towards(intelligentbusiness(...

Towards Intelligent business process modelling

UWL, School of Computing and Technology

1

Dr Samia Oussena

Business process modelling •  A Business Process is a collec:on of related, structured

ac:vi:es that produce a specific service or product (serve a par:cular goal) for a par:cular customer or customers

•  A Process Model is a formalized view of a business process represented as a coordinate set of parallel and/or sequen:al set of process ac:vi:es that are connected to achieve a common goal

•  Business process management (BPM) refers to methods, techniques, and tools that support the design, management, and analysis of business processes

BPMN and BPEL

•  BPMN (Business Process Modeling Nota:on) one of the most widely used to model BPs.

•  Execu:ng BPMN :BPEL –  BPEL is standard executable language for specifying ac:ons within BPs with web services

Towards intelligent business process

•  Adap:ve business process modelling –  The model adapt to its environment by considering its internal context

•  Smart business process modelling –  External context is considered in the model

•  Intelligent business process modelling –  Machine Learning control the processes

•  Engineering the intelligent business process model

MobWEL •  Gave a presenta:on with the :tle “Context-‐aware

Collabora:ve PlaTorm”

•  The aim of the work is to extend BPEL to be “context-‐aware”

Context

Device-‐centric: Bluetooth on/off Ba@ery level

User-‐centric: User preference Current task

Environmental: LocaGon Surrounding devices

Social: Work context of fellow collaborators

Adapted Workflow

MobWEL Metamodel of Adapted Process Control Flow

Architecture

Where do we go from here

•  Describing context in linked data

•  Provide more flexibility for reasoning

about context; i.e. similar context

•  Possibility to include external contexts;

including sensors data

•  Looking at context for single event and

complex events Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

Sensors

•  Sensors can measure anything from temperature, force, pressure, flow and posi:on, to light intensity

•  Sensors are embedded in devices in order to make them smart.

•  The term “Internet of Things” (IoT), is used to describe the ubiquitousness of sensing devices or smart objects, “things”, and their ability to be networked together (Gubbi et al., 2013)

Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013) ‘Internet of Things (IoT): A vision, architectural elements, and future direc:ons’, Future Genera,on Computer Systems. Elsevier B.V., 29(7), pp. 1645–1660

•  More generalized concept

•  IoE is concept that tries to connect everything that can be connected to the Internet, where everything refers to people, cars, televisions (TVs), smart cameras, microwaves, sensors, and basically any thing that has Internet-‐connec:on capability (Abdelwahab et al. 2014)

IoE

Abdelwahab, S., Hamdaoui, B., Guizani, M., Rayes, A.: Enabling smart cloud services through remote sensing: An internet of everything enabler. Internet of Things Journal, IEEE 1 (3), 276–288 (2014)

Key proper:es of IoT devices

•  A dis:nct iden:ty,

•  A set of sensors / actuators

•  They can communicate with other objects,

•  They exist in both the physical and virtual worlds

•  They can collaborate and interact with other objects directly

IoT Challenges

•  IoT devices are olen low-‐powered and this leads to problems in terms of what solware the devices can run.

•  Require management and monitoring –  device monitoring (for example, are devices s:ll alive, are they connected, and what is their bamery status?)

–  firmware and solware updates –  physical management (for example, installa:on, re:rement and reloca:on of things),

–  security management

IoT Challenges (cont.)

•  IoT applica:ons olen run on a wide variety of different device types such as Arduino, Raspberry Pi or other similar devices –  this heterogeneity offers its own challenges in terms of devices having their own flavours of opera:ng system, Applica:on Programming Interfaces (API’s) and solware stacks.

IoT Challenges (cont.) Data management : –  it may not be a viable op:on to store or transmit all the data •  Could we trust the filtered data? •  Do we need to trade off ―accuracy and usefulness? •  Is the data complete and correct?

–  Require real-‐:me data for some ac:ons •  Stream processing is an approach to addressing several of the

challenges concerning analysis of data that cannot be, may not be, or is bemer not stored

–  Combining data from mul:ple sources is a challenge, especially if this data comes in very different forms

IoT and workflow •  Solu:ons for execu:ng business processes relying on IoT

are becoming more and more common •  Adding process-‐awareness to the proper:es of the

cyber-‐physical systems promise higher of flexibility and automa:on

•  Extensions to business process modelling have been proposed –  Add IoT as a component –  BPMN to drive the IoT network configura:on –  Extension to the execu:on phase –  Integra:on of the extension to both the modelling and the execu:on phase.

BPM extension for IoT

•  Meyer et al. propose to extend the BPMN 2.0 nota:on to model smart devices as process components.

•  This approach keeps the process knowledge on the informa:on system, and no process fragments are introduced on smart devices.

Meyer, S., Ruppen, A., Magerkurth, C.: Internet of things-‐aware process modeling: In-‐ tegra:ng IoT devices as business process resources. Lecture Notes in Computer Science (including subseries Lecture Notes in Ar:ficial Intelligence and Lecture Notes in Bioinfor-‐ ma:cs) 7908 LNCS, 84–98 (2013). DOI 10.1007/978-‐3-‐642-‐38709-‐8 6

BPM extension for IoT •  Tranquillini et al., propose a framework that employs BPMN for driving the

configura:on of a Wireless Sensor Network (WSN)

•  Sungur et al. Extend the work and propose a meta-‐model extension for BPMN to support the modeling and processing of wireless sensor networks .

Tranquillini, S., Spieß, P., Daniel, F., Karnouskos, S., Casa:, F., Oertel, N., Mot-‐ tola, L., Oppermann, F., Picco, G., Ro m̈er, K., Voigt, T.: Process-‐based design and integra:on of wireless sensor network applica:ons. In: Proc. BPM 2012, Berlin, Heidelberg, Springer-‐Verlag (2012) 134–149 Sungur, C.T., Spiess, P., Oertel, N., Kopp, O.: Extending BPMN for Wireless Sensor Networks. 2013 IEEE 15th Conference on Busi-‐ ness Informa:cs pp. 109–116 (2013).

BPM extension for IoT

•  Schief et al., propose a centralized framework that extends the process design and execu:on phases of BPM by taking into account events generated by Smart Objects.

•  The framework provides data quality mechanisms for evalua:ng events and sensor data.

Schief, M., Kuhn, C., Rsch, P., Stoitsev, T.: Enabling business process integra:on of iot-‐events to the benefit of sustainable logis:cs. Technical report, Darmstadt Technical University (2011)

BPM extension for IoT •  Seiger et.al developed PROtEUS an integrated system for process specifica:on and

execu:on in cyber-‐physical systems

•  It consists of a core engine for execu:ng model-‐based processes, a complex event processing engine for the integra:on and processing of low-‐level sensor data, and a service invoker for calling on-‐site or external services.

Seiger, R., Huber, S., Schlegel, T.: Proteus: An integrated system for process execu:on in cyber-‐physical systems. In: K. Gaaloul, R. Schmidt, S. Nurcan, S. Guerreiro, Q. Ma (eds.) Enterprise, Business-‐Process and Informa:on Systems Modeling, Lecture Notes in Business Informa:on Processing, vol. 214, pp. 265–280 (2015).

Intelligent Machines

•  Advanced machine learning is what makes smart objects appear “intelligent” by enabling them to both understand concepts in the environment, and also to learn.

•  Through machine learning a smart object can change its future behavior

Machine Learning

•  Machine Learning is the use of algorithms that learn itera:vely from data

•  Categorised into three different groups: –  Unsupervised Learning : aim to describe and classify the data by finding similari:es between groups of data points.

–  Supervised Learning : use pamerns in the data to predict the class labels or values

–  Reinforcement Learning: learn to react to its environment

Machine Learning applica:ons

Machine learning

applica:ons

Classifica:on(face, speech, image)

Anomaly detec:on (intrusion disease)

Forecast (weather, price, ra:ng)

Autonomous machines (robot, vehicle)

Machine Learning Algorithms

Type Typical algorithm

Classifica:on

• Decision trees • Naïve Bayes • Logis:cs regression

Regression: predic:ng (numerical value)

Linear regression Logis:c regression

Associa:on rules Apriori

clustering K-‐means

Nearest neighbour Nearest neighbour

Deep Learning/reinforcing learning

Type

Deep Neural Networks

Classifica:on and regression

ConvoluGonal Neural Networks (CNNs)

Used for computer vision

Recurrent Neural Networks (RNNs) Used for :me series analysis

Deep Boltzmann Machines Used for recommenda:on systems

Deep Q-‐learning Combine Reinforcing learning with neural network

Machine Learning and Business process modelling

•  Used to predict the outcome of a process

•  Used to predict the reaming :me for a process to complete

•  Recent publica:ons –  Cogni:ve compu:ng

Time remaining to comple:on •  Folino et al. present two contribu:ons that use clustering trees and

finite state machines (FSM) to predict the remaining :me of a running process case

•  Bevacqua et al. present a predic:on technique based on clustering and regression on case data

•  Bolt et al. employ a clustering approach on par:al and completed cases

•  Polato et al. present two approaches that are based on annotated transi:on systems, as well as support vector regression and naive Bayes classifiers

Folino, F., Guarascio, M., Pon:eri, L.: Discovering context-‐aware models for pre-‐ dic:ng business process performances. In: On the Move OTM Confederated In-‐ terna:onal Conferences, Rome, Italy, September 10-‐14, 2012. Proceedings, Part I. (2012) 287–304 Bevacqua, A., Carnuccio, M., Folino, F., Guarascio, M., Pon:eri, L.: A data-‐driven predic:on framework for analyzing and monitoring business process performances. In: Enterprise Informa:on Systems -‐ 15th Interna:onal Conference, ICEIS 2013, Angers, France, July 4-‐7, 2013, Revised Selected Papers. (2013) 100–117 Polato, M., Sperdu:, A., Buravn, A., de Leoni, M.: Data-‐aware remaining :me predic:on of business process instances. In: 2014 Interna:onal Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, July 6-‐11, 2014. (2014) 816–823

Process outcome

•  Kang et al. present two different approaches to predic:ng process failures, one using a support vector machine (SVM) and another one based on clustering and local outlier detec:on

•  Evermann et.al. used Deep Leaning (RNN) to predict the behaviour of running processes

Evermann, J., Rehse, J.R. and Femke, P., 2016. A deep learning approach for predic:ng process behaviour at run:me. In Proceedings of the 1st Interna:onal Workshop on Run:me Analysis of Process-‐Aware Informa:on Systems. Springer.

Cogni:ve compu:ng

•  Hull et.al. discuss a conceptual framework for Cogni:ve BPM.

•  The basis of the framework includes all kinds of informa:on that Cogni:ve Compu:ng can make sense of, including unstructured data, Internet of Things (IoT) data, new kinds of “smart” devices.

Hull, R. and Nezhad, H.R.M., 2016, September. Rethinking BPM in a Cogni:ve World: Transforming How We Learn and Perform Business Processes. In Interna:onal Conference on Business Process Management (pp. 3-‐19). Springer Interna:onal Publishing

Research challenges •  Advances in goal iden:fica:on and planning •  knowledge representa:on, priori:za:on, and

explana:on –  enabling agents to take advantage of knowledge that is relevant to a decision or task at hand, and ignore knowledge that is irrelevant.

•  Advances are needed in process-‐specific knowledge acquisi:on.

•  event monitoring and triage –  tools that enable appropriate response to incoming events, be they from the environment, from agents, or from newly acquired knowledge.

Data Driven processes

•  Data modelling needs to be considered as first-‐class ci:zens in the same way as process ac:vi:es

•  Two types of data: – Data that support decisions – Data about the processes

•  Data with the applica:on of Machine Learning models allows us to make faster and more intelligent decisions as well as control the environment effec:vely –  Model ML workflow

ML workflow

•  What data do we have ? •  Data Integra:on •  Data explora:on •  Data Cleaning •  Feature Engineering –  Data Transforma:on –  Data Reduc:on

Solware Engineering Prac:ces

•  BPMS are solware solu:ons

•  Solware Engineering prac:ces •  Tes:ng –  Tes:ng forms a fundamental part of the solware development life cycle and is a key solware engineering principle •  In IoT most research is in network communica:on protocols… •  TDD is de facto approach in solware •  BDD : expected behaviours are used to drive the development and ul:mately act as a way of measuring whether the right solware has been developed

Solware Engineering Prac:ces (cont.)

•  Reproducible environment (Dev, prod.) –  Strict separa:on between the build, release and run (prod) –  Containers make it easy –  Automa:on

•  Con:nuous integra:on –  minimizing code conflicts and maximizing efficiency –  Build and test cycle –  micro-‐services to deliver modular, flexible and dynamic solu:ons •  Domain Driven Development to be considered modeling business processes

Conclusions

•  Processes/ process fragments operates as learning organisms, interac:ng with their environment and con:nuously improving, based feedback

•  Processes need to be self-‐assemble, self-‐regulate and evolve — we do not directly specify them

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