Knowledge Base

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The Big Policy Canvas Knowledge Base is a state-of-the-art, online and dynamic repository that functions as an accumulator uniting all the knowledge produced during the project. It is structured along the three dimensions of needs, trends and assets and furthermore offers a mapping among them by defining how they are interconnected and how they influence each other.

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Public sector has to deliver services to citizens despite resource constraints and budgetary pressures. Because of this personnel and financial limitations, available resources must be used as cost saving and valuable as possible.

In: Needs

Standards require a certain legal basis and binding specifications. At the same time, they must be also accepted by the target group. If standards are enforced, they offer the advantage of planning and investment protection. This provides a good basis for further digitisation of processes.[1]

In: Needs

In 2001, the industry analyst Douglas Laney at Gartner described data management challenges along the three dimensions volumes, velocity and variety in the E-commerce branch. Volumes stands for the quite huge increase of volumes of data, Velocity for increased point-of-interaction speed and the pace of data generated by interactions and used to support interactions. Data Variety means variety of incompatible data formats, non-aligned data structures and inconsistent data semantics. This 3-V-model has been widely used attempting to define big data since this publication in 2001. [1]

In: Trends

Artificial Intelligence (AI) is a possibility to improve policy and decision making and can be understood as the automation of intelligent and human-like behaviour. The most important techniques to support specific cases of indeed high complex policy making processes are decision support and optimisation techniques, game theory, data and opinion mining, agent-based simulation and visual scenario based evaluations. [1]

In: Trends

By now, the most promising application of artificial intelligence is the use of machine learning as a subfield of AI. The Encyclopaedia Britannica states that machine learning is concerned with the implementation of computer software that can learn autonomously. [1]

In: Trends

Gartner assumes that by 2020 modern BI and analytics platform components will deliver smart, governed, search- and visual-based data discovery capabilities. Natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms and organisations that offer users access to a curated catalogue of internal and external data will realise twice the business value from analytics investments than those that do not. Gartner outlined fifteen critical capabilities by a BI and Analytics Platform [1]:

In: Trends

Predictive analytics brings together advanced analytics capabilities. It extracts information from existing data sets in order to determine patterns and predict future impacts and trends. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessments.[1]
Data analytics encompasses techniques such as regression analysis, pattern matching, forecasting, multivariate statistics, predictive modelling and forecasting. [2]

In: Trends

Cloud Computing is a model that enables ubiquitous access to a shared pool of configurable technological assets available on-demand in a virtualised environment. Cloud services are remotely managed by cloud service providers and can be rapidly provisioned and released with minimal effort or service provider interaction. It can potentially achieve coherence and economies of scale.
The cloud model encompasses the four deployment models Public, Private, Hybrid and Community and the following three delivery models [1]:
Software as a Service

In: Trends

There are various definitions of the Internet of Things (IoT). The Internet Engineering Task Force says Internet of Things’ basic idea is to connect electronical and non-electronical objects to provide seamless communication and contextual services by them through e.g. RFID tags, sensors, actuators or mobile phone. The latter is related to the term “things”. The term “Internet” considers the TCP/IP suite and non-TCP/IP suite at the same time. [1]

In: Trends

The pressure to redesign city infrastructures is strong, since climate change and the problem of allocation are defining new requirements, which will not be met through cosmetic and maintenance repairs. In particular, energy infrastructures like water, waste or recycling are affected by this issue.
The following description draws a picture for future smart cities.

In: Trends

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