Milan Zdravković presented on next-generation enterprise information systems (NG-EIS) and challenges in research opportunities. Key points include hypothesizing properties and a generic architecture for NG-EIS, such as omnipresence, multiple identities, and dynamic configurability. Implementation of EIS remains challenging due to high costs and failure rates. Semantic interoperability, requirements engineering, and model-based systems engineering are important areas of focus for overcoming IoT system challenges.

1. NEXT-GENERATION EIS – RESEARCH
OPPORTUNITIES AND CHALLENGES
Milan Zdravković
Baltic DB&IS 2018, July 1-4, Trakai, Lithuania

2. Baltic DB&IS 2018, July 1-4, Trakai, Lithuania
ABOUT
 My research
 Semantic interoperability/formal modelling, MBSE, IoT; domains:
SCM, orthopaedic devices
 86 papers, 14 journal papers with IF, 308 citations, h=10,
 My academic/community work
 Guest edited 3 journal special issues, never said no to review/evaluation
assignment
 Chairing ICIST conference series since 2014
 Member of IFAC TC5.3, IFIP WG8.1
 Fascinated with research policy
 Hands-on
 1 start-up, 2 spin-offs
 Enjoy working with local IT communities
 Can’t live without music, skiing and travel
http://www.masfak.ni.ac.rs/milan.zdravkovic/

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IFAC TC 5.3 TECHNICAL COMMITTEE ON
ENTERPRISE INTEGRATION AND NETWORKING
 TC 5.3 fosters research in
 enterprise integration and interoperability
 enterprise architectures,
 enterprise engineering methods,
 enterprise modelling
 https://tc.ifac-control.org/5/3

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FIND US @
 13th OTM/IFAC/IFIP International Workshop on
Enterprise Integration, Interoperability and
Networking (EI2N 2018)
 Valletta, Malta, October 24-25, 2018
 16th IFAC Symposium on Information Control
Problems in Manufacturing
 Bergamo, Italy, June 11-13, 2018

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INTRODUCTION
English translation of Welsh: “I am
not in the office at the moment.
Please send any work to be
translated”

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DATA
NEVER
SLEEPS
5.0
https://www.domo.com/learn/data-never-sleeps-
5?aid=ogsm072517_1&sf100871281=1

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TODAY, DATA IS NOT CREATED ONLY BY PEOPLE
 Pratt & Whitney’s Geared
Turbo Fan (GTF) engine is
fitted with 5,000 sensors
and can generate up to
10GB of data each second
 Data generated by the
aerospace industry alone
could soon surpass the
magnitude of the
consumer Internet. http://aviationweek.com/connected-aerospace/internet-
aircraft-things-industry-set-be-transformed

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GARTNER’S HYPE CYCLE FOR THE INTERNET
OF THINGS

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THE FIRST BUZZWORD TODAY:
SENSING ENTERPRISE
 “an enterprise anticipating future decisions by using multi-
dimensional information captured through physical and virtual
objects and providing added value information to enhance its
global context awareness” (FInES cluster, Santucci et al, 2012)
 Key tech enablers for Sensing Enterprise are IoT and big data

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WHAT ARE IOT PLATFORMS?
 Cloud-based, Platform-as-a-Service
 Typical features
 connectivity as a service, monitoring and maintenance of devices
(including firmware updates), data visualization, data analytics, basic
application logics through alerts and triggers.
 Core market niches (Zdravkovic et al, 2016)
 Domain-specific (turnkey) platforms
 Technology-specific platforms
 M2M connectivity services
 Full-scale generic IoT middlewares
 Supporting services

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BASIC INFRASTRUCTURE COMPONENTS OF
IOT PLATFORMS
IoT Gateway
IoT Platform
IP Device
MQTT
HTTP REST
Thin client
Platform
specific
client SDK
Monitoring
Data analytics
Maintenance
Data storage
External services
– not native IoT
platforms
Data storage
API
Business rules,
e.g. IFTTT
API
Advanced
analytics
API
Advanced
visualization
API
….
API
IP Endpoints
Custom, domain
specific backend
API
Device-oriented services
Security
Data visualization
Data-oriented services
HTTP REST
PAN Endpoints
ZigBee
Bluetooth
6LowPAN
Platform
specific
gateway
SDK
Client
Data pre-processing
Scalability
Infrastructural services
CoAP
PAN Device
Trigger-response-
alert rules
App-oriented services
Scripting engine -
interpreter
Reasoning

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WHAT’S WRONG WITH IOT PLATFORMS?
 IoT platforms are typically driven by models of the trivial complexity; they
support very simple data structures and almost no business logic
implementation
 Application Logic (AL) processing in IoT platforms is a bottleneck
 AL not expressive, if-then rules, or..
 => NOT EFFECTIVE
 AL complex - hidden in code, this code is written by using specific platform
SDKs.
 => NOT FLEXIBLE
 AL not formal, no semantics used, no interoperable with other platforms/systems
 => NOT INTEROPERABLE
 IoT systems are today usually managed centrally

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HYPOTHESES: PROPERTIES AND GENERIC,
ABSTRACT ARCHITECTURE OF NG-EIS
Omnipresence
Multiple identities
Model Driven
Architecture
Awareness/inclusive
sensing
Openness Dynamic configurability
Computational flexibility
Distributed identities
Models infrastructure Services infrastructure
Deployment platform
Interoperability infrastructure
Core execution environment
Security
Trust
Scalability
Performance
Next Generation Enterprise Information System

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http://www.tamingdata.com/2010/07/08/the-project-management-tree-swing-cartoon-past-and-present/

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OH YES, EIS IMPLEMENTATION IS STILL A
PROBLEM
 In 2010, the mean Enterprise Resource Planning (ERP) systems’
implementation cost was $5.48 million, and the average implementation
time-frame was 14.3 months (Galy and Sauceda, 2014)
 On average, large IT projects run 45% over budget and 7% over time,
while delivering 56% less value than predicted (McKinsey, 2012)
 85% of existing devices worldwide are based on unconnected legacy
systems (IDC, 2013)

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BASIC IMPLEMENTATION BLUEPRINT

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INTEROPERABILITY FOR IOT
 Interoperability is the ability to
communicate with peer systems and
access the functionality of the peer
systems (Vernadat, 1996)
 In IoT:
 ReST (Representational State Transfer)
 CoAP (Constrained Application
Protocol)
 MQTT (Message Queue Telemetry
Transport)
 XMPP (Extensible Messaging and
Presence Protocol)
Google Cloud Internet of Things Core, End-to-End Example
https://cloud.google.com/iot/docs/samples/end-to-end-sample

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INTEROPERABILITY AT SEMANTIC LEVEL
 60-80% of the resources in data sharing projects are spent on addressing the
issues of semantic heterogeneities (Doan et al, 2004)
 IoT services, namely devices’ capabilities can be semantically represented, so
the process of discovery and selection is done by using SPARQL queries
(Song et al, 2010)
 Scalability, flexibility and other issues cannot be resolved with centralized
approach.
 Each of the devices must be represented by (and connected to) its virtual
counterpart, which is implemented as autonomous software agent (Katanasov et al,
2008).
 IoT platform is then only a run-time environment for these software agents.

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SEMANTIC INTEROPERABILITY IN IOT
SensationPerception
Cognition Articulation
∃R(system(R))
Sensation Perception
CognitionArticulation
∃S(system(S))
∀p (
(transmitted-from(p,S) ∧ transmitted-to(p,R)) ∧
∀q(statement-of(q,S) ∧ p⇒q)
∃q’(statement-of(q’,R) ∧ p⇒q’ ∧ q’⇔q)
) ⇒ interoperable(S,R)

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BUILDING BLOCKS FOR SEMANTIC
INTEROPERABILITY IN IOT
 In order to interoperate with another device, each device must sense, perceive, interpret and
understand data, sent from another device and act (operate) upon this understanding.
 Abstract the heterogeneity of devices, so one device can better understand the capabilities of
another
 Sensor Network (SSN) Ontology (Compton et al, 2012)

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INTEROPERATION ENGINE
 NOT a middleware, it does not mediate traffic, but meaning
 Interoperating Engine is basically a formal reasoner with extensions
 Computed values can be defined in OWL (Iannone and Rector, 2008)
 Alternative: pre-processing (with different modeling approach) which will classify data in ranges of interest.
 PR-OWL - Probabilistic extensions, based on Bayesian network (Costa and Laskey, 2006)
 Probabilistic/fuzzy can be defined by Fuzzy OWL 2 (Bobillo and Straccia, 2010), there are
reasoners for that (DeLorean)

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REQUIREMENTS ENGINEERING FOR IOT
SYSTEMS
 To be transformed to models – models make the decisions of RE
explicit
 Key concepts for RE for IoT (Zambonelli, 2016)
 goals - desirable situations or state of the affairs that should be activated
and which should be decomposed to system requirements
 identification of stakeholders and users who will manage or use systems’
functionalities and from which functional requirements should be elicited

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CHALLENGES FOR RE AND MANAGEMENT
 Elicitation of non-functional requirements, such as security and
privacy, performance, reliability, energy usage, impact to
environment
 Prioritization
 New requirements will emerge, related to decision-making
capabilities and higher level of automation
 Incoming data as a source for new requirements
 New change management strategies

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MODEL-BASED SYSTEM ENGINEERING (MBSE)
FOR IOT SYSTEMS
 OMG Standards for IoT
 System Modeling Language (SysML - INCOSE and OMG)
 language for systems specification, analysis, design, verification and validation
 Less implicit formalisms are used at application design level - Domain Specific Languages (DSL)
 Conceptual models as starting effort towards DSL (Patel and Cassou, 2014)
 Midgar IoT platform with DSL (Garcia et al, 2014)
 ThingML for forward engineering of IoT (Harrand et al, 2016)
 To facilitate interaction modeling in a heterogeneous environment, many researches rely on the explicit -
formal models
 Devices will be formally represented and managed, registered, aligned, composed and queried through suitable
abstraction technologies (Kotis and Katasonov, 2013)
 Formal models are convenient for integrating different views – levels of abstraction
 Formal models can be used also for system and application design

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CHALLENGES FOR MBSE
 Efficiency related
 Cost (of time consumed and errors made) of manual creation of models,
including maintaining dependencies between different model views
 Model mapping and transformation
 Rationality related
 Cost of choices made in very heterogeneous environment of non-synced
models, languages, beliefs and preferences of the system engineers.
 Lack of tools for validating model consistency, completeness and dependability
 Lack of formalisms for representing business logics

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FORMAL FRAMEWORK FOR MULTI-FACETED
VIEW TO IOT ECOSYSTEM
 Model validation problem could
be solved by using Formal
Specification Techniques (FST)
 Use of formal models and
associated methods (semantic
annotation, ontology matching)
 Towards ontology-driven IoT
systems, which use formal
models in a runtime

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SYSTEM ARCHITECTURE FOR IOT
 IoT system is inherently distributed
 Agent-based node in IoT systems is independent, self-
governing software and hardware integrated system. It is
capable to sense, to interpret the sensation, make the best
informed decision on that interpretation and finally, act
upon it
 intelligent goal-based agent
 Composition approaches
 Centralized
 Decentralized

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CHALLENGES RELATED TO SA
 Trust models, which will ensure that proper decisions are made
in environment of uncertain credibility, high reliability and
security concerns
 Self-management capability of IoT systems, by realizing the
context-awareness and self-adaptation properties of the
individual agents (Ayala et al, 2015)

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MATURITY MODELS FOR IOT
 MM used to continuously assess the capability of a system
 to maintain its operation at the agreed quality levels,
 to evolve, sometimes even in very short time frames, on demand
 MM typically include a sequence of levels (or stages) that form an
anticipated, desired, or logical path from an initial state to maturity
(Becker et al, 2009)
 IoT MMs
 MM and tool to assess manufacturing companies adherence (Schumacher et al, 2016)
or readiness (Lichtblau et al, 2015) (Rockwell Automation, 2014) to the Industry 4.0
vision
 MM to enable organizations to assess their readiness for IoT and to compare
themselves against others with IoT initiatives (Halper, 2016) or progress in
implementing IoT (Vachterytė, 2016)
SELF-AWARE
SENSING
SMART
ACTIVE

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CHALLENGES RELATED TO MM
 Maturity assessment reference ontology, which generically and
formally defines improvement paths and maturity levels
 In context, defined by the domain ontologies
 With implicit problem representations, defined by application
ontologies

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IOT POLICY AND
REGULATORY
ASPECTS
 Those issues affect
each of the
elements of the IoT
systems
implementation
framework

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EXTENDED DOMAIN FRAMEWORK FOR IOT SYSTEMS
IMPLEMENTATION

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CASE STUDY – IOT ENABLES MANUFACTURING
SHOP-FLOOR

38. NEXT-GENERATION EIS – RESEARCH OPPORTUNITIES AND
CHALLENGES
Milan Zdravković
Baltic DB&IS 2018, July 1-4, Trakai, Lithuania
THANK YOU FOR YOUR ATTENTION
Q&A