Better drilling through sensor analytics

Using sensor data from fixed highway traffic detectors, as well as data from highway patrol logs and local weather stations, we aim to answer the domain problem: "A traffic incident just occurred. How severe will its impact be?" In this paper we show a practical system for predicting the cost and impact of highway incidents using classification models trained on sensor data and police reports. Our models are built on an understanding of the spatial and temporal patterns of the expected state of traffic at different times of day and locations and past incidents. With high accuracy, our model can predict false reports of incidents that are made to the highway patrol and classify the duration of the incidentinduced delays and the magnitude of the incident impact, measured as a function of vehicles delayed, the spatial and temporal extent of the incident. Equipped with our predictions of traffic incident costs and relative impacts, highway operators and first responders will be able to more effectively respond to reports of highway incidents, ultimately improving drivers' welfare and reducing urban congestion.

show abstract

Big data in software engineering: A systematic literature review

Bağrıyanık

Karahoca²

2016

gjit

View full text Add to dashboard Cite

Purpose of Study: We investigate the big data studies using batch and/or streaming data generated in the process of software development lifecycle. All phases of application development phases are in our scope including but not limited to elicitation, requirements analysis, design, software implementation, version control management, unit / functional / regression / automated / performance / stress test, release management, application log monitoring, application usage monitoring, user complaint management, security and compliance management and software problem management.Methods: We use a systematic literature review methodology used in Software Engineering studies to find and analyse the related studies published from January 2010 to October 2015. We synthesize the quantitative and qualitative outputs of selected papers and report the results.Findings and Results: In general, there are scarce studies in the literature. However there are relatively more papers regarding some areas such as Software Quality, Development, Project Management and Human Computer Interaction. However research in some fields such as Deployment, Requirements Engineering, Release Management and Mobile Applications were relatively less. Conclusions & Recommendations: More studies are required to identify the use cases, data attributes, measurements, platform requirements especially in the fields which are identified as having lack of study. A holistic big data perspective is needed to support software engineering ecosystems in large and complex enterprises. Keywords: Big Data, Software Engineering, Software Analytics, Data Mining, Software Development, Operational Intelligence, Software Archaeology

show abstract

Better drilling through sensor analytics

Cited by 3 publications

References 7 publications

Optimizing Flows for Real Time Operations Management

Optimizing Flows for Real Time Operations Management

Mining traffic incidents to forecast impact

Big data in software engineering: A systematic literature review

Contact Info

Product

Resources

About