANDtable3a.attr2=table10c.attr1 ANDtable3a.attr6=table6a.attr3 ANDtable3a.attr9=‘keyword’ ANDtable4a.attr10 IN (‘keyword’) ANDtable4a.attr1 IN (‘keyword’) ANDtable5a.kinds=table4a.attr13 ANDtable5b.kinds=table4c.attr74 ANDtable5b.name=‘keyword’ AND(table6a.attr19=table10c.attr17 OR(table6a.attr2 IS NULL ANDtable10c.attr4 IS NULL)) ANDtable6a.attr14=table5b.attr14 ANDtable6a.attr2=‘keyword’ AND(table6b.attr14=table10c.attr8 OR(table6b.attr4 IS NULL ANDtable10c.attr7 IS NULL)) ANDtable6b.attr19=table5a.attr55 ANDtable6b.attr2=‘keyword’ ANDtable7a.attr19=table2b.attr19 ANDtable7a.attr17=table15.attr19 ANDtable4b.attr11=‘keyword’ ANDtable8.attr19=table7a.attr80 ANDtable8.attr19=table13.attr20 ANDtable8.attr4=‘keyword’ ANDtable9.attr10=table16.attr11 ANDtable3b.attr19=table10c.attr18 ANDtable3b.attr22=table12.attr63 ANDtable3b.attr66=‘keyword’ ANDtable10a.attr54=table7a.attr8 ANDtable10a.attr70=table10c.attr10 ANDtable10a.attr16=table4d.attr11 ANDtable4c.attr99=‘keyword’ ANDtable4c.attr1=‘keyword’ AND
table11.attr10=table5a.attr10 ANDtable11.attr40=‘keyword’ ANDtable11.attr50=‘keyword’ ANDtable2b.attr1=table1.attr8 ANDtable2b.attr9 IN (‘keyword’) ANDtable2b.attr2 LIKE ‘keyword’% ANDtable12.attr9 IN (‘keyword’) ANDtable7b.attr1=table2a.attr10 ANDtable3c.attr13=table10c.attr1 ANDtable3c.attr10=table6b.attr20 ANDtable3c.attr13=‘keyword’ ANDtable10b.attr16=table10a.attr7 ANDtable10b.attr11=table7b.attr8 ANDtable10b.attr13=table4b.attr89 ANDtable13.attr1=table2b.attr10 ANDtable13.attr20=’‘keyword’’ ANDtable13.attr15=‘keyword’ ANDtable3d.attr49=table12.attr18 ANDtable3d.attr18=table10c.attr11 ANDtable3d.attr14=‘keyword’ ANDtable4d.attr17 IN (‘keyword’) ANDtable4d.attr19 IN (‘keyword’) ANDtable16.attr28=table11.attr56 ANDtable16.attr16=table10b.attr78 ANDtable16.attr5=table14.attr56 ANDtable4e.attr34 IN (‘keyword’) ANDtable4e.attr48 IN (‘keyword’) ANDtable4f.attr89=table5b.attr7 ANDtable4f.attr45 IN (‘keyword’) ANDtable4f.attr1=‘keyword’ ANDtable10c.attr2=table4e.attr19 AND(table10c.attr78=table12.attr56 OR(table10c.attr55 IS NULL ANDtable12.attr17 IS NULL))
At Statoil, it takes up to 4 days to formulate a query in SQL.
Statoil loses up to 50.000.000e per year because of this!!
Diego Calvanese (FUB) Ontologies for Data Integration FOfAI 2015, Buenos Aires – 27/7/2015 (5/52)
50.000.000 €persi ogni anno
Quali soluzioni?• Migrazione verso sistemi monolitici/integrati
• Sostenibile in termini di costi?
• Ogni quanto?
• Come integrare sistemi legacy?
• Come gestire la ridondanza dei dati?
• Non risolve il problema di “comprendere” i dati, comunque necessario per una buona migrazione
• Attenzione: questi problemi esistono anche per le PMI!!!
68
OBDA Accesso ai dati mediato da ontologie
69
OBDI framework Query answering Ontology languages Mappings Identity Conclusions
Ontology-based data integration framework
. . .
. . .
. . .
. . .
Query
Result
Ontologyprovides
global vocabulary
and
conceptual view
Mappingssemantically link
sources and
ontology
Data Sourcesexternal and
heterogeneous
We achieve logical transparency in accessing data:
does not know where and how the data is stored.
can only see a conceptual view of the data.
Diego Calvanese (FUB) Ontologies for Data Integration FOfAI 2015, Buenos Aires – 27/7/2015 (7/52)
sorgenti di dati
modello strutturale
mapping
OBDA• I dati vengono lasciati nelle loro sorgenti
• I mapping permettono di interpretare i dati secondo i modelli concettuali sviluppati
• Gli utenti finali esprimono le proprie interrogazioni sul modello concettuale
• Il sistema OBDA è “intelligente”:
• Traduce automaticamente le interrogazioni astratte in query eseguite sulle sorgenti di dato
• Aggrega e ritraduce le risposte così ottenute
70
Ontop• Sistema di OBDA open-source sviluppato alla
Libera Università di Bolzano (Prof. Calvanese)
• Supporta standard per il web semantico (OWL, SPARQL)
• Si integra con una pletora di DBMS relazionali
• Apache open license
• http://ontop.inf.unibz.it
71
72
Process Mining
73
1.3 Process Mining 9
Fig. 1.4 Positioning of the three main types of process mining: discovery, conformance, and en-hancement
However, most information systems store such information in unstructured form,e.g., event data is scattered over many tables or needs to be tapped off from sub-systems exchanging messages. In such cases, event data exist but some efforts areneeded to extract them. Data extraction is an integral part of any process miningeffort.
Let us assume that it is possible to sequentially record events such that eachevent refers to an activity (i.e., a well-defined step in the process) and is related toa particular case (i.e., a process instance). Consider, for example, the handling ofrequests for compensation modeled in Fig. 1.1. The cases are individual requestsand per case a trace of events can be recorded. An example of a possible traceis ⟨register request, examine casually, check ticket, decide, reinitiate request, checkticket, examine thoroughly, decide, pay compensation⟩. Here activity names are usedto identify events. However, there are two decide events that occurred at differenttimes (the fourth and eighth event of the trace), produced different results, and mayhave been conducted by different people. Obviously, it is important to distinguishthese two decisions. Therefore, most event logs store additional information aboutevents. In fact, whenever possible, process mining techniques use extra informationsuch as the resource (i.e., person or device) executing or initiating the activity, thetimestamp of the event, or data elements recorded with the event (e.g., the size of anorder).
Event logs can be used to conduct three types of process mining as shown inFig. 1.4.
Punto di partenza• Visione orientata ai processi
dei dati che tracciano la realtà
• IEEE Standard XES
• Dati di partenza ottenibili con…
• Tecniche ETL
• Data warehousing
• OBDA (ricerca attiva)
74
Technische Universiteit Eindhoven University of Technology
Log
Trace
Event
Float
Int
Date
String
Container
List
Attribute
Classifier
Extension name
prefix
URI
Key
Value
<declares>
<defines> <defines>
<defines><trace-global>
<event-global>
<contains>
<contains><contains>
<contains>
ID
Boolean
<orders>
Figure 2.1: The UML 2.0 class diagram for the complete meta-model for the XES standard
• One examination in which the x-ray machine is employed
• One visit of the website, by one specific user
Tag name for the trace object in the XML serialization of XES: <trace>
No XML attributes are defined for the <trace> tag.
2.1.3 Event
Every trace contains an arbitrary number (may be empty) of event objects. Events representatomic granules of activity that have been observed during the execution of a process. As such,an event has no duration. Examples of an event are:
• Recording the client’s personal information in the database has been completed
3 XES / Version 2.0
Play-in
75
register travel request (a)
get detailed motivation letter (c)
get support from local
manager (b)
check budget by finance (d)
decide (e)
accept request (g)
reject request (h)
reinitiate request (f)
start end
Case Activity Timestamp Resource432 register travel request (a) 18-3-2014:9.15 John432 get support from local manager (b) 18-3-2014:9.25 Mary432 check budget by finance (d) 19-3-2014:8.55 John432 decide (e) 19-3-2014:9.36 Sue432 accept request (g) 19-3-2014:9.48 Mary
credits to W.M.P. van der Aalst
Discovery
76 credits to W.M.P. van der Aalst
Play-out
77
register travel request (a)
get detailed motivation letter (c)
get support from local
manager (b)
check budget by finance (d)
decide (e)
accept request (g)
reject request (h)
reinitiate request (f)
start end
Case Activity Timestamp Resource432 register travel request (a) 18-3-2014:9.15 John432 get support from local manager (b) 18-3-2014:9.25 Mary432 check budget by finance (d) 19-3-2014:8.55 John432 decide (e) 19-3-2014:9.36 Sue432 accept request (g) 19-3-2014:9.48 Mary
credits to W.M.P. van der Aalst
Simulazione
78
Replay
79
register travel request (a)
get detailed motivation letter (c)
get support from local
manager (b)
check budget by finance (d)
decide (e)
accept request (g)
reject request (h)
reinitiate request (f)
start end
Case Activity Timestamp Resource432 register travel request (a) 18-3-2014:9.15 John432 get support from local manager (b) 18-3-2014:9.25 Mary432 check budget by finance (d) 19-3-2014:8.55 John432 decide (e) 19-3-2014:9.36 Sue432 accept request (g) 19-3-2014:9.48 Mary
credits to W.M.P. van der Aalst
Conformance Checking
80 credits to W.M.P. van der Aalst
Hands-on
• Piccola azienda dedicata allo sviluppo di tecniche di process mining
• Tool Disco
• Slide successive: credits a Anne Rozinat e Christian Günther
81
Scenario
82
Purchasing process ERP
Requester PurchasingAgent
RequesterManager
Supplier FinancialManager
Strategia
83
Questions Data Extraction Presentation Data
Analysis
Problemi
1.Gestione inefficiente
2.Lamentele sui tempi di gestione
3.Necessità di mostrare compliance
84
Estrazione dati
• Il personale IT estrae i log di eventi dal sistema ERP dell’azienda
• Il log è estratto come file di testo (campi separati da virgole)
85
ERP
CSVData
86
Importazione dei dati• Identificazione delle colonne
• Dove si trova l’identificativo dell’istanza di processo (id dell’ordine)?
• Dove stanno le attività?
• E i tempi di esecuzione?
• Dove trovo i ruoli e i responsabili delle attività?
87
88
Magia…
89
Dettagli
90
Zoom
91
Uscita!!!
Interazione!
92
fluxicon
fluxicon
Vista sui tempi di esecuzione
fluxicon
Visione organizzativa
96
Uno sguardo ulteriore• ProM framework (iniziativa accademica, centinaia di plug-in)
97
Screenshot of ProM 5.2
PAGE 7
Conclusioni• BPM come metafora di gestione organizzativa orientata alla
“qualità totale”
• Necessità di connettere i modelli prodotti alla realtà
• Problema di comprendere e accedere ai dati
• Process mining per arricchire, modificare, approfondire i modelli con “l’impronta digitale” della realtà
• Non solo IT!
• Molti altri temi: integrazione dati+processi, verifica, analisi della qualità, …
98
Grazie
99