Going Big: A Large-Scale Study on What Big Data Developers Ask

Software developers are increasingly required to write big data code. However, they find big data software development challenging. To help these developers it is necessary to understand big data topics that they are interested in and the difficulty of finding answers for questions in these topics. In this work, we conduct a large-scale study on Stackoverflow to understand the interest and difficulties of big data developers. To conduct the study, we develop a set of big data tags to extract big data posts from Stackoverflow; use topic modeling to group these posts into big data topics; group similar topics into categories to construct a topic hierarchy; analyze popularity and difficulty of topics and their correlations; and discuss implications of our findings for practice, research and education of big data software development and investigate their coincidence with the findings of previous work

Bisimulation-based Structural Summaries of Large Graphs

With an increasing number of heterogeneous entity descriptions available as large graphs that grow to millions of nodes and billions of edges, it is a challenge to understand, explore, and query these large graphs. Bisimulation-based structural summaries have often been used as a compact representation of the dataset that can improve query performance. However, current bisimulation summary construction techniques for large graphs do not scale and do not facilitate the use of summaries within existing systems. We address these challenges with three contributions. First, we describe bisimulation summary construction techniques for large graphs that leverage a novel singleton optimization which drastically reduces construction time. Second, we show how structural summaries can be used to improve query performance within existing RDF systems. Third, we give an ontology for describing structural summaries as RDF that enables their use and verification with existing RDF tools. Our work also demonstrates that the S+EPPs system, built on top of existing RDF processors, is an efficient, scalable, and flexible approach to exploring and querying large graphs using bisimulation-based structural summaries.Ph.D

Efficacy of aerosol reduction measures for dental aerosol generating procedures

Aerosol particles generated by dental procedures could facilitate the transmission of infectious diseases and contain carcinogen particles. Such particles can penetrate common surgical masks and reach the lungs, leading to increased risk for dental care professionals. However, the risk of inhaling contaminated aerosol and the effectiveness of aerosol reduction measures in dental offices remain unclear. The present study aimed to quantify aerosols produced by drilling and scaling procedures and to evaluate present recommendations for aerosol reduction. The concentration of aerosol particles released from the mock scaling and drilling procedures performed on a dental mannequin were measured using a TSI Optical Particle Sizer (OPS 3330) during 15-min sessions carried out in a single-patient examination room. Using the mock drilling procedure as the aerosol source, the aerosol reduction performance of two types of high-volume evacuators (HVEs) and a commercial off-the-shelf air purifier was evaluated in a simulated clinical setting. The use of either HVEs or the air purifier individually reduced the aerosol accumulated over the course of a 15-min drilling procedure at a reduction rate of 94.8 to 97.6%. Using both measures simultaneously raised the reduction rate to 99.6%. The results show that existing HVEs can effectively reduce aerosol concentration generated by a drilling procedure and can be further improved by using an air purifier. Following current regulatory guidelines can ensure a low risk of inhaling contaminated aerosol for dentists, assistants, and patients.Scopu