Tools and Data Sets

Computer Hope

Demonstration Videos

Data Sets

  • CityPulse Dataset Collection

    This webpage offers a number of semantically annotated datasets collected from partners of the CityPulse EU FP7 project and relevant resources for smart city data. Visitors can use the menu on the left to access these resources

 
  • Quality Explorer

    The Quality Explorer provides machine readable and semantically annotated quality analysis of the online data streams. The quality explorer also provides a visual interface for end-users (e.g. city planners) to browse and see the quality related information for the underlying sensory data collection infrastructure on smart city environments. Various quality assessment, analysis and compensation mechanisms are implemented in this component. (video links: CityPulse QoI Explorer , QoI Explorer Demonstration (Frequency) and QoI Explorer Demonstration (Age) )
     

  • Quality Ontology

    The Quality Ontology is used to represent the quality of information for data streams in smart cities. This quality can than be used to select streams by the quality of the information they provide. The annotated data streams are represented with the Stream Annotation Ontology. To provide information about the provenance of the streams parts of the PROV-O ontology are used.
     

  • Stream Annotation Ontology

    Representing IoT data streams is an important requirement in semantic stream data applications, as well as in knowledge-based environments for Smart Cities. This study aims to semantically represent the features of a data stream defining the specifications of an information model on top of Semantic Sensor Networks (SSN), PROV-O and TimeLine Ontologies, and involves connections with the Complex Event Processing Ontology and Quality Ontology.
     

  • Awesome real-time IoT platform

    arip is an object oriented approach towards exploiting Web sockets for the IoT. arip consists of remote procedure calls, publish/subscribe and discoverability. This means that a client can connect to the arip server, provide a description of it self (ex. what rpc's does it provide), search for other clients and their descriptions, call rpc's, publish content and subscribe to other clients publications.
     

  • Extracting City Traffic Events from Social Streams

    This tool has two major components: (1) Annotator and (2) Extractor. Annotator: Sequence labeling model trained with declarative knowledge from location and event knowledge base is utilized for annotation of raw tweets. Open Street Maps is used as a location based knowledge specific to a city and 511.org schema of events is used as a knowledge of traffic related events. Extractor: Extraction algorithms use space, time and theme characteristic of city events to aggregate all the tags for emitting events. (video link: Twitter Map Demo)
     

  • KAT: Knowledge Acquisition from Sensor Data

    The Knowledge Acquisition Toolkit (KAT) is designed to support the process of knowledge acquisition from numerical sensory data. The goal of this work is to develop a toolkit that is able to extract and represent human understandable and/or machine interpretable information from raw data. The toolkit includes a collection of algorithms on each step of the acquisition workflow ranging from data and signal pre-processing algorithms such as Frequency Filters, dimensionality reduction techniques such as PCA, Wavelets, FFT, ACPA, SAX, and Feature Extraction and Abstraction and Inference methods such as Clustering, Classification and Logical Reasoning. KAT can be used to design and evaluate algorithms for sensor data that aim to extract and find new insights from the data.

  • Linked Sensor Middleware (LSM)

    Sensing devices are becoming the source of a large portion of the Web data. To facilitate the integration of sensed data with data from other sources, both sensor stream sources and data are being enriched with semantic descriptions, creating Linked Stream Data. Despite its enormous potential, little has been done to explore Linked Stream Data. One of the main characteristics of such data is its ``live'' nature, which prohibits existing Linked Data technologies to be applied directly. Moreover, there is currently a lack of tools to facilitate publishing Linked Stream Data and making it available to other applications.

  • IoT-Framework

    The IoT-framework is a computational engine for quantitative information collected by sensors. More information on this piece of software can be found here: https://labs.ericsson.com/blog/iot-framework. The system is open source, available under Apache 2.0 license.

  • ODAA - Open Data Aarhus

    The overall objective of  www.odaa.dk  is to make data openly and freely available to support productivity and innovation in the development of greater use of data. Developers, entrepreneurs, companies, institutions, citizens and others will be able to easily access the open data and turn them into new services / applications.

  • ckanext-realtime

    ckanext-realtime is the latest contributions to Free Software from Gatesense team. It is an extension for CKAN open data platform which enables app developers to make realtime applications with CKAN. More specifically, you can subscribe to resources (datastores) and thus monitor their changes in realtime. gatesense.com is a realtime-enabled CKAN portal, among other things. This means that you can build realtime apps with datasets found at http://gatesense.com/data/dataset .A tutorial on how to use ckanext-realtime can be found here: http://alexandrainst.github.io/ckanext-realtime/tutorial.html