Radwa Elshawi

Prior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality). We use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used. Two set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling. The results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.

Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data. The intersection of these trends is what is called, nowadays, as Big Data Science. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting big data science as a commodity service for data scientists. We provide various insights about the latest ongoing developments and open challenges in this domain.

A graph is a popular data model that has become pervasively used for modeling structural relationships between objects. In practice, in many real-world graphs, the graph vertices and edges need to be associated with descriptive attributes. Such type of graphs are referred to as attributed graphs. G-SPARQL has been proposed as an expressive language, with a centralized execution engine, for querying attributed graphs. G-SPARQL supports various types of graph querying operations including reachability, pattern matching and shortest path where any G-SPARQL query may include value-based predicates on the descriptive information (attributes) of the graph edges/vertices in addition to the structural predicates. In general, a main limitation of centralized systems is that their vertical scalability is always restricted by the physical limits of computer systems. This article describes the design, implementation in addition to the performance evaluation of DG-SPARQL, a distributed, hybrid and adaptive parallel execution engine of G-SPARQL queries. In this engine, the topology of the graph is distributed over the main memory of the underlying nodes while the graph data are maintained in a relational store which is replicated on the disk of each of the underlying nodes. DG-SPARQL evaluates parts of the query plan via SQL queries which are pushed to the underlying relational stores while other parts of the query plan, as necessary, are evaluated via indexless memory-based graph traversal algorithms. Our experimental evaluation shows the efficiency and the scalability of DG-SPARQL on querying massive attributed graph datasets in addition to its ability to outperform the performance of Apache Giraph, a popular distributed graph processing system, by orders of magnitudes.

Computer Systems provide the foundation upon which all other software applications rely. The goal of systems research is to develop the key abstractions and services that enable software to be efficiently and portably run on hardware, as well as to elicit the principles underlying the development and use of complex, parallel and distributed software/hardware systems. In this article, we study the community of the computer systems research community by analyzing the research publications and the program committee memberships of three top-tier and prestigious computer systems conferences (EuroSys, SOSP and OSDI) over the period between 2006 and 2014. We study the growth in the size of research publications, research community and the program committees of the three studied conferences over the period between 2006 and 2014. We highlight the main scholars participating in the program committees of these venues. We also analyze the affiliation of program committee members to determine the main countries and institutions contributing to the computer systems area. Further, we show our results on the scholars with the highest number of participation on each of the studied conferences and when combined. Finally, we analyze the co-authorship network to highlight some of the important research collaboration patterns in this community.

An algorithm represents the computer-based road map for accomplishing a given, well-defined task. In general, computer algorithms form one of the core practical technologies of computer science. In principle, research in theoretical computer science focuses on the design and analysis of algorithms and data structures, as well as the study of fundamental barriers to efficient computation. The research community of this field has been significantly growing in the last decade. In general, in the computer science field, disseminating research outcomes is an integral part of a scholar’s professional career. The preference for conference publications is motivated by many arguments such as the high dynamism of the field requiring shorter turnaround time between submission; the prestige associated with publishing at highly-selective venues. We analyze the publications of three top-tier computer algorithms research conferences (SODA, ICALP and SOCG) over the last two decades (1995 - 2014). In particular, we report the list of the scholars who have the highest number of publications for each venue separately and when combined. We also analyze the co-authors relationships to understand their research collaborations. Finally, we study the increase in the number of publications and the size of the research community in the last two decades.