Recent advances in sensing technologies and imaging systems have spurred interest in analyzing high-dimensional, streaming data from real-world complex systems. In the context of big data, streaming data are generated by many different sources at high speed. These data capture the dynamic behaviors and causalities of the underlying processes and provide unprecedented opportunities to model, monitor, detect, and predict incipient and critical changes in a process in (near) real time.

This Editor's Cut volume highlights recently published work by considering four subtopics:

• Streaming Data in Quality and Reliability
• Streaming Data in Energy
• Streaming Data in Transportation
• Data Generation, Analysis, and Visualization

Analyzing streaming data enables real-time and data-driven decisions that are essential in quality, statistics, and reliability (QSR) problems and improvement. For example, manufacturers have been placing sensors in shop-floor equipment to feed information directly to an analytics engine – enabling them to identify, predict, and then proactively address the machine-related quality and reliability issues. Another example, many production systems deteriorate over time as a result of load and stress. The deterioration rate of these systems typically depends on the production rate. The use of condition monitoring to dynamically adjust the production rate can greatly minimize maintenance costs and maximize production revenues.

Over the past few years, there have been important breakthroughs in data analytics, statistics, computing, and machine learning. In-depth integration of engineering domain knowledge with the emerging methods provide new opportunities to achieve another level of sophistication in terms of methodological innovation and application impacts. For example, one great research opportunity is to help with development of novel methods for monitoring, diagnosis, prognosis, control, management, and re-design of energy infrastructure. In the area of wind energy, researchers integrate multisource data, including environmental conditions, wind direction, air density, turbulence, wind shear, and humidity, to answer the fundamental questions of how various conditions affect wind turbine reliability and wind power production.

Transportation is another significant application area for streaming data, which can contain critical information on traffic flow and is important for operational control. Researchers leverage such data to study traffic flow phenomena, system analysts need such data to predict system utilization and congestion, accident investigators find the model handy to reconstruct accidents, software developers may implement the model to enable computerized simulation, and practitioners can devise strategies to improve traffic flow.

The explosive growth of data in volume, variety, and velocity has also transformed the ways that we collect and visualize the data. Experimental design or adaptive sampling, among others, are effective tools to explore data space. Various disciplines, ranging from technology, business strategy, to governmental policies and regulations, are exploring streaming data analytics to achieve viable business solutions.

Through academic research, professional practice, and community service, we hope that this Editor’s Cut collection can yield some critical insights on the future challenges that will transform our research, industry practice, and organizational culture.