Open-source

CityPulse 3D Map:

The CityPulse 3D Map provides a 3D visualization of cities and different geo-located events provided by the CityPulse platform.

CityPulse Tourism Planner:

This scenario combines sources of data related to events and points of interest (PoIs) in the city of Stockholm and generates a schedule to explore the PoIs that the users select. The schedule is created based on the opening times of each PoI as well as the user’s budget, travel period, and type of transport.

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CityPulse Pickup Planner:

The Pickup Planner scenario aims to provide a travel service for users located around Stockholm. Users specify pickup location, destination, arrival time constraints and preferences in travel requests, from which the system devises a pickup path to be used by vehicle(s) in delivering users to their intended destinations.

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CityPulse Dynamic Bus Scheduler:

This scenario introduces a reasoning mechanism capable of evaluating travel requests and generating bus timetables with reduced average waiting time for passengers. Furthermore, the system has the potential to detect traffic flow and make adjustments to the regular path of each bus, so as to decrease the waiting time which is a result of traffic congestion.

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CityPulse Traffic Planner:

The Traffic Planner is an Android application for citizens that can be used for obtaining user-oriented travel and parking recommendations.

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CityPulse Brasov Bus:

The CityPulse Brasov Bus is an Android application that allows citizens and visitors in the city of Brasov to search for bus routes between two points. The app suggests routes and monitors the user’s location. Users can also report traffic incidents in their vicinity, which will are forwarded to other users on bus routes affected by the incident.

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CityPulse City Dashboard:

The CityPulse City Dashboard supports visual analytics for the different relevant datasets registered in the CityPulse platform.

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Semantic validator:

This web application allows ontology developers to validate their data that are based on the SSN ontology. Users can input their data either by uploading a file or directly by inputting the data in the textbox. The application also provides a tag cloud that illustrates the popularity of the terms that are used by all the data that have been submitted for validation.

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Data Quality Explorer:

The Data Explorer gives a graphical user interface to monitor the quality and the trust of the data sources in real-time.

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Atomic Data Quality Monitoring/ Composite Data Quality Monitoring:

This component shows the quality of the data sources that are used in the CityPulse platform – e.g. the correctness of data and if some information is missing. If a city has no exactly planned infrastructure it is a complex process to determine, which sensor measurements are correct in case of contradictory information. This component predicts the plausibility of events by pursuing a monitoring approach that analyzes sensor values of related sensors of different kinds.

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Social Media Analyzer:

The Social Media Analyzer is a component where the user can visualize events extracted from social media streams such as Twitter.

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Event Detector:

The Event Detector component in the CityPulse framework provides a generic tool for processing the annotated as well as aggregated data streams to detect events occurring in the city. This component is highly flexible in deploying new event detection mechanisms, since different smart city applications require different events to be detected from the same data sources.

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Knowledge Acquisition Toolkit:

The Knowledge Acquisition Tool (KAT) is a software component that implements state-of-the-art machine learning and data analytic methods for sensors data. The algorithms and methods implemented in KAT are used for processing and analysing the smart city data in the CityPulse project.

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IoT Framework:

A mechanism for converting data points stored in the IoT Framework into semantically annotated data. This can be used for searching and accessing raw sensory data in a smart city data analytics framework.

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CityPulse Geospatial Data Infrastructure:

The Geospatial Data Infrastructure (GDI) component is used by a number of other CityPulse components to tackle geo-spatial tasks.

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Stream Discovery and Integration Middleware:

This component is responsible for discovering the most appropriate IoT sources for the application and integrates data from multiple heterogeneous IoT data sources.

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Event Testing:

An Android application for reporting events and an application for web browsers to show and inspect events generated by the application and the Event Detector.

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Fault Recovery:

The Fault Recovery component ensures the continuous and proper operation of the CityPulse enabled application by generating estimated values for the data stream when the quality drops or it has temporally missing observations. When the quality of the data stream is low for a longer time period, an alternative data source has to be selected.

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Decision Support and Contextual Filtering:

This component utilises contextual information to provide optimal solutions of smart city applications. The component continuously identifies and filters critical events that might affect the optimal result of the decision making task.

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Resource Manager:

The Resource Manager component is responsible for managing all smart city data sources. Each source is described in a Data Wrapper. A Data Wrapper is a software bundle that connects to the remote data sources in order to fetch and interpret new sensor observations. During runtime an application developer or the CityPulse framework operator can deploy and manage new Data Wrappers to include data from new data streams.

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Stream Processing Benchmark:

This benchmark has been designed using CityPulse reference datasets and it provides a configurable benchmark to evaluate RDF Stream Processing engines using smart city applications.

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Data Stream Generator:

This is a tool for generating data streams. A data stream is a sequence of measurements from a sensor or other sources, over time (for example temperature data, a car’s tire pressure, level of fuel on an aircraft, etc.). It is meant to be used as a tool for testing the performance of systems designed to gather and process large amounts of data from different sources (e.g. IoT middleware). The CityPulse framework uses this component to run some of the demonstrators.