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Physical-Cyber-Social Computing: An Early 21st Century Approach to
Computing for Human Experience
Visionaries and scientists from the early days of computing and electronic communication have discussed the proper role of technology to improve human experience. Technology now plays an increasingly important role in facilitating and improving personal and social activities and engagements, decision making, interaction with physical and social worlds, generating insights, and just about anything that a human, as an intelligent being, seeks to do. We have used the term Computing for Human Experience (CHE)  to capture this essential role of technology in a human centric vision. CHE emphasizes the unobtrusive, supportive, and assistive role of technology in improving human experience, so that technology “takes into account the human world and allows computers themselves to disappear in the background” (Mark Weiser ). This can be distinguished from Licklider’s vision of human-computer collaboration , Engelbart’s vision of augmenting human intellect , and McCarthy’s definition of intelligent machines .
In this article, we present a vision of the future of computing, called physical-cyber-social (PCS) computing. PCS computing is a holistic treatment of data, information, and knowledge from physical, cyber, and social worlds to integrate, understand, correlate, and provide contextually relevant abstractions to humans. PCS computing takes ideas from, but goes significantly beyond, the current progress in cyber-physical systems, socio-technical systems and cyber-social systems to support CHE . We will exemplify future CHE application scenarios in healthcare and traffic management that are supported by (a) a deeper and richer semantic interdependence and interplay between sensors and devices at physical layers, (b) rich technology mediated social interactions, and (c) the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning in order to bridge machine and human perceptions. We will share an example of PCS computing using semantic perception , which converts low-level, heterogeneous, multimodal and contextually relevant data into high-level abstractions that can provide insights and assist humans in making complex decisions. The key challenge for PCS computing requires moving away from traditional data processing to multi-tier computation along a data-information-knowledge-wisdom dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable by people.
Role of Technology in Human Experience
Ideas on the ways technology -- including devices, computing and communication -- help humans have taken many forms, including: natural human interfaces and interactions (natural computing , gesture computing , and intelligence at the interface ), ubiquitous computing , robotics that have focused on mimicking simple human activities, and current efforts in automating complex human activities such as war.
These different visions of computing fall within a spectrum between machine-centric computing and human-centric computing. One end of the spectrum that delineates major visions (Figure 1) focuses on making computing more intelligent in order to think and behave like humans, in the vein of Vannever Bush (i.e., Memex ). A variety of approaches to creating Artificial Intelligence include recent work in neurocomputing all the way to current discussions of droids with increasingly human-like capabilities. The fundamental premise here is for technology to be as capable as humans, or at least more like humans. This indeed can indirectly serve humans. For CHE, however, we are interested in the other end of the spectrum, where the focus is on developing technology, which directly complements humans and enhances their experiences. Between the two ends of the spectrum occupied by making computing smarter and CHE, the middle ground is occupied by work in advanced Human Computer Interaction (HCI) and augmented human intellect, or Ambient Intelligence. In Ambient Intelligence, the focus has been on making machines surrounding humans behave intelligently, making it more machine-centric. HCI accommodates the human experience of interacting with technology, making it closer to the human-centric vision of CHE.
During the last few decades, and especially in recent years, we have seen accelerated development within each layer of physical, cyber, and social domains that have a strong bearing on human experience. We see a new form of systems developing: the cyber component encompassing computing and communications, and its modern evolution for collective intelligence and physical devices (which have been assisting humans for many years). The role of digital technologies (cyberspace) in impacting the physical , intellectual, and social worlds is of particular significance.
For example, healthcare is suffering from the increasing cost of services and an aging population all over the world. In the U.S., over $30 billion was spent in 2006 for hospital readmissions . Reducing healthcare costs using innovative sensing and analytics to create a sustainable future is a grand challenge. There is a need to provide assisted living for the elderly still in their own homes to minimize costs, enhancing their comfort and overall experience. The full potential of the decreasing cost of sensors that can monitor physiological and physical parameters, low cost networking, and increased use of smart phones is yet to be realized.
We will consider a healthcare scenario as a motivating example to exemplify the vision of Physical-Cyber-Social (PCS) computing, extending beyond conventional Physical-Cyber systems. For example, consider the case of Ram, an Asian male of age 60 who wants to take care of his health, and yet is aware of the costs. He visits his doctor and receives a blood pressure screening and discovers that his blood pressure is slightly higher than expected: 90 diastolic. Let’s consider two questions that he may have: What is the normal blood pressure of an Asian male of his age? What are the best ways of managing a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from people of similar characteristics and demographics (Physical). The ethnic, social, cultural, and economic background should be considered for similarity (Social). Moreover, in addition to expert knowledge, the knowledge and experiences of similar people dealing with the same health issue is important (Cyber). Current computing systems such as cyber-physical systems are not capable of answering these questions. We envision that PCS computing will provide answers to such important questions in a holistic manner.
For clarity, we have organized the related work in a progressive path toward PCS computing as shown in Figure 2.
There are a myriad of patient monitoring apps that continuously monitor physical/physiological parameters of a person and report to first responders through the cyber world (e.g. LifeWatch , MedApps , CardioNet , and Intelesense ) . These systems span the physical and cyber worlds. In this case, they monitor physical things, such as physiological parameters of a patient, and deliver information to first responders through cyber infrastructure. However, Ram’s questions cannot be answered by a system that does not understand the social aspects of the question.
Social networks such as Facebook, Twitter, and Myspace fall into the category of cyber-social systems. The actors are in the cyber world, engaging in social activities such as sharing, discussing, and propagating their experiences, opinions, and perceptions. Some of the communities may be specialized, such as healthcare (e.g. PatientsLikeMe, 23andMe) , product reviews (e.g. Amazon, Epinions), and disaster management (e.g. Ushahidi ). Ram has to sift through tens of thousands of documents or discussions to find experiences of similar people. However, the physical component is not addressed and he knows that the answers he finds may not be relevant to him.
PCS systems involve physical, cyber, and social components, but within current manifestations they are often loosely connected. Quantified Self is an example of a physical-cyber-social system. It involves a community of enthusiasts who monitor health related parameters, such as food intake, exercise, sleep, and other physiological parameters. They analyze these observations to come up with insights that are valuable for their own health, fitness, and overall well-being. These insights are shared through articles, videos, and social events organized in many cities around the world. Ram has collected observations of his blood pressure, which are stored in a database on his computer. He visualizes them and presents them to the Quantified Self community. But, his questions still cannot be answered due to the lack of knowledge of the experiences of other people with a similar condition.
Observations from these systems are too often stove-piped due to many challenges, such as the semantic integration and sharing of heterogeneous sensor observations, and multiple service providers. Current integration and interaction between physical, cyber, and social worlds is brittle involving limited syntactic interoperability or integration rather than semantic integration. These two challenges have led to significant human involvement in making sense of observations from contextually relevant information from physical, cyber, and social worlds.
Physical-Cyber-Social (PCS) Computing
The vision of PCS computing extends Licklider’s vision of computing in 1960  by adding social, collective, and personalized computing components.
Figure 3 depicts PCS computing involving observations, experiences, background knowledge, and perceptions in a goal toward a human centric vision. The observations from the physical world are used to perceive an environment. Our perceptions are strongly influenced by our background knowledge and current observations. By analyzing the observations in the context of our background knowledge, we orient ourselves toward subsequent actions. The decision regarding which action to take is based on our evidence and experiences. The final action is executed and the process repeats in a loop. In Figure 3, the influence of background knowledge on the way we interpret current observations though perceptual inference is shown. Perceptions determine our experience and evolve our background knowledge. Experiences from the social world influence our background knowledge and indirectly shape our perceptions and subsequent experiences. All these interactions are heavily dependent on humans to analyze the connections between physical, cyber, and social worlds. The vision of PCS computing is to consider observations, knowledge, and experiences across PCS layers to provide a more holistic computational framework.
Physical-Cyber systems can no longer be limited to special purpose embedded systems designed for a single task . Our vision goes beyond the interactions and integration of observations from physical, cyber, and social domains. We envision a deeper semantic integration and interplay between the three layers, as shown in Figure 2. The outer circle in the figure represents systems with shallow integration between physical, cyber, and social systems. PCS computing in the inner circle provides deeper integration, interpretation, and personalization of the physical, cyber, and social worlds.
Relationship between PCS and CHE
John Boyd’s concept of observe, orient, decide, and act (OODA-Loop)  provides a useful context to describe PCS computing, involving observations from physical, cyber, and social worlds, and their intelligent processing leading to CHE. In order to show how PCS computing can be used to improve human experience, we first need to be able to speak intelligibly about experience. Experience is a broad term, so we will begin by dividing experience into its component parts. This ontology of human experience is composed of observation (and perception), orientation, decision, and action. These experiential activities are derived from the OODA loop, which will serve as a foundational ontology of human experience.
- Observe – Observation is the act of examining the environment and gathering data. The environment could be physical, examined through the human senses or machine sensors. The environment could be social, examined through social network technologies such as facebook or twitter. The environment could be cyber, examined through Web technologies such as Wikipedia, Google search, or Wolfram Alpha.
- Orient – Orientation is the act of analyzing the data from the observed environment to form a conceptualization of the situation. That is, integrate and interpret the data, translating it from low-level data into high-level knowledge. Orientation is how we conceptualize and understand a situation, based on “culture, experience, new information, analysis, synthesis, and heritage.”
- Decide – Decision is the act of deliberating and choosing between multiple actions in order to proceed towards a goal.
- Act – Action is the process of engaging in a decided activity. This could involve physical action, such as movement, or gathering additional information through observation, by examining the physical, social, or cyber environment.
We characterize the PCS computing process as an OODA-Loop process and Figure 3 can be viewed as an OODA-loop.
How to resolve the Big Data problem?
The use of physical, cyber, and social data indeed manifests into a Big Data challenge and opportunity. If the existence of social data leads to a Big Data problem, as with many contemporary social networking systems, with the introduction of physical data, the Big Data problem will explode. The Big Data problem can be divided into the task of managing data with four distinguished characteristics: volume, velocity, variety, and veracity. In our PCS computing approach, semantic computing plays a pivotal role in addressing these challenges. The following sketch describes how semantics-empowered PCS computing will address these challenges:
- The challenge of variety, or heterogeneity of data, is addressed by complementing traditional statistical and information retrieval, lexical, natural language processing techniques with semantic interoperability and integration. The former addresses syntactic and structural/representational interoperability. The latter involves the use of ontologies for semantic descriptions of concepts that data and observations capture, semantic annotation of data and semantic integration or fusion of the data. Semantic services enhanced techniques are also used to model and interact with devices and sensors in the physical world, or agents interfacing with humans.
- The challenge of volume and velocity is addressed by integration and interpretation of data at the source (or as close as possible), through "intelligence at the edge." This is accomplished by downscaling semantic processing of data and making each resource-constrained device more intelligent, i.e., capable of semantic filtering, integration, and interpretation of streaming heterogeneous data .
- The challenge of veracity is addressed by assessing trustworthiness and provenance of observations in the PCS systems. Trustworthiness may have different interpretations in physical, cyber, and social domains. Semantics can play an important role of meaningfully integrating these different notions of trust spanning physical, cyber, and social worlds .
Besides the semantics-empowered capabilities such as those described above, we expect that the core of PCS computing will be made possible by a series of PCS operators. We describe two such operators as early examples, shown in Figure 4.
Semantic integration of heterogeneous, multi-modal observations play an important role in deeper integration and understanding of observations spanning physical, cyber, and social domains. These operators map multimodal PCS data into concepts to support semantic integration within each level of the multi-tier computation along data-information-knowledge-wisdom dimension. Heterogeneous observations in the domain of healthcare include: (1) machine sensor observations: physical measurements from multiple, heterogeneous, active (active human involvement e.g. blood pressure) and passive (passive human involvement e.g. heart rate) sensors, (2) self-observations (in the form of subjective thoughts, feelings, moods, etc.), (3) social observations (from a network of family, friends, colleagues, etc.), and (4) demographic observations (aggregated characteristics of the population with similar attributes and/or lineage). PCS horizontal operators integrate all these observations (within each layer in Figure 4) using semantics-empowered integration techniques.
Vertical operators are used to transcend observations from a lower level to a higher level in Figure 4. These operators are unique compared to existing operations of ascending through the DIKW triangle in the following two ways: (1) the operator is agnostic of the source of the observations, and (2) the knowledge base is not limited to a formal ontology – it spans from statistical knowledge to social experiences. Semantic perception [4,8,9] is one way of ascending through the triangle.
Perception is the act of translating low-level data, acquired through observation, into high-level knowledge. People have evolved sophisticated mechanisms to perceive their environment, which allow for high-level conceptualizations (or abstractions) of a situation. These abstractions are then used for subsequent decision-making and action, without mentally overwhelming a person through the sheer volume and velocity of incoming sensory signals. Through study in cognitive psychology, scientists now understand that the key to perception is prior knowledge . In order to design machines capable of perception, Semantic Web technologies may be utilized to integrate observation data with prior knowledge on the Web (such as Linked Open Data) , in order to interpret and make sense of the data. Traditionally, perception is thought of as the act of abstracting physical sensory signals (i.e., physical data). However, we must expand this notion to envision machine perception capable of utilizing prior knowledge found throughout the Web (cyber) in order to integrate and interpret observations from physical, cyber, and social dimensions. This type of perception is far more encompassing and far-reaching than the capability of any single person.
“In the next century, planet earth will don an electronic skin. It will use the Internet as a scaffold to support and transmit its sensations. This skin is already being stitched together .” With the advance of technologies such as semantic perception, along with PCS computing, this dream is quickly becoming reality. Another concept called the Global Brain , envisioning computing to be more encompassing and holistic states that “The best symbiosis of man and computer is where a program learns from humans but notices things they would not.”
Next we describe two early examples of next generation applications that can be made possible with PCS computing.
Personalized health and patient empowerment are important themes for the next generation of healthcare technology. Today we have smartphone applications driven by background knowledge, we have an increasing number of cyber-physical systems, such as medical implants for heart related diseases, and we have social media applications for patients to learn from other patients and experts. Nevertheless, medicine and health are complex, with a wide array of variables, including symptoms, vital signs, personal history, family history, medications taken, genetic makeup, diet, exercise regimens, expectations and quality of life choices, social and economic factors, and so on. All these variables need to be put in a social and personal context. As an example, the NIH has identified that a person with diastolic pressure between 86 and 90 may be considered pre-hypertensive, and above 90 classified as stage I hypertension . For a South Asian male such as Ram, however, preferred and normative ranges may be lower.
Given the rapid growth of the Quantified Self movement and the use of social media for health, it is appropriate to expect that in the future one would have devices taking vital signs with respect to a health concern, and PCS computing technology constantly evaluating health conditions with respect to relevant knowledge (e.g., knowledge derived from the NIH, and other recommendations) as well as social knowledge. For the latter, consider that a South Asian male is likely to have a good cross section of friends with similar genetic and socio-economic profiles, so his health condition can be compared with those of his friends for a more personalized solution. Knowledge that several friends who are close in age, with similar education and socio-economic situations have cardiovascular problems would empower this person to closely compare a broader variety of variables. This leads to a better understanding of his risk profile that wouldn’t be possible without such social knowledge.
PCS computing would be able to answer Ram’s questions, introduced in the beginning of the article, and as shown in Figure 3. The numbering indicates the ordering of the processes in the cycle. Ram has diastolic blood pressure between 86 and 90 mmHg. The background knowledge from authoritative sources (e.g. NIH) indicates that he is pre-hypertensive. However, knowledge from similar people with the same ailment indicates that Asian males have lower thresholds than average for being diagnosed with hypertension. This background knowledge influences the perception process in inferring a higher risk. PCS computing can provide deeper insights and corrective actions. It is capable of integrating observations, experiences, and knowledge from people with similar ethnic, social, cultural, and economic background. Considering all these aspects while computing solutions, PCS will provide effective, personalized, and actionable information to Ram, as shown in Figure 3. Being an Asian male, it is recommended to reduce his salt intake and substitute it with spices.
With an increased demand for resources, cities are under pressure to reduce and optimize resource consumption in a city. Traffic monitoring is part of a broader scope of sustainability applications (e.g. water, energy, public safety) and involves cyber-physical systems. Observations in the domain of traffic span across multiple modalities spanning machine sensors to citizen sensors . Machine sensors include speed sensors, cameras, noise sensors, etc., while citizen sensors include people reporting observations about traffic (such as police, and commuters). All these sensors are monitoring a physical system, the road network, which collectively forms a physical, cyber, and social system. These sensors are interconnected and their observations are available on the web. There are social (textual) observations about various events in a city some of which may influence traffic. PCS computing envisions a holistic approach to computing by considering observations from all these modalities and further exploiting cyber and collective knowledge.
Figure 5 shows a slow moving traffic event being detected by sensors monitoring the speed of vehicles (red strip) on I-77 South at Ridgewood Road. Current cyber-physical systems [12, 13] do not exploit collective knowledge of the domain of traffic available from existing knowledge bases on the web (e.g. ConceptNet 5 , Open Data from city authorities). The knowledge of relationships between events affecting traffic can be derived from these sources. In this example, ConceptNet 5 defines a causal relation between an accident and slow moving traffic. There is a tweet and a news article reporting an accident on Ridgewood Road where the machine sensors detected slow moving traffic.
Such an event will have a different effect on a person travelling vs. a decision-maker such as traffic authorities. PCS computing will consider the context of this observation by asking questions: Who needs this information? How does this impact a person travelling? How does this impact a decision-maker? How long will these events last? What available knowledge and social experiences can be used in this analysis?
Considering different modalities for analysis will help us deal with incompleteness (complementary sensor observations) and uncertainty (redundant sensor observations) which prevail in most of the domains making physical-cyber-social observations an important way of dealing with problems in many domains.
PCS computing involves personalized and contextual processing of observations for enhancing human experience.
Technologies have been assisting humans to solve problems in many ways. There are many computing theories that exist for solving problems using crisp and well-developed theories. Humans are inundated with a lot of observations from physical, cyber, and social worlds. Yet, we perform integration and interpretation of these observations in a seamless way. While the approach of computing has been in modeling problems using well-formed theories, we envision two possible ways of achieving seamlessness as humans: (1) Use multiple theories that are well formulated to solve a problem (e.g. probabilistic, logical, statistical), and (2) A fundamentally different approach to computing.
PCS computing captures a synergetic interaction between computing and humans while providing holistic computational solutions extending physical, cyber, and social worlds. The future of technology will not be primarily about asking questions and receiving relevant documents for search queries. CHE enabled by PCS computing represents a paradigm shift from search-based technology to solution-based technology where knowledge is generated by continuous observation of human activities within the physical, cyber, and social worlds, and to use this knowledge to improve human experience. That is, the vision of PCS computing is to think of computing which translates into action using knowledge from cyber space, an idea that is synergistic to that of the global brain .
Related Talks and Presentation
 Amit Sheth, 'Computing for Human Experience: Semantics-Empowered Sensors, Services, and Social Computing on the Ubiquitous Web,' IEEE Internet Computing (Sp. Issue on Internet Predictions: V. Cerf and M. Singh, Eds.), vol. 14, no. 1, pp. 88-91, Jan./Feb. 2010, doi:10.1109/MIC.2010.4
 Weiser, Mark. "The computer for the 21st century." Scientific American 265, no. 3 (1991): 94-104.
 Amit Sheth, Semantics empowered Physical-Cyber-Social systems, In: What will the Semantic Web look like 10 years from now? In conjunction with the 11th International Semantic Web Conference 2012 (ISWC 2012). Boston, USA. November 11-15, 2012 (Workshop date: 11/11/2012).
 C. Henson, A. Sheth, K. Thirunarayan, Semantic Perception: Converting Sensory Observations to Abstractions
 Licklider, J.C.R., "Man-Computer Symbiosis", IRE Transactions on Human Factors in Electronics, vol. HFE-1, 4-11, March 1960.
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 Gregory, R.L.: Knowledge in perception and illusion. In: Philosophical Transactions of the Royal Society of London, 352(1358), pp.1121-1127 (1997).
 Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth. An Ontological Approach to Focusing Attention and Enhancing Machine Perception on the Web. Applied Ontology, vol. 6(4), pp.345-376, 2011.
 Cory Henson, Krishnaprasad Thirunarayan, and Amit Sheth, 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,' In: Proceedings of 11th International Semantic Web Conference (ISWC 2012), Boston, Massachusetts, USA, November 11-25, 2012.
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 Pramod Anantharam, Cory A. Henson, Krishnaprasad Thirunarayan and, Amit P. Sheth, "Trust Model for Semantic Sensor and Social Networks: A Preliminary Report", Aerospace and Electronics Conference (NAECON), Proceedings of the IEEE 2010 National , vol., no., pp.1-5, 14-16 July 2010.
 Yan Xu, Maribeth Gandy, Sami Deen, Brian Schrank, Kim Spreen, Michael Gorbsky, Timothy White, Evan Barba, Iulian Radu, Jay Bolter, and Blair MacIntyre. 2008. BragFish: exploring physical and social interaction in co-located handheld augmented reality games. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology (ACE '08). ACM, New York, NY, USA, 276-283.
 Blake Sawyer, Francis Quek, Wai Choong Wong, Mehul Motani, Sharon Lynn Chu Yew Yee, and Manuel Perez-Quinones. 2012. Using physical-social interactions to support information re-finding. In CHI '12 Extended Abstracts on Human Factors in Computing Systems (CHI EA '12). ACM, New York, NY, USA, 885-910.
Amit Sheth, Pramod Anantharam, Cory Henson, 'Physical-Cyber-Social Computing: An Early 21st Century Approach,' IEEE Intelligent Systems, pp. 79-82, Jan./Feb. 2013
Related Writings, Talks and Events
- Krishnaprasad Thirunarayan and Amit Sheth, " Semantics-empowered Approaches to Big Data Processing for Physical-Cyber-Social Applications," Kno.e.sis Technical Report, May 14, 2013.
- A. Sheth, Physical-Cyber-Social-Computing An early 21st century approach to Computing for Human Experience Keynote at The International Conference on Web Intelligence, Mining and Semantics (WIMS 13), July 12-14, 2013. Video of the KeyNote
- A. Sheth, Semantics empowered Physical-Cyber-Social systems, Blog, August 12, 2012
- A. Sheth, Physical Cyber Social Computing, Talk at What will the Semantic Web look like 10 years from now? (SW2022 at ISWC2012), November 11, 2012.
- Dagstuhl Seminar on Physical-Cyber-Social Computing (Organizers: Barnaghi P., Jain R., Sheth A., Staab S., Strohmier M.), Dagstuhl Seminar, September 30-October 04, 2013.
- A. Sheth, P. Anantharam, Physical Cyber Social Computing for Human Experience, Intl Conf on Web Intelligence, Mining and Semantics (WIMS2013), June 12-14, 2013, Madrid, Spain (Invited Paper).
Also see: Computing for Human Experience