Abstract
Nowadays, data are being produced like never before because the use of the Internet of Things, social networks, and communication in general are increasing exponentially. Many of these data, especially those from public administrations, are freely offered using the open data concept where data are published to improve their reutilisation and transparency. Initially, the data involved information that is not updated continuously such as budgets, tourist information, office information, pharmacy information, etc. This kind of information does not change during large periods of time, such as days, weeks or months. However, when open data are produced near to real-time such as air quality sensors or people counters, suitable methodologies and tools are lacking to identify, consume, and analyse them. This work presents a methodology to tackle the analysis of open data sources using Model-Driven Development (MDD) and Complex Event Processing (CEP), which help users to raise the abstraction level utilised to manage and analyse open data sources. That means that users can manage heterogeneous and complex technology by using domain concepts defined by a model that could be used to generate specific code. Thus, this methodology is supported by a domain-specific language (DSL) called OpenData2CEP, which includes a metamodel, a graphical concrete syntax, and a model-to-text transformation to specific platforms, such as complex event processing engines. Finally, the methodology and the DSL have been applied to two near real-time contexts: the analysis of air quality for citizens’ proposals and the analysis of earthquake data.
Highlights
Many companies and public administrations are adopting the open data paradigm in order to offer transparent information such as contracts, budgets, resources, and so on [1]
The main benefits of open data [1] include (i) political and social benefits, such as more transparency, democratic accountability, more participation and self-empowerment of citizens, public engagement or scrutinisation of data; (ii) economic benefits, such as economic growth and stimulation of competitiveness, stimulation of innovation, development of new products and services, creation of a new sector adding value to the economy; and (iii) operational and technical benefits, such as the ability to reuse data, optimisation of administrative processes, fair decision-making by enabling compassion, the creation of new data based on combining data, external quality checks on data, and the ability to merge, integrate and mesh public and private data
This paper shows that model-driven development is a suitable approach to the development of tools to tackle the complexity of heterogeneous technology as occurs in the context of open data sources and complex event processing
Summary
Many companies and public administrations are adopting the open data paradigm in order to offer transparent information such as contracts, budgets, resources, and so on [1]. Data reutilisation and transparency are key elements around which a new economy based on the value of data could be defined [5,6]. The main benefits of open data [1] include (i) political and social benefits, such as more transparency, democratic accountability, more participation and self-empowerment of citizens, public engagement or scrutinisation of data; (ii) economic benefits, such as economic growth and stimulation of competitiveness, stimulation of innovation, development of new products and services, creation of a new sector adding value to the economy; and (iii) operational and technical benefits, such as the ability to reuse data, optimisation of administrative processes, fair decision-making by enabling compassion, the creation of new data based on combining data, external quality checks on data (validation), and the ability to merge, integrate and mesh public and private data. The main consequences for citizens are that using public data could help them to understand and analyse what is happening around them in real-time.
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