Toolkit (KAT) is an open-source software that includes methods to process numerical sensory
data. KAT is able to extract and
represent human understandable and/or machine interpretable information
from raw data.
KAT includes a collection of algorithms for each step of the Internet of Things (IoT) data processing workflow ranging from data and signal pre-processing algorithms such as Frequency Filters, dimensionality reduction techniques such as Wavelet, FFT, SAX, and Feature Extraction and Abstraction and Inference methods such as Clustering. Figure 1 shows the steps of the process chain for processing cyber-physical data on the Web. KAT can be used to design and evaluate algorithms for sensor data that aim to extract and find new insights from the data.
Figure 1: The process chain for the physical world data on the Web
(Source: P. Barnaghi, A. Sheth, C. Henson, "From data to actionable knowledge: Big data challenges in the web of things," IEEE Intelligent Systems, volume:28 , issue: 6, Nov/Dec 2013).
The current version supports:
How to use KAT?Please visit the Documentation page for more information on how to install, use and extend KAT.
Click here to download the overview slides.
A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things", IEEE Internet of Things Journal, 2015.
 Payam Barnaghi, Maria Bermudez-Edo, Ralf Toenjes, "Challenges for Quality of Data in Smart Cities", ACM Journal of Data and Information Quality, to appear, 2015.
 Frieder Ganz, Payam Barnaghi, Francois Carrez, "Automated Semantic Knowledge Acquisition from Sensor Data", IEEE Systems Journal, 2014.
 Frieder Ganz, Payam Barnaghi, Francois Carrez, "Information Abstraction for Heterogeneous Real World Internet Data" ,IEEE Sensors Journal, vol. 13, no. 10, pp. 3793-3805, 2013.
Frieder Ganz. For more informaiton about KAT, contact us at firstname.lastname@example.org.
Institute for Communication Systems (ICS)
University of Surrey