TDT20: Enterprise Architecture, Smart Cities and Value-Added services
A Comparison of the Top Four Enterprise Architecture Approaches in 2014
What is EA?
Enterprise
An organizational unit - from a department to a whole corporation
Architecture
Formal description of a system or a detailed plan of the system at component level to guide its implementation
The structure of components, their inter-relationship and principles and guidelines governing their design and evolution over time
EA
A formal description of an enterprise, a detailed map of the enterprise at component level to guide its changes
The structure of an enterprise’s components, their inter-relationships and the principles and guidelines governing their design and evolution over time
The open group definition
EA is about understanding all of the different components that go to make up the enterprise and how these components inter-relate
IFEAD
A complete expression of the enterprise
a master plan which acts as a collaboration force
Why does an organization need an EA?
To get an overview of the business processes, systems technology, structures and capabilities.
Provides a strategic context for development of the IT system in constant change
Achieve competitive advantage
Value for the IT organization
Deeper understanding of organizational strategic intent
Correct IT investment allocation
Realized economics of scale
Elimination of redundancies
Reduced IT delivery time due to reuse
Higher quality decision making at all levels
An organization that works on the right things at the right time
Selection/identification of correct technologies/functionality required by the organization
An understanding of what we are doing and why as well as how individual roles and responsibilities support creation of an environment for enterprise success
EA key concepts
Stakeholder concerns
interests that are critical or important to stakeholders
Principles
a univocal understanding about what is of fundamental importance for the organization
Models
purposeful abstractions of reality
Views
difficult to make a univocal and comprehensive set of models that can be understood by all concerned, hence views
Framework
Structure to select views
EA approaches
Zachman
An architectural framework in which an enterprise is modeled as thirty or thirty-six cells, each of which represents an intersection between a stakeholder perspective and an abstraction.
Taxonomy for describing an enterprise
Matrix
Aspects
what
how
where
who
when
why
Viewpoints
Planner
Owner
Designer
Builder
Subcontractor
Strength
Comprehensive taxonomy to describe the enterprise
Weaknesses
No process to get to the finished product
No regards for future architecture
TOGAF
An architectural methodology that is controlled by The Open Group.
consists of
Architectural Development Method (ADM)
Foundation Architecture
Technical Reference model (TRM)
Standards Information Base(SIB)
Building Blocks Information Base (BBIB)
Resource base
Architectural views
IT Governance
Business scenarios
Architecture patterns
ets.
Most important part is the ADM
Method for creating EA
Sessions calls TOGAF process
Complements Zachman
Benefits
flexible about architecture generated
Weakness
TOGAF tells you how to generate A EA - not necessarily a good one
FEAF - Federal Enterprise Architectural Framework
An enterprise architectural framework used by the United States federal government to describe how the various governmental agencies and their IT systems are related to each other.
VRF - Value realisation framework
Enterprise is modeled as collection of capabilities
Each capability performs a function for the enterprise
Goal is to identify capabilities in need of improvement
Capability
Consumes consumebles
creates resources
expends fuel
Agile EA: Oxymoron or new vision:
Introduction:
Enterprise Architecture involves both textual and graphical representations that comprise an integrated set of composite models that are normally built using one of the major EA tools.
Once the models are contained in a tool it allows decision makers in an organization or extended enterprise the ability to query across all models the rippling effects of any actual or planned change.
Architectures, Ontologies and Frameworks
John Zachman describes how architectural instantiations such as buildings (and enterprises) are the result of architectures
In that result we can see the Architect’s “architecture”
Architecture is the set of description representations for characterizing complex objects, such that an instance of the object can be created and serve as a baseline for changing it
Each of these perspectives relate to six different columns associated with the Aristotelian questions – what, how, where, who, when and why. The “what” refers to the entities involved, the “how” to processes, the “where” to locations, the “who” to roles and responsibilities, the “when” to timing and the “why” to motivations and business strategies.
So, for instance in the “what column” the executive perspective addresses a bill of materials or things relevant to the business, the management perspective would treat these as data entities in a semantic data model, the architect would understand these as logical data model, the engineer as a physical data model, etc.
The enterprise conundrum
Emergent and Agile enterprises are inconsistent with the traditional ontological view of an organization as an objectively observable activity
This objective view sees the enterprise as something that can be measured, labeled, classified, and related to other organizational processes
From this perspective, EA considers the enterprise as a static snapshot to serve as a baseline for target architectures
James Thompson (1967) and Jeanne Ross (2011) describe how organization structure is affected by types of technology - which differs depending on the degree of interdependence (integration) in the transformation process.
The Complex Adaptive System (CAS) Perspective
Contrary to the objective view is the recognition of organizations as complex adaptive systems that give rise to considerations of emergence, leading to a derived definition of emergent and agile enterprise architectures.
This approach identifies organizations as non-linear and highly complex systems
This necessitates a rethinking about how they are to be represented within the context of EA
Dealing with Organizational Messes rather than Problems
In a real sense, problems do not exist. They are distractions from real situations. The real situations from which they are abstracted are messes.
A mess is a system of interrelated problems. We should be concerned with messes, not problems.
The solution to a mess is not equal to the sum of the solution to its parts. The solution to its parts should be derived from the solution of the whole, not vice versa
Science has provided powerful methods, techniques and tools for solving problems, but it has provided little that can help in solving messes. The lack of mess.solving capability is the most important challenge facing us.
- Russ Ackoff
Enterprise Messes and the Need for an Emergent EA
Problems have solutions. Messes do not have straight forward solutions.
Messes are more than complicated and complex. They are ambiguous.
They contain considerable uncertainty - even as to what the conditions are, let alone what the appropriate actions might be
They are bound by great constraints and are tightly interconnected, economically, socially, politically, technologically.
They are seen differently from different points of view, and quite different worldviews.
They contain many value conflicts.
They are often a-logical or illogical.
The Gartner View of Emergent EA
A Case of TOPEND: Knowledge Spaces and Knowledge Modeling
In the early 90s the NCR Corporation owned TOPEND - a transaction-oriented middleware platform that was eventually sold to BAE Systems and incorporated into Tuxedo
Although there was an active TOPEND consulting practice there were only a few designers who had the reputation of being TOPEND Gurus.
NCR wanted to capture their expert knowledge and sent them to a location in Norway and had them define a set of best practises.
Bellman was tasked with modeling thos best practises into METIS.
When the models were shown to the TOPEND architects they agreed the knowledge was correctly modeled.
However, they argue the models were analogous to learning to paint by numbers. One can paint a Picasso painting by the numbers, but this does not infer one is a Picasso.
This points to a need for another approach to deal with corporate memory and understand the phenomenological nature of knowledge spaces.
Define the Lines rather than the Boxes
Implications for the TOGAF ADM
To-be is a moving target
Continual iterations of the ADM Phases as the enterprise adapts to transitional architectural steps.
Areas of Focus transform into ellipse change cycle patterns.
- Interaction and interferences from different areas of focus is expected.
Requirements Management begins to take on a “hive intelligence” structure.
A Representation
Imagine that exchange “planned” information system (IS) change is like an ellipse going out from the initial point in time and ending on a planned end time.
Now, move forward a unit in time, and a new change in the IS becomes necessary.
If the change can be found within the first eclipse, then it would expand somewhat. Otherwise, it would be a new ellipse layered on top of the old one.
If you repeat this n times for n changes, then you will see a pattern of overlapping ellipses stretching forward.
A wave front is revealed, if you draw two (curving) lines connecting the outer edges of the ellipses. This model is close to one of the supersonic sound wave barriers.
Architecting for emergence is a naturally disruptive process
Change cycle Ellipses
Change ellipse has a beginning and a release; there is no “end”
Internal change path is not necessarily linear
Change can be interrupted, redirected or ended at anytime
Many changes can and will occur at once. They can interact or interfere with one-another
Yet, Maintaining Quality of Service is mandatory
Emergent Architecture must consider how to balance system integrity with ongoing complex changes
There is no “to be” end state in architecting for emergence
Emergent Change Cycle Patterns
Change ellipses can overlap, split and recombine
- Look for the emergent, yet repeated, patterns of change
They exist, change or “die off” due to either
- Endogenous change (Internal stakeholders, resources, time, policies, directives...)
- Exogenous change (External related or competing systems, operational environment, external stakeholders, policies, change in technologies, mergers and acquisition, etc…)
Active and work-centric knowledge have important intrinsic properties found in mental models including reflective views, recursive tasks, repetitive roles, and replicable knowledge architecture elements.
This points to the need to discover the rules of change cycle complexity.
Conclusion
In conclusion we need to consider architecture as a crucible(smeltedigel) that entails the trade-offs between resource availability and constraints, along with meeting organizational/enterprise goals and user experience.
Agile is a set of actions that focuses and models the results to meet acceptance criteria, while enabling ongoing adaptation and accounting for emergence. Architecture is the representative artifact of the solution. We must consider how agile augments(make greater, more) and changes architecture methods to produce solutions that embrace greater user experience, while enabling adaptation. We must recognize how architecture
brings focus and system discipline to agile.
Reflection
How is this applicable to my project?
Startups are small enterprises. They are emergent and develop FAST! Especially for me, using lean, this article shines a light on the fact that the architecture of my business is a crucible that entails the trade-offs between resource availability and constraint. The requirements for the enterprise is ever changing, as well as the resources, the stakeholders, the enterprise goals and user experience.
Many consider Agile EA to be an oxymoron. For me it is highly relevant. Yes, EA could be the blueprints and the plan for the enterprise, but all businesses do eventually need to change and adapt, and it is especially relevant for startups.
Framework to develop a business synergi through EA
Research problem:
There is a waste of resources and data owned or produced by organizations
Applying analysis in the EA concept can convert data into valuable info that again can be used in decision making
Leads to competitive advantage in production;
improve profitability by implementing insight in the production activities
Related work:
there is a lack of analysis of enterprise complexity
lack of clear model og mapping to goal a virtual EA
Lack real case applications
Proposal:
Virtual organizations are built by collaborations and partnerships between organizations.
Create synergy and satisfy the strategic needs:
making profit from handling data and processes.
This paper study integration between organizations through the concept of EA Exploiting the knowledge obtained from processes, data and technology
Characteristics of framework
Evolvability: capacity to change according to demand in market and customer demands.
Flexibility: Demand, Integration, Cooperation, Agility, Adaptability
EA Components: Make use of data to conveniently integrate and offer flexible products demanded.
Conclusion
Data is taking an important role in the management and development of big enterprises.
In the era of e-commerce, it is necessary to leverage knowledge through the value chain in the virtual organization to enhance performance.
Reflection:
How is this relevant to my task?
The Software I am developing for my master thesis could become the groundwork for a startup company. As a small time company, the fact is that I may not be able to afford good IT infrastructure. At the same time, I have little to no data or information about customers. By creating a virtual enterprise with one of my competitors, I may both save resources on IT infrastructure, as well as share data with them, creating information and knowlegde on the field and creating a product for the potential customers.
Defining Smart City Architecture for Sustainability
Introduction
Smart city definition is dependent on perspective.
Close to a common definition: Innovation–not necessarily based on the information and communications technologies (ICT)-, which aim to enhance urban performance in terms of people, governance, mobility, economy, environment and living.
Smart city domain has emerged massively, with 40B EUR in 2016 and 3 trillion USD by 2025
Most investments in the smart city market are public, as there is still a resilience to invest from private investors.
Governments are positive to standardization. Standards have been developed for smart city solutions, but not for smart cities as a whole.
The aim of this paper is to define a common smart city architecture to serve government purpose for innovation and sustainability
Background
A smart city is divided into 6 dimensions:
People: in terms of discovering and meeting today and future requirements;
Living: enhancing quality of life and social coherency, as well as efficiency regarding energy, food, water etc.
Environment: protection, waste and emissions control and resilience against climate change;
Governance: in terms of ensuring urban utility and service availability;
Economy: in terms of sustainable growth and city competitiveness;
Mobility: addressing transportation and traffic management issues.
Existing smart city architecture approaches
Next the paper talks about module definition:
Module definition for a smart city is an extremely complex process and it has to consider both the type and the architecture
According to the mentioned analysis of previous research, soft urban infrastructure (people, data and applications) is flexible and can easily extend and interconnect.
Difficulties rise from requirements, which deal with hard infrastructure and environment.
Modular architecture approach:
1. Networking Infrastructure and Communications Protocol:
addresses necessary infrastructure to deploy smart services and enhance living within the smart city
Cities from scratch have innovations based on hard infrastructure
- New Songdo in South Korea is one of these from scratch cities, and has a waste disposal, recycling and tele-heating factory installed and interconnected with all buildings in the city. They also have fiber-optic networks connect all local buildings with a central operating center, while smart buildings are accessible by their inhabitants via specific applications
In existing cities there is innovation with existing hard infrastructure with the IoT and basically with sensors that exchange data with specific applications.
2. Applications
Module concerns all smart applications available in the smart city
Good method for analyzing the module could be classifications in the four smart city dimensions, including a separate group for mobility
3. Business
Adresse the business groups available in the city
4. Management
Contains rules and procedures for managing smart city elements. Elements include:
- Processes
- people
- resources
- land
-information
5. Servies
Concerns all types of smart city services offered with the contribution of the ICT from smart city supplier-side users and requested by demand-side users (inhabitants)
The paper suggests that the architecture of the smart city must be multi-tier in order to be clear and sustainable, in terms of standardization and communication of these standards. This because the examined cases in this study mostly use N-tier architecture.
Layer 1) Natural Environment: it concerns all the environmental features where the city is located
(landscape, rivers, lakes, sea, forests etc.).
Layer 2) Hard Infrastructure (Non ICT-based): it contains all the urban features,
which have been installed by human activities and are necessary for city operation
(buildings, roads, bridges, energy-water-waste utilities etc.)
Layer 3) Hard Infrastructure (ICT-based): it concerns all smart hardware, with which
SSC services are offered
(i.e., data centers, supercomputers and servers, networks, IoT, sensors etc.)
Layer 4) Services: all types of smart city services, grouped in the smart city six
dimensions and organized according to international urban key-performance indicators.
Layer 5) Soft Infrastructure: individuals and groups of people living in the city, as
well as applications, databases, software and data, with which the SSC services are
realized.
Conclusion
Addressing lack of standardization, grounding RQ1
Used literature findings and data from well-known smart cities
Findings suggest a common architecture must be multi-tier with 5 layers
This architecture can be the baseline for smart city standardization
Reflection
How is this paper relevant to my task?
It really isn’t. This paper mainly reviews previous research regarding EA for smart cities, and suggests a standardization for Smart cities, but I can’t relate this to my project as my project doesn't deal with smart cities.
A CONCEPTUAL ENTERPRISE ARCHITECTURE FRAMEWORK FOR SMART CITIES
A survey based approach
Definitions and goals
Smart city:
“A city well performing in 6 characteristics, built on the ‘smart’ combination of endowments and activities of self-decisive, independent and aware citizens”
Goals:
Discover important quality properties for smart cities and propose a conceptual architectural framework for smart cities with these properties. This framework can be used as a starting point for actual smart city architectures.
Understand the current state in business aspects in relation to the IT support infrastructure for smart cities’ projects.
The survey
Five groups of questions:
Architectural relative questions
Data sources
Smart city management - organization and funding.
Smart city management - critical issues/milestones
Smart city management - project mission and objectives.
Concluding this Section
The most prominent quality drivers that we need to consider are in the order of importance:
Interoperability;
Usability
Authentication and Authorization
Availability
Recoverability;
Maintainability
Confidentiality (should be possible).
The framework
Identify Prominent Architectural drivers and Select architectural patterns per driver
Interoperability – API Façade
Useability – Model View Controller (MVC) pattern
Authentication and Authorization –Integrated application + Layered Architecture pattern
Availability and Recoverability – Messaging Architecture pattern
Maintainability – Layered Architectural pattern
Apply pattern to pationing the system
the application these patterns to partition the system. The result is the conceptual application architecture framework
Assess impact on drivers:
Assess if framework has desired impact on architectural drivers
Interoperability – Web Services (WSDL) interface at the Business Logic layer
Useability – Access from different channels
Authentication and Authorization - Application’s and web server’s provided mechanisms
Availability and Recoverability – smart city integration host
Maintainability – Layered architectural style + use of messaging
Conclusion
Identified important quality properties for smart cities
Proposed conceptual architectural framework
The framework can be used as a starting point but needs tailoring
Created an understanding of the current state in business aspects in relation to the IT support infrastructure for smart cities projects
Reflection
How is this relevant to my project?
The survey uncovered different quality drives for the smart city architecture that might be relevant to my choosing of architecture.
The survey also revealed preferences on architecture patterns per drive, which again gave me a clue to which I might select.
A Framework for a Smart City Design: Digital Transformation in the Helsinki Smart City
Introduction:
Smart city initiativ goals:
Streamlining processes
Make city services more accessible
Enhance resource management efficiency
Reduce the cost of city services
Improve the return on investments
Accelerate economic growth, competitiveness and transparency
Foster knowledge creating
Enhance social inclusion
Prevent inequality among citizens
Objective of this paper:
Shed light on the elements relevant for robust digital transformation, ecosystem creation and orchestration in a smart city
Digital transformation:
Perceived as a paradigm shift
Technology-induces change
Ubiquitous impacts on organizations and industry functions
Strategic focus needed for long-term digital transformation
Specific digital strategy for organizations
Digital strategy a holistic view
Framework for a smart city design:
Strategy - capabilities:
Strategy for a digital or smart city
Identifies changes national and global
Considers the impact of social and technological changes
Envisions future state of city
Sets guidelines on how a city must develop
Integrates digital technologies to infrastructure
Climate change and emission
Considered the goals, resources and capabilities
Capabilities
Technical and human
Technology - Digital technologies and data
Digital technologies
Emerging digital technologies rapidly expanding
Rapidly increasing online city services
ICT connected city infrastructures
Fast adoption of Internet-connected technologies
Cloud computing
Data
Open city data
Capability to process and analyze city data needed
Technology - experimentations in smart cities
Technology experimentations in smart cities
Cities must develop and apply urban experimentation policies
City-level experiments enable iterative technology and service deployment
Ten relevant dimensions for robust smart city technology experimentation platforms:
Openness
Real-world experiments
User/public involvement
Vertical and horizontal scope
Scalability
Sustainable value creation
Continuity
IoT/data heterogeneity
System Architecture design
Technology - security and privacy - horizontal and vertical scope
Security and privacy
Increase in the potential for security and privacy vulnerabilities
Security and privacy important
Vertical and horizontal scope
Vertical scope - specialization
Horizontal scope - variety
Focusing on verticals may lead to data silos
Enabling ubiquitous access to heterogeneous and interconnected city data
Governance
Smart city governance
A body that envisions the future state of the smart city
Provides strategic leadership and resources
Ensures dialog and decision making in smart city ecosystems
Assesses the performance of a smart city and the quality of its citizens’s lives
Consider long term financial needs
Funding
Major investments needed
EU allocation funding to improve infrastructure
Metrics
How to evaluate the performance of a smart city
Guidelines and key performance indicators are developed
Smart city standards provide step-by-step guides to transit a city towards being a digitized smart city
Stakeholders
Smart city stakeholders
Collaborative innovation ecosystems
Enterprises using cities to experiment
Public-private dominated
Integrating citizens and civil society
Quadruple helix
Stakeholder value
Smart city projects tend to decline after funding is obtained
Smart city development, a long-term process, require added value for stakeholders
Methodology
Foundation for the framework presented by Hämäläinen & Tyrväinen(2018)
Data obtained by interviewing persons and stakeholders involved
Additional data collected from workshops and seminars, as well as reviewing official Helsinki data
Helsinki:
The most functional city in the world
Strategy of the Helsinki Smart City
Smart Kalasatama (neighbourhood in Helsinki)
Facilitated by Forum Virium Helsinki (FHV)
Helsinki commits to take concrete actions to produce high quality city services with strong citizen inclusion
Trust safety and social coherence
Helsinki aims to improve its personnel’s capabilities in emerging digital technologies like AI Robotics and Smart Education
Technology:
Data
The city actively experiments and benefits from data analytics, AI sensor and IoT technologies in multiple city domains
Key Issues
The content of data
How information is distributed to relevant target groups
How information is utilized in decision-making processes
The concept of open data was introduced to the Helsinki administration in 2009
Technology experimentations
Forum Virium Helsinki (FVH)
Helsinki will be an attractive and leading city for agile smart city technology experimentations, thereby stimulation new business activities in the city
2015-2018, FVH har organized 21 agile smart city technology and service experimentations in Kalasatama
Smart-mobility services
Effective waste management
Food waste reduction
Fail fast, learn fast
Vertical and horizontal scope
Mobility-as-a-service is one of the most extensive efforts that have taken place in Helsinki
Whim
Common mobility app for city bus, taxi, car and bike share
Security and privacy
New ICT training programs must focus on smart city development by enhancing security and privacy issues in diverse city domains
City lawyers are used to consulting diverse city organizations
Governance
General
The notion of a smart city is related too the manner in which cities govern their ICT systems and data and how they integrate new digital technologies into city infrastructure.
There is no organization that governs the development of the Helsinki smart city and the related initiatives.
Development should progress from agile pilots to a more mature smart city governance approach
Funding and metrics
Smart Kalasatama is funded by Helsinki
The agile smart city pilots and experimentations are funded by diverse EU funds
In addition, local and national public organizations have participated and invested in the Helsinki smart city pilots
None of the international smart city standards are applied in Helsinki
Estimated that open procurement data has resulted in 1-2% savings in city procurement activities
Stakeholders
Stakeholder value
FVH’s role is to be facilitator during agile pilots and the goal is to start agile pilots that must create value for the stakeholders of a smart city
Stakeholders experiences contributes to the success of agile pilots and willingness to participate in pilot activities
Stakeholders enjoy the increased transparency that Helsinki open data gives, and benefits of internal saving and resource efficiency
The open data hopes to stimulate new businesses and improve the competitiveness of companies.
Summary
The smart city design framework considered Helsinki smart city through four dimensions:
Strategy
Emphasizes digitalization, user-centric development, civic society engagement and agile technology pilots.
Technology
Manages to create a specific experimentation culture for novel digital technologies like IoT solutions and data usage within diverse city organizations
Governance
Stakeholders
The development of a smart city in Helsinki is rather scattered, which makes the governance of the smart city confusing
Numerous Helsinki smart city initiatives are funded through diverse EU funds, Helsinki city and private organizations.
Reflection
Big data driven multi-tier architecture for electric mobility as a service in smart cities
Introduction
eMaaS
Electric Mobility as a Service
There is an increase in urban residents causing need for better transportation
eMaas will mitigate(lessen) environmental issues
Results in noise reduction
Interoperability
Wish to connect current applications into one.
Wish for standardized approach to accessing data
Big Data
Data collected from:
Citizens
Sensors
companies
API
Today there are very many different formats, which results in little interoperability
There is a need for one common approach
Multi layer Architecture
A combination of the following:
STS - Socio-technical systems
Technical and social aspects must work together
Organizational
Social
Technical
Human factors
ANT - Actor network theory
Examine the relationship between human and non-human
Actor
Links
Network
Action
Layer architecture of digital technology
Content
Services
Networks
Devices
Combined - Multi-tier architecture
Context
Service
Business
Application and data processing
Data space
Technologies
Physical infrastructure
Proposed architecture - layers
Context
Main feature → eMaaS
Stakeholdes
Main target → Ease of transport + Reduce polution
Service
All individual services that ensure mobility
Provide communication module between
municipalities
transport companies
other parties offering mobility services
Aids trusted third-party mobility data providers
Routing location
Weather services
Business
Strategies by each enterprise to reach goals of sustainability
Create Virtual Enterprise to align with businesses' approach to provide a medium for IT.
Application and data processing
Software and APIs used to provide eMaaS services
Integrates APIs to process, provide and manage eMaaS data
Should provide trusted software to citizens to push and pull transport information
Layer facilitates developers with existing API to use in new eMaaS applications.
Data space
Comprises of all types and sources of data
Deploys variety of datamanipulation, storage and management to maintain data continuum
Contains both SQL and non-SQL databases
Historical mobility data
Technologies
Software and hardware infrastructure
Process data from physical devices
Describes how data layers are linked
Physical infrastructures
Physical devices
Collects data
Data transferred to technology layer
Research methodology
Methodology
Identify problem and motivate
Define objectives of a solution
Design and development
Demonstration
Evaluation
Communication
The interview:
Steps:
Thematization the Interview
Design the interview
Conducting the interview
Transcript of recording
Analysing of transcript
Verifying follow up with informants
Prepare interview data
Findings
Conclusion
Multi-tier architecture is applicable
APIs foster innovation and ease of use
Enables new opportunities
Potential improvements
Reflection
How is this applicable to my task?
It isn’t, and I have no idea how to reflect over this papers finding
The City as a Service Platform: Typology of City Platform Roles in Mobile Service Provision
Introduction
Explores roles of city governments in the platformization of the mobile services industry.
Takes an experimental approach that applies existing typology for mobile service platforms to the context of the city. Forms a starting point for research into platform strategies in the context of the city
Four city platform types
Enabler
Integrator
Neutral
Broker
1. Goal
Verify whether a general typology or mobile service platforms would hold up to scrutiny in the more applied and specific context of the city and to what extent city agencies could take up platform roles within the value network
2. Framework
Four types of mobile services
Enabler
Platform owner controls many or most of the assets involved in mobile service provision, but leaves the customer relationship to third-party developers
Integrator
Closed approach where the platform owner controls service development and distribution
Neutral
platform owner does not control most of the assets necessary for the value proposition and on top of this does not have customer ownership.
Broker
The platform relies on other actors that control most of the assets for establishing the value proposition, but does integrate customer ownership.
Table:
Mobile service platform typology
3. City platform typology
Smart city definition:
“We believe a city to be smart when investments in human and social capital and traditional (transport) and modert (ICT) communication infrastructure fuels sustainable economic growth and high quality of life, with a wise management of natural resources, through participatory governance.”
Four types of city platforms:
Enabler
City providing open data and statistical information to interested developers.
Integrator
Closed approach where the city controls service development and distribution.
Neutral
City not taking any initiative to deploy mobile services: leaving the initiative to private projects
Broker
City operates a platform that hosts personal information on its users, but is nott involved in developing the services benefiting from it.
Conclusion
We can clearly define platform roles that are being taken up by the cities in different parts of the world
The different roles have their own merits and consequences that would need to be explored in greater detail in future research
The applied topology can be used as an initial tool for city governments to consider their own role within the value network and the potential platform dynamics at play when they are involved in mobile service provision
Future research will establish whether there are crucial gatekeeping platform roles at play in the creation and provision of mobile services in the context of the city
A clearer definition of what control over assets and customers eans in the city context required
Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities
Introduction
Connecting everyday objects via existing network highly favorable
Smart city is an application of the IoT
UN projected 66% of the world population to be urban by 2050
75% of total energy is consumed by cities
This consumption generate 80% of greenhouse gases
Smart city is the ideal solution to urbanization challenges
Features of a smart city
Smart city comprises of:
Attributes - characteristics of a smart city
Themes - the pillars of the smart city
Infrastructure - provides the operational platform
Characteristics of a smart city
Four main attributes
Sustainability
Quality of Life
Urbanization
Smartness
Pillars of a smart city
Institutional infrastructure
Governance
Technocratic governance
Physical infrastructure
Natural resources
Manufactured infrastructure
Social infrastructure
Intellectual capital
Human capital
QoL
Economic infrastructure
Smart economy
Smart city architecture
Sensing layer
Data collection
Collecting data concidered the most important role but also the most challenging task
Captures all types of data from all types of sensors and devices
Transmission layer
Carries data to upper layers
Acts as the backbone of ant smart city architecture
Convergence of various communication networks
Consists of various types of wired, wireless, and satellite technologies
Data management layer
The brain of any smart city
Performs a variety of data manipulating, organizing, analyzing, storing, and decision-making tasks.
Maintains vitality of data
Derived decisions are conveyed to the application layer to execute
Application layer
Mediates between urban citizens and data management layer
Key services:
Community development
Grid distribution
Smart transportation
Weather forecasting
Information sharing between applications a promising approach
Composition of smart city
Varies between cities and often synergies between areas
Smart community
Aspire to uplift citizen satisfaction and well-being
Smart buildings, water and waste management
Smart building connected with smart grid
Green buildings and smart waste
Smart hospitality
Smart Transportation
Already seeing this transformation
Will be safer for both passengers and riders
Congestion control
Synergies with other smart applications
No need to own car
Other types, such as drones
Smart healthcare
Combined with transportation, analytics of blood samples
Highly empirical field, better diagnosis
Remote surgery
Automatic surgery
Smart energy and interconnectivity
Smart green energy production
Predicting future demand
Enormously important to have all interfaces talking with eachother
Smart cities in the world
City In Motion Index (CIMI), 77 indications, 10 categories. 181 cities with NYC London and Paris at the top. Very unevenly spread.
London
One million population growth over the last decade
London data store, first open access to public data
Congestion control, smart money, smart waste management
Data collection, management and application layer
Smart energy and smart healthcare
San Francisco
Greenest city in North America
Smart buildings, extended transportation system, centralized waste
Innovative waste management main application (zero landfill waste by 2020)
Autonomous, shared electric vehicles
Extensive data collection, with connected transport, pedestrian crossing and traffic signals.
Santander City, Spain
Part of SmartSantander project
Extremely large framework for research expreiments on smart cities and IoT. Largest testbed for IoT applications
The city employs more than 12,500 sensors
Available parking, remaining volume of trash, pedestrian crossing, and more
Has separate device layer compared to other architectures
Challenges and opportunities
Design and maintenance cost
Isolated data - how should it be accessed, processed and personal lives protected?
Fault tolerance
Opportunities because of Green City
Advances of big data processing
Reflection
How is this relevant to my task?
The paper talks about E-commerce and e-business
Smart building could be a part of my application, providing smart solutions to their housing. This way they can control their housing from afar, like heating up before guests arrive.
This is also part of the smart hospitality component, but this is not mentioned in the article.
Standardization of enterprise architecture development for smart cities
Introduction
What are smart cities:
Smart cities are innovative cities which use ICT to facilitate daily activities of the citizens to improve their QoL.
Smart cities are dealing with complexity of ICT services
Many EA frameworks introduced to manage complex information systems, processes and infrastructures
EA framework for smart cities should consider specific concerns for stakeholders and improve their QoL.
This framework contains two new layers
Context layer:
To capture the smart city context information about strategies, priorities, and other critical aspects (e.g. stakeholders and their concerns) to deliver effective services to the citizens.
Service layer:
To define appropriate goals, scope, etc. for the services with regard to the smart city
requirements, concerns, and priorities.
Glossary
ADM (Architectural Development Method) – A process for creating an enterprise architecture that is part of the TOGAF standard.
application architecture – The architecture of a specific application.
architect – One whose responsibility is the design of an architecture and the creation of an architectural description.
architecture – The fundamental organization of a system embodied in its components, their relationships to each other, and to the environment, and the principles guiding its design and evolution (from IEEE-1471-2000).
architectural artifact – A specific document, report, analysis, model, or other tangible that contributes to an architectural description.
architectural description – A collection of products (artifacts) to document an architecture.
architectural taxonomy – A methodology for organizing and categorizing architectural artifacts.
architectural framework – A skeletal structure that defines suggested architectural artifacts, describes how those artifacts are related to each other, and provides generic definitions for what those artifacts might look like.
architectural process – A defined series of actions directed to the goal of producing either an architecture or an architectural description.
architectural methodology – A generic term that can describe any structured approach to solving some or all of the problems related to architecture.
business architecture – An architecture that deals specifically with business processes and business flow.
business reference model (BRM) – A FEA term that gives a business view of the various functions of the federal government.
business services segment – A FEA term that refers to a segment that is foundational to most, if not all, political organizations, such as financial management.
capability – A collection of business functionality that works together to deliver some specific business value.
capability centric approach – An approach to enterprise architecture that focuses on analyzing the capabilities of an organization with the goal of maximizing the value that each returns.
CIO — Chief Information Officer, the executive in charge of information technology in a corporation.
CIO Council – A council consisting of CIO’s from each of the federal governmental agencies that coordinates work related to common interests.
Clinger/Cohen Act — See Information Technology Management Reform Act.
common object request broker architecture (CORBA) – A component-oriented system designed and evangelized by the OMG.
common systems architectures – A TOGAF term referring to an architecture that are common to many, but not all types of enterprises. In contrast to foundation architectures and industry architectures.
component reference model (CRM) – A FEA term that gives an IT view of systems that support business functionality.
data architecture – The architecture of the data (typically stored in databases) owned by the enterprise.
DCOM - A component-oriented system designed and evangelized by Microsoft.
enterprise architect – An architect who is who specializes in enterprise architectures.
enterprise architecture — An architecture in which the system in question is the whole enterprise, especially the business processes, technologies, and information systems of the enterprise.
enterprise service – A FEA term referring to a well-defined function that spans political boundaries, such as security management.
FEA – See Federal Enterprise Architecture.
FEAF — See Federal Enterprise Architectural Framework.
FEAPMO – The organization within the OMB that owns and administers the Federal Enterprise Architecture.
Federal Enterprise Architecture – An architectural description of the enterprise architecture of the U.S. federal government that includes various reference models, processes for creating organizational architectures that fit in with the federal enterprise architecture, and a methodology for measuring the success of an organization in using enterprise architectures.
Federal Enterprise Architectural Framework (FEAF) — An enterprise architectural framework used by the United States federal government to describe how the various governmental agencies and their IT systems are related to each other.
Federal Architecture Program EA Assessment Framework – A benchmark used by the OMB to measure the effectiveness of governmental bodies in using enterprise architecture.
foundation architecture – A term used by TOGAF to refer to the most generic of architectures that can be used by any IT organization. In contrast to common systems architectures.
GAO — See General Accountability Office.
Gartner – An IT research and advisory organization.
gateway — A transfer point of an autonomous system from which messages from the outside world are received or through which messages to the outside world are sent.
General Accountability Office — A branch of the United States Government that is responsible for monitoring the effectiveness of different organizations within the U.S. Government.
industry architecture – A TOGAF term that refers to a architecture that is common to most enterprises within an industry. In contrast to a common systems architecture and an organizational architecture.
Information Technology Management Reform Act — An act passed by the United States Congress in 1996 that requires all governmental organizations to use effective strategies and frameworks for developing and maintaining IT resources.
OMB (Office of Management and Budget) – Part of the Executive Office of the President of the United States which serves the function of presidential oversight on federal agencies.
organizational architecture – A TOGAF term that applies to an architecture that is specific to a particular organization. In contrast to an industry architecture.
performance reference model (PRM) – A FEA term that gives standard ways of describing terms related to measuring value.
perspective centric approach – An approach to enterprise architecture that focuses on aligning different organizational perspectives.
process centric approach – An approach to enterprise architecture that focuses on creating a well defined process for modeling the enterprise.
Return on Investment — A measure (in percent) of the business value of a project based on the increase in profit (either because of increased income or decreased expenses) divided by the cost of the project. For example, a project with a cost of $100,000 that returned $200,000 in increased profit has an ROI of 200%
segment – A FEA term that refers to a major line-of-business functionality, such as human resources, that might be shared across organizations.
standardization centric approach – An approach to enterprise architecture that focuses on defining standard ways of describing different processes, reference models, and common services.
standards information base (SIB) – A TOGAF term which refers to a collection of information about standards, particularly in the area of open source.
TAFIM (Technical Architecture Framework for Information Management) — An architectural framework developed by the Department of Defense and officially discontinued in 2000.
technical architecture – Usually refers to the architecture of the technical infrastructure within which applications run and interact.
technical reference model (TRM) – Part of TOGAF, a reference model that gives a common language for various pieces of IT architecture. This term is also used for a similar meaning within FEA.
TOGAF (The Open Group Architectural Framework) 8.1 — An architectural methodology that is controlled by The Open Group.
Zachman Framework for Enterprise ArchitecturesTM — An architectural framework in which an enterprise is modeled as thirty or thirty-six cells, each of which represents an intersection between a stakeholder perspective and an abstraction.