Framework for Querying and Analysis of Evolving Graphs

The average person spends several hours a day behind the wheel of their vehicles, which are usually equipped with on-board computers capable of collecting real-time data concerning driving behavior. However, this data source has rarely been tapped for healthcare and behavioral research purposes. This MS thesis is done in the context of the Diagnostic Driving project, an NSF funded collaborative project between Drexel, Children Hospital of Philadelphia (CHOP) and the University of Central Florida that aims at studying the possibility of using driving behavior data to diagnose medical conditions. Specifically, this paper introduces focuses on the classification of driving behavior data collected in a driving simulator using deep neural networks. The target classification task is to differentiate novice versus expert drivers. The paper presents a comparative study on using different variants of LSTM (Long-Short Term Memory networks) and Auto-encoder networks to deal with the fact that we have a small amount of labels (16 examples of people driving in the simulator, each labeled with an 'expert' or 'inexpert' label), but each simulator drive is high dimensional and too densely sampled (each drive consists of 100 variables sampled at 60Hz). Our results show that using an intermediate number of neurons in the LSTM networks and using data filtering (only considering one out of each 10 samples) obtains better results, and that using Auto-encoders works worse than using manual feature selection.Ph.D., Information Studies -- Drexel University, 201

Transformer meets wcDTW to improve real-time battery bids: A new approach to scenario selection

Stochastic battery bidding in real-time energy markets is a nuanced process, with its efficacy depending on the accuracy of forecasts and the representative scenarios chosen for optimization. In this paper, we introduce a pioneering methodology that amalgamates Transformer-based forecasting with weighted constrained Dynamic Time Warping (wcDTW) to refine scenario selection. Our approach harnesses the predictive capabilities of Transformers to foresee Energy prices, while wcDTW ensures the selection of pertinent historical scenarios by maintaining the coherence between multiple uncertain products. Through extensive simulations in the PJM market for July 2023, our method exhibited a 10% increase in revenue compared to the conventional method, highlighting its potential to revolutionize battery bidding strategies in real-time markets

Introducing Access Control in Webdamlog

We survey recent work on the specification of an access control mechanism in a collaborative environment. The work is presented in the context of the WebdamLog language, an extension of datalog to a distributed context. We discuss a fine-grained access control mechanism for intentional data based on provenance as well as a control mechanism for delegation, i.e., for deploying rules at remote peers.Comment: Proceedings of the 14th International Symposium on Database Programming Languages (DBPL 2013), August 30, 2013, Riva del Garda, Trento, Ital

Introducing Access Control in Webdamlog

International audienceWe survey recent work on the specification of an access control mechanism in a collaborative environment. The work is presented in the context of the WebdamLog language, an extension of datalog to a distributed context. We discuss a fine-grained access control mechanism for intentional data based on provenance as well as a control mechanism for delegation, i.e., for deploying rules at remote peers