‘Cognitive facility management’: Definition, system architecture, and example scenario

Abstract Prevailing facility management (FM) discourses have recognized the importance of efficient facilities to improve the quality of life and the productivity of business. Advanced technologies have elevated facilities from ‘bricks and mortar’ to ‘intelligent beings’. To date, facilities have become more anthropomorphized, imbued with cognitive capability akin to humans, e.g., able to perceive, learn, and act. However, the development of ‘cognitive FM’ remains in its infancy. This paper attempts to put forward the concept of cognitive FM by providing a working definition and articulating its key characteristics, including perception, learning, and action. An eight-layer system architecture is proposed to facilitate the implementation of cognitive FM. Following that, a demonstrative scenario called ‘Event Manager’ is utilized to showcase the potential applications of such cognitive FM. The paper contributes to the body of knowledge by advancing the stagnant FM discourses defined and subsequently confined by the smart/intelligent building language three decades ago. It opens a new avenue for both researchers and practitioners to better investigate and value FM as a cognitive system.

[1]  Longhui Liao,et al.  Managing critical drivers for building information modelling implementation in the Singapore construction industry: an organizational change perspective , 2019 .

[2]  P. Berkes,et al.  Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .

[3]  Amit Konar,et al.  Artificial Intelligence and Soft Computing: Behavioral and Cognitive Modeling of the Human Brain , 1999 .

[4]  Selçuk Bilgen,et al.  Structure and environmental impact of global energy consumption , 2014 .

[5]  Christopher D. Manning,et al.  Advances in natural language processing , 2015, Science.

[6]  Richard de Dear,et al.  Individual difference in thermal comfort: A literature review , 2018, Building and Environment.

[7]  Yuki Suga,et al.  Multimodal integration learning of robot behavior using deep neural networks , 2014, Robotics Auton. Syst..

[8]  Johnny Wong,et al.  Digitisation in facilities management: A literature review and future research directions , 2018, Automation in Construction.

[9]  Pentti Vähä,et al.  The benefits and obstacles of mobile technology in FM service procurement , 2009 .

[10]  Simon Haykin,et al.  Cognitive Dynamic Systems: Perception-action Cycle, Radar and Radio , 2012 .

[11]  Myron Flickner,et al.  Compass: A scalable simulator for an architecture for cognitive computing , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[12]  A. H. Buckman,et al.  What is a Smart Building , 2014 .

[13]  Bernard Drion,et al.  Facilities management: lost, or regained? , 2012 .

[14]  Tamas Madl,et al.  LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning , 2014, IEEE Transactions on Autonomous Mental Development.

[15]  R. Kaplan,et al.  Strategy Maps: Converting Intangible Assets into Tangible Outcomes , 2003 .

[16]  Susanne Balslev Nielsen,et al.  Sustainability in facilities management: an overview of current research , 2016 .

[17]  Ming-Whei Feng,et al.  Complex event processing for the Internet of Things and its applications , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[18]  Qihui Wu,et al.  Cognitive Internet of Things: A New Paradigm Beyond Connection , 2014, IEEE Internet of Things Journal.

[19]  M. St-Hilaire,et al.  A cloud-based approach for smart facilities management , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

[20]  Herbert A. Simon,et al.  Cognitive Science: The Newest Science of the Artificial , 1980, Cogn. Sci..

[21]  Knud Illeris,et al.  Transformative Learning in the Perspective of a Comprehensive Learning Theory , 2004 .

[22]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[23]  Chimay J. Anumba,et al.  Smart Construction Objects , 2016, J. Comput. Civ. Eng..

[24]  Weisheng Lu,et al.  Bridging BIM and building (BBB) for information management in construction , 2019, Engineering, Construction and Architectural Management.

[25]  Derek Clements-Croome,et al.  What is an intelligent building? Analysis of recent interpretations from an international perspective , 2016 .

[26]  Neil Johnson,et al.  A smart sensor to detect the falls of the elderly , 2004, IEEE Pervasive Computing.

[27]  Amit P. Sheth,et al.  Internet of Things to Smart IoT Through Semantic, Cognitive, and Perceptual Computing , 2016, IEEE Intelligent Systems.

[28]  Ying Chen,et al.  IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. , 2016, Clinical therapeutics.

[29]  Donald A. Norman,et al.  Twelve Issues for Cognitive Science , 1980, Cogn. Sci..

[30]  Weisheng Lu,et al.  Taxonomy and Deployment Framework for Emerging Pervasive Technologies in Construction Projects , 2019, Journal of Construction Engineering and Management.

[31]  Diane J. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Ambient Intelligence: Technologies, Applications, and Opportunities , 2022 .

[32]  Diego Reforgiato Recupero,et al.  Bridging learning analytics and Cognitive Computing for Big Data classification in micro-learning video collections , 2019, Comput. Hum. Behav..

[33]  Charles Crichton,et al.  Interdependence and uncertainty : a study of the building industry , 2001 .

[34]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[35]  Daniele Toti,et al.  Experimentation of a smart learning system for law based on knowledge discovery and cognitive computing , 2019, Comput. Hum. Behav..

[36]  Karen E. Adolph,et al.  Learning by doing: Action performance facilitates affordance perception , 2010, Vision Research.

[37]  Adrian Brooks,et al.  Total Facility Management , 2015 .

[38]  Carlos T. Formoso,et al.  An analysis of construction safety best practices from a cognitive systems engineering perspective , 2008 .

[39]  Yiyu Yao,et al.  Perspectives on Cognitive Informatics and Cognitive Computing , 2010, Int. J. Cogn. Informatics Nat. Intell..

[40]  P. Love,et al.  From justification to evaluation: Building information modeling for asset owners , 2013 .

[41]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[42]  Byoung-Tak Zhang,et al.  Perception-Action-Learning System for Mobile Social-Service Robots Using Deep Learning , 2018, AAAI.

[43]  Ke Chen,et al.  Automatic building information model reconstruction in high-density urban areas: Augmenting multi-source data with architectural knowledge , 2018, Automation in Construction.

[44]  Kyohei Otsu,et al.  Where to Look? Predictive Perception With Applications to Planetary Exploration , 2018, IEEE Robotics and Automation Letters.

[45]  Daniel Díaz Sánchez,et al.  Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies , 2017, Future Gener. Comput. Syst..

[46]  Alessandra Flammini,et al.  Exploiting Internet of Things and building information modeling framework for management of cognitive buildings , 2016, 2016 IEEE International Smart Cities Conference (ISC2).

[47]  Mehrdad Arashpour,et al.  Integrated management of on-site, coordination and off-site uncertainty: Theorizing risk analysis within a hybrid project setting , 2017 .

[48]  Salman Azhar,et al.  Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC Industry , 2011 .

[49]  Nicu Sebe,et al.  Multimodal Human Computer Interaction: A Survey , 2005, ICCV-HCI.

[50]  J. Fuster Cortex and mind : unifying cognition , 2003 .

[51]  Tai-hoon Kim,et al.  Applications, Systems and Methods in Smart Home Technology: A Review , 2010 .

[52]  Luis M. Candanedo,et al.  Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models , 2016 .

[53]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[54]  Joern Ploennigs,et al.  Materializing the Promises of Cognitive IoT: How Cognitive Buildings Are Shaping the Way , 2018, IEEE Internet of Things Journal.

[55]  Ke Chen,et al.  Linking radio-frequency identification to Building Information Modeling: Status quo, development trajectory and guidelines for practitioners , 2018, Automation in Construction.

[56]  W. Prinz,et al.  Motor learning enhances perceptual judgment: a case for action-perception transfer , 2001, Psychological research.

[57]  Chen Mao,et al.  Occupancy Estimation in Smart Building using Hybrid CO2/Light Wireless Sensor Network , 2016 .

[58]  David J. Edwards,et al.  The building information modelling trajectory in facilities management: A review , 2017 .

[59]  Fernando Seco Granja,et al.  Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis , 2017, IEEE Transactions on Instrumentation and Measurement.

[60]  Edward E. Lawler,et al.  The New American Workplace , 2006 .

[61]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[62]  Naixue Xiong,et al.  Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications , 2016, Inf. Sci..

[63]  Frank Schultmann,et al.  Building Information Modeling (BIM) for existing buildings — Literature review and future needs , 2014 .

[64]  Younghan Jung,et al.  An approach to automated detection of structural failure using chronological image analysis in temporary structures , 2019 .

[65]  Mary Helen Immordino‐Yang The Smoke around Mirror Neurons: Goals as Sociocultural and Emotional Organizers of Perception and Action in Learning. , 2008 .

[66]  S. Clegg,et al.  Bringing Space Back in: Organizing the Generative Building , 2004 .

[67]  José Rodellar,et al.  Active and semi-active control of structures – theory and applications: A review of recent advances , 2012 .

[68]  Alberto M Marchevsky,et al.  Evidence-based pathology in its second decade: toward probabilistic cognitive computing. , 2017, Human pathology.

[69]  Sanghoon Lee,et al.  Activity theory-based analysis of BIM implementation in building O&M and first response , 2018 .

[70]  Paul Rookes,et al.  Perception: Theory, Development and Organisation , 2000 .

[71]  Marek R. Ogiela,et al.  Cognitive systems for intelligent business information management in cognitive economy , 2014, Int. J. Inf. Manag..

[72]  Subhas Mukhopadhyay,et al.  WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings , 2015, Sensors.