Rules Interface Mockup

These slides are a walkthrough of a design for a domain expert who is a non-ontology or ontology-N3 rules expert user to generate rules for querying an ontology space

DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness

In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data and each channel can be correlated to another one, such as x, y, z axis of accelerometer. We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis and then feeding the data into a LSTM-based denoising autoencoder which can reconstruct missingness along the time axis. We experiment the model on the extreme missingness scenario ($>50\%$ missing rate) which has not been widely tested in wearable data. Our experiments on activity recognition show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.Comment: 5 pages, 2 figures, accepted in ICASSP'202

INTERACTIVE VISUALIZATION TECHNIQUES FOR SEARCHING TEMPORAL CATEGORICAL DATA

Temporal data has always captured people’s imagination. Large databases of temporal data contain temporal patterns that can lead to the discovery of important cause-and-effect phenomena. Since discovering these patterns is a difficult task, there is a great opportunity to improve support for searching. Temporal analysis of, for example, medical records, web server logs, legal, academic, or criminal records can benefit from more effective search strategies. This dissertation describes several interactive visualization techniques designed to enhance analysts ’ experience in performing search, exploration, and summarization of multiple sets of temporal categorical data. These techniques are implemented in the software Lifelines

Ontology Performance Profiling and Model Examination: First Steps

Abstract. “[Reasoner] performance can be scary, so much so, that we cannot deploy the technology in our products. ” – Michael Shepard 3. What are typical OWL users to do when their favorite reasoner never seems to return? In this paper, we present our first steps considering this problem. We describe the challenges and our approach, and present a prototype tool to help users identify reasoner performance bottlenecks with respect to their ontologies. We then describe 4 case studies on synthetic and real-world ontologies. While the anecdotal evidence suggests that the service can be useful for both ontology developers and reasoner implementors, much more is desired.