Regardless of your location, if you look around you will find numerous sources that are continuously generating data streams. Anything from a car passing by to a smartwatch on your wrist or a traffic motion sensor in the street outside now produces ordered sequences of data points that are potentially infinite, flowing at high-speed, dynamic and time-changing.
Data streams or dynamic data are different from static data precisely because of these qualities. Passive repositories provide insight into the near past. Dynamic data, on the other hand, facilitates sensing and responding by continuously monitoring and reacting to anomalous or distinctive events as they happen. When clustered into sensor networks, these distributed autonomous devices can cooperatively monitor the environment, in a way where each node in the network is able to sense, process and act.
The inherent potential of real-time data to improve our lives both on micro and macro levels is immense. Currently society is facing many societal, political, and environmental challenges that require precision, speed, and ambition in decision-making. Addressing these issues entails a global vision, which takes into consideration an extreme entanglement of all aspects of everyday life and more so, the future. Data streams produced by numerous connected edge devices produce a link, an in-between layer between cyberspace and the lived reality that mediates between the two in near real-time and can, therefore, enable an informed, detail-oriented – and, sometimes, seemingly unconventional – response to a problem.
For instance, in a smart city, sensors can provide information on adverse structural changes in roads, bridges or buildings, thus alerting that maintenance is needed and thereby resulting in an improved infrastructure. Combine that with technologies such as intelligent traffic signals, which optimize traffic flow, as well as with efficient vehicle routing and you will get a highly efficient transport system, which may encourage citizens to use public transport, and eventually, significantly reduce the environmental toll caused by traffic pollution.
In addition, the insight provided by data streams has a capacity to revolutionize not only society but also individual lives, both personally and professionally. Take the example of a smart house. It provides a setting in which our living spaces adapt to our shifting needs over time. Seniors whose physical and mental abilities deteriorate with time may choose smart-home devices that assist with daily tasks, such as smart lighting for dark hallways or smart switches that ensure that heaters or ovens aren’t left unattended. In turn, this could provide a cheaper alternative to care facilities.
What is even more fascinating is that combining typical areas of electrical and computer engineering (such as integrated systems and sensor technology) with recent computer science subjects (such as machine learning, data mining or artificial intelligence) within disciplines including but not exclusive to biology, agriculture, or medicine, we are given limitless possibilities for interdisciplinary research and, most importantly, its real-life applications.[1] This myriad of potentialities opens up a space for radical innovation, which is founded on an acquisition of previously untapped knowledge, as we combine data into meaningful structures and put it into context. Throughout history, new knowledge was mainly built on the foundation of already established theories by simply adjusting the models or parameters of those models. Data-driven research, instead, has shown us that knowledge acquisition can be exploratory in nature. Since it does not conform to conceptual fragmentation or social barriers, its capacity to unveil disruptive patterns is far-reaching and unbound. Essentially, the availability of a vast amount of new data and its combinations enables drastically new ways of imagining. Thus, not only may it help to connect the dots where it previously seemed impossible, but it may herald new scientific, business, or economic thought, eventually speeding up the pace of progress in all areas.
Data streams or dynamic data can, therefore, have a profound effect on the way we acquire, apply, and generate knowledge, which can result in radical innovation, surprising connections, and even more astonishing solutions to the defining problems of our time.
[1] João Barros, “Sensor Networks: An Overview”, in Learning from Data Streams: Processing Techniques in Sensor Networks, ed. João Gama and Mohamed Medhat Gaber (Berlin: Springer, 2007), p. 9.
