Situation-aware Building Information Models for Next Generation

Building Management Systems

Ovidiu Noran

1,2 a

, Peter Bernus

and Sorin Caluianu

IIIS Centre for Enterprise Architecture Research and Management - Griffith University, Brisbane, Australia

Faculty of Installations Engineering, Technical University of Constructions Bucharest, Romania

Keywords: Building Information Model, Building Management System of Systems, Decision Support Systems,

Situational Awareness, Big Data, Data Warehousing, Decision Model, Situated Reasoning, Channel Theory.

Abstract: Technical advances in Information and Communication Technology have enabled the collection and storage

of large amounts of data, rising hopes of improving asset decision-making and related building management

support systems. It appears however that the gap between the required decision-making knowledge and the

actual useful information provided by current technologies appears to increase, rather than contract. Thus,

often the multitude of patterns afforded by current data analytics techniques does not deliver a set of scenarios

prone to effective decision making. This paper advocates a decision analytics solution featuring the use of

Situated Logic to create ‘narratives’ providing adequate meaning to data analytics results, and the use of

Channel Theory so as to support adequate situational awareness. This approach is also analysed in the context

of a Building Management System-of-Systems paradigm, highly relevant to the emerging complex Clusters

of Intelligent Buildings within Smart Cities, featuring collaborative decision-making centres and their

associated decision support systems.

1 INTRODUCTION

Building information models (BIMs) are files

typically containing proprietary formats and data

which can be extracted, exchanged or networked to

support decision-making within a Building

Management System (BMS) for a built asset (Van

Nederveen & Tolman, 1992).

BIM can be used to enhance building decision

making (Nowak, Ksiazek, Draps, & Zawistowski,

2016), to coordinate projects (Rokoeei, 2015) and

deliver sustainable building value (Fadeyi, 2017).

BIMs have an important role in reducing the

fragmentation among professionals at each stage and

across building delivery stages by providing a virtual

repository that allows easy access to- and sharing of

information and knowledge in real time. Thus, BIM

also enhances interoperability, providing a platform

for professionals to work in an integrated

environment at any stage of the building delivery

https://orcid.org/0000-0002-2135-8533

https://orcid.org/0000-0001-5371-8743

https://orcid.org/0000-0001-5961-4187

process and further on in the management phase

within BMS (Santos, 2009).

Although traditionally focused more on the short term

and real-time operation, BIMs and BMS are

increasingly required to assist in medium- and long

term asset planning, especially in the case of

Intelligent Building Clusters in Smart Cities (Zhang

et al., 2018). The scope of the models is also expected

to expand from mainly energy consumption to all

aspects of asset management so as to provide a

holistic and integrated view (Teliceanu, Golovanov,

Lazaroiu, & Dumbrava, 2017; Wei & Zhu, 2016). It

must also be noted that nowadays, due to the

increased complexity brought by numbers and variety

of controlled parameters, BMS are increasingly

taking on hybrid agent features, featuring human and

machine components, beyond mere Human Machine

Interface (HMI) within the BMS ‘head end’ (Forte

Asset Services, 2019). All of the above endeavours

essentially depend on the quality and relevant content

of the information obtained from the collected data.

Noran, O., Bernus, P. and Caluianu, S.

Situation-aware Building Information Models for Next Generation Building Management Systems.

DOI: 10.5220/0008767900750082

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 75-82

ISBN: 978-989-758-423-7

Thus, Big Data (Li, Zhang, Hu, & 17., 2017), the

Liquid Enterprise (Bayler, 2016), Sensing

Information Systems (Zdravković, Noran, &

Trajanović, 2014) and similar concepts hold the

promise to provide all necessary decision-making

information for building management in the adequate

detail, quality and ‘freshness’ required. Conceptually,

this endeavour comprises achieving the necessary

capabilities to use the data to derive decision-making

information in an efficient and effective manner,

based on inferring knowledge that was not available

(and attainable) before; nowadays, this assumes the

presence and support of suitable data analytics.

The degree of success in supporting decision-

making heavily depends on proper data synthesis and

interpretation (Dibb, Meadows, & Wilson, 2015).

Thus, proper data analytics will effectively support

decision analytics.

It is also becoming clear that finding new ways to

correctly interpret complex data in context is

necessary (SAS, 2015). For example, evidence-based

medicine that relies on large scale data gathering

through clinical trials and careful statistical analysis

is now showing difficulties when the evidence

gathered is applied in complex individual cases

(Greenhalgh, Howick, & Maskrey, 2014; Pope,

2003).

From the above, an obvious perspective is that

when intending to use large amounts of gathered data

to create useful decision-making information, one

must carefully consider the information needs of the

intended audience (e.g. management) and

importantly, how the interpretation of data is

influenced by context.

This paper intends to analyse the barriers in using

data warehousing and big data approaches for BIM in

order to support proper decision analytics manifested

in effective decision-making within BMS.

2 DATA TO INFORMATION:

CONCEPTS AND

APPROACHES

2.1 Building Management Systems and

Their Information Needs

Initially limited to operational and real time, Building

Management Systems nowadays increasingly need to

also make decisions on strategic and tactical levels.

This endeavour can be reasoned about in relation to

the information flow, usage and needs, and optimised

using various types of models. In the following, the

authors will use an example of mainstream systematic

model of decision-making, namely the GRAI Grid

(Doumeingts, Vallespir, & Chen, 2003) (see Fig. 1).

Fundamentally, this generic model identifies

management, command and control tasks at various

levels (identified via time spans called ‘horizons’)

and the information flow between them.

The exogenous and endogenous information

flows feeding the Manage, Command and Control

centre in Fig. 1 illustrate the point that in order to

make successful decisions it is necessary to satisfy the

information needs of the management functions.

Figure 1: Information flows in a Basic mainstream decision

making model: GRAI Grid (Doumeingts, Vallespir, &

Chen, 2003).

This means that the data gathered and analysed

must be meaningful, properly aggregated (level of

detail) and suitably expressed in order to meet the

demands and competencies of each audience

populating the decision centres at various horizons.

This is not a trivial task. To justify this point the

authors refer to two main approaches to data

analytics, namely data warehousing and big data.

2.2 Data Warehousing and Big Data

The concept of Data warehousing is relying on high

quality clean and integrated data to enable the use of

operational databases and other data repositories

snapshots and build an interface enabling the analysis

(‘mining’) in order to identify management-relevant

information. Some authors (Inmon, Zachman, &

Geiger, 1997; Kimball, 1996) argue that such a

facility could be built fast and in an affordable manner

using existing databases and possibly transaction logs

so as to gain management insight (Matte & Rizzi,

2009; Satyanarayana, Srinivasu, Poorna Rao, &

Rikkula, 2010). The aim is to create a narrative that

ICEIS 2020 - 22nd International Conference on Enterprise Information Systems

is characterising the present or predicted future

situation and is essential for decision-making. After

some initial success in creating meaningful insight for

management, data warehousing displayed some

notable failures to deliver on its promises (Adelman

& Moss, 2000).

‘Big data’ is a technology that uses traditional data

analysis and machine learning techniques to derive

useful interpretations based on large and varied data

sources (Gandomi & Haider, 2015; Marr, 2015).

Brought forward by the technological advances in

data gathering (cheaper and more intelligent Internet-

of-Things (IoT)-enabled sensors, cheaper and cloud-

based storage etc.) and initially boasted as the

solution to the problems where traditionally

implemented data warehousing fell short, big data is

still maturing and yet to make significant inroads in

decision support (Horitaa, de Albuquerquea,

Marchezinid, & Mendiondoc, 2016; Kościelniaka &

Putoa, 2015). This is also due to its inherent

dependence on machine learning algorithms that

attempt to predict but cannot adequately explain the

predictions, which is an essential factor in gaining

human trust in decision support systems (Wang &

Benbasat, 2008).

2.3 Shortcomings of Data Warehousing

and Big Data

A first drawback of the two concepts refers to the

associated methodologies, which do not lay enough

emphasis on understanding the fundamental

information needs of the decision maker rather than

rushing to data collection and interpretation (Seen &

Sinha, 2005).

In addition, there is minimal or inexistent

correlation between internal- and external data

sources (i.e., connecting the endogenous and the

exogenous information flows - see Figure 1).

Further on, insufficient effort is put into realising

what data is needed for being able to draw useful

inferences, but is unavailable. Even the recent

approaches proposing to limit the amount of sensor

data used in situation assessment (e.g. by switching

on- or off additional pre-stored sensor data sources)

is in fact relying on the command and control to

pinpoint what data should be taken into account to

possibly change the narrative.

If the above deficiency is identified, then the need

for data that is not available, but is deemed necessary,

may become the source of additional data collection

tasks; however, this can inadvertently result in poor

data quality ((Hazen, Boone, Ezell, & Jones-Farme,

2014; SAS, 2015)).

Another issue is the limited progress in

transforming existing building management

processes to produce the necessary data as a by-

product of the production (or service delivery)

process, instead of requiring additional data entry

(Hazen et al., 2014) (a main source of data quality

issues as shown above).

There has been a tendency to disregard the

collected data context (Corrigan, 2007), thus creating

the danger of situation mis-identification without

even being aware of having committed this mistake

(Santanilla, Zhang, Althouse, & Ayers, 2014).

Another important aspect is the typical reliance of

big data technology on machine learning techniques

producing models whose uncertainty cannot be

adequately assessed and whose predictions cannot be

adequately explained (Kendall & Gal, 2017).

To conclude, the main shortcomings found are:

- On each decision-making level, one must correlate

internal and external data.

- With the opportunity to collect and access very large

amounts of building–related data, the typically low

density of useful content (Li et al., 2017) makes it

difficult to identify patterns that are useful for

decision making (too many patterns identifiable by

algorithms) – unless one uses heuristics (i.e., the

result of prior learning) to discern what is relevant

(note that the measure of relevance may change in

time and with the current interpretation of data).

3 BUILDING INFORMATION

MODELS FOR EFFECTIVE

DECISIONS

3.1 The OODA Loop as an Activity

Network

The tasks that appear in each type and level of

decision-making and the feedback that can be used to

inform the filters used to selectively observe reality

might be studied using a model that explains how

successful decisions are made. This model is part of

the Observe, Orient, Decide and Act (OODA) Loop

devised by John Boyd (Osinga, 2006).

Although some authors such as Benson & Rotkoff

(2011) understand OODA to be a strict sequence of

tasks, this is in fact not true due to the feedback links

inside the high level ‘loop-like’ structure that are

responsible for learning and for decisions about the

kind of filters necessary. Thus, OODA is in fact an

activity network featuring rich information flows

among its activities and the environment.

Situation-aware Building Information Models for Next Generation Building Management Systems

A brief review of Boyd’s OODA concept can be

used to highlight potential development directions for

data warehousing and/or big data methodology for

decision support. Thus, decisions can be made by the

management / command & control system of a BMS,

in any domain of action and on any level or horizon

of management (i.e., strategic, tactical, operational

and real-time, performing four interdependent tasks.

The tasks that appear in each type and level of

decision-making and the feedback that can be used to

inform the filters used to selectively observe reality

may be studied using a model that explains how

successful decisions are made.

Note that this ‘loop’ is often misunderstood to be

a strict sequence of tasks (Benson & Rotkoff, 2011).

OODA is not a strict loop, due to the feedback links

inside the high level ‘loop-like’ structure that are

responsible for learning and for decisions about the

kind of filters necessary. Thus, in fact it is actually an

activity network featuring rich information flows

among the OODA activities and the environment.

This learning has the potential to result in

decisions that also emphasize relevant gaps and thus

initiate capability improvement efforts. Self-reflective

management typically engages in such learning,

comparing the behaviour of the external world and its

requirements on the system (the future predicted

action space) with the action space of the current

system (including the current system’s ability to

sense, orient, decide and act). Note that here, the term

‘action space’ describes the set of possible outcomes

reachable using the system’s current resources

(technical, human, information and financial).

The learning loop is in itself an OODA loop

analogous to the one discussed above, although the

ingredients are different and closely associated with

strategic management (see Figure 2). Thus the

OODA-style questions are in this case: a) what to

observe, b) how to orient to become situation-aware

and c) what is guiding the decision about what to do

(within constraints, decision variables and possible

actions) so as to be able to act. The action space of

this strategic loop consists of transformational actions

(company re-missioning, change of identity, business

model change, capability development, complete

metamorphosis, etc.).

Strategic self-reflection compares the current

capabilities of the system to desired future

capabilities, enabling management to decide whether

the change will affect the system’s capabilities

(including decision making capabilities), the system’s

identity (re-missioning), or both. Note that the

management may also decide to instead

decommission that part of the system due to its

inability to fully perform the system’s mission.

Such transformations are typically implemented

using a separate programme or project using a similar

suitable iterative paradigm, such as the so-called

Plan-Do-Check-Act (PDCA) loop (Lawson, 2006),

possibly in a recursive manner (Schmidt, Elezi,

Tommelein, & Lindemann, 2014) e.g. for complexity

control.

Figure 2: Extended OODA Loop as an activity network (based on (Fadok, Boyd, & Warden, 1995)) featuring additional

Learning and Narratives Loops.

ICEIS 2020 - 22nd International Conference on Enterprise Information Systems

3.2 Consequences for BMS Decision

Support based on Data

Warehousing and Big Data –

Enabled BIM

The above analysis implies that ‘big data’ (meaning

the collective technologies and methods of data

analysis and predictive analytics) has the potential to

enable situational awareness (a condition of

successful action) by delivering a plethora of

previously unavailable domain-level facts and

patterns relevant for decision-making. However, this

data needs to be interpreted, which calls for a theory

of situations resulting in a narrative of what is being

identified or predicted. Without such a narrative,

there is no true situational awareness or trust in the

system, which can substantially limit the chances of

effective action.

It is therefore argued that having the ability to

gather, store and analyse large amounts of data using

only algorithms is not a guarantee that the patterns

thus found in data can be turned into useful and

trustworthy information that forms the basis of

effective decision-making, followed by appropriate

action leading to measurable success.

Importantly, the process is similar the other way

around: when interpreting available data, there can be

multiple fitting narratives; unfortunately, it is quite

difficult to choose the ‘correct’ one. In this case,

adequate means of reasoning with incomplete

information could help articulate a need for new data

(or new types of data) that can resolve the ambiguity.

As a result of the above reasoning, the authors

argue that supporting decision-making based on data

warehousing using ‘big data’ requires the collection

of a second level of data. This ‘second level’ is not

meant to refer to particular facts, but rather to

underpin the creation of an inventory of situation

types, containing facts that must be true, facts that

must be not true, as well as constraints and rules of

corresponding causes and effects. These situation

types can be considered models (or model prototypes)

of the domain, which can be matched against findings

on the observed data level.

Note that due to the changing nature of the

Universe of Discourse, the above-mentioned situation

types are also expected to evolve; therefore, one

should not aim to design and / or construct a facility

that relies on a completely predefined ontology of

situation types. Rather, there is a need for a capability

to continuously improve and extend this type of

knowledge, including the development and learning

of new types, which are not a specialisation of some

known type. This is required in order to ensure that

the ‘world of situations’ remains open, as described

by Goranson and Cardier (2013).

In order to achieve adequate situation awareness

for effective decision making, collected data needs to

be filtered based on relevance (Li et al., 2017;

Szafranski, 1995), dictated by the possible situations

of interest. However, as the current situation is

typically not unambiguously known and changes as

data gathered is interpreted, one will have to maintain

a dynamic narrative (or set thereof) of the situation,

which will continually adjust the data needs (Madden,

2012) as well as what needs to be filtered out, or be

kept. This constitutes yet another OODA loop,

applied to the set of narratives involved in the

interpretation of data for decision making (see

Narratives Loop in Figure 2).

4 DESIGN PRINCIPLES FOR

‘NEXT GENERATION’ BMS

DECISION MAKING

On both existing and emergent system levels,

decision making needs a timely and accurate

narrative that looks behind the ‘observables’. An

essential aspect of agile and effective decision

making (whether on strategic or tactical level) relies

on the ability of the system in question to create and

to continually update its situated insight, thus being

able to deal with uncertainty.

4.1 First Functional Principle: Employ

Situated Reasoning

Consider the following domain-level observations

within the domain of building energy demand at

cluster level and its coupling with complex integrated

energy systems; O1: some buildings within an

‘intelligent’ cluster report increased levels of energy

consumption; O2: occupants of some of the buildings

within the cluster (some overlap with O1) report

reduced thermal comfort, O3: there are no reports of

malfunction. Then, O4: energy consumption goes to

normal however O5: occupants still complain. Then

later on, the situation (O1 – O5) repeats itself.

A domain-level theory could describe energy

supply-demand rules in complex smart building

clusters including e.g. energy consumption

fluctuations depending on various factors.

While it is possible to build a model of the

situation, it is quite difficult and computationally

intensive to know all the intricacies (in space and

time) of such complex interconnected systems within

Situation-aware Building Information Models for Next Generation Building Management Systems

the building cluster (Frayssinet et al., 2018). More

importantly, the information usable to address the

situation with the right decision is incomplete.

Therefore, one does not really know what really

causes the increased energy consumption and thermal

discomfort in the absence of well-identified

malfunctions.

Typically, the Service Entity (SE) attempting to

address the situation must be able to interpret past

observed events and build types of situations (the

candidate interpretations of the events described),

each with their own logic and constraints. Thus, a

competent SE would be familiar with a repertoire of

situation types and their internal logic. The question

is: which one of these can be used as the correct

narrative of the observed events in this case?

The SE can interpret the current situation S based

on known matching situation types S

– e.g. sensor

measurement drift (e.g. due to low quality control or

bugs in embedded software) in some batches and S

e.g. semantic communication problem between

BMS-es within the cluster. Note that there may be a

myriad of situation types S

, some being variations of

a general type.

The SE’s Decision Suppot Sstem (DSS) can

employ situated reasoning in order to find out what

(perhaps very simple) additional fact/s would have to

be discovered to disambiguate between S

and S

and

thus be able to act appropriately.

While such avenues of action may seem rather simple

for humans, the real issue is the potential automation

and optimisation of such behaviour in BMS, based on

interpretation of BIM data. In order to achieve this,

one needs to employ a second functional princciple.

4.2 Second Functional Principle:

Channel Theory for Situation

Awareness

If situations are organised in types and their internal

logic is known (like in the scenario in Section 4.1),

then there exist possible ‘channels’ through which

information in one situation type can be transferred

(possibly in a lossy manner) to another situation type.

A recently popularised mathematical approach of

the above is the category theoretic treatment of

situation theory (Barwise & Perry, 1998; Devlin,

2003; Goranson & Cardier, 2013). The mechanism

that allows the two levels (situation theory and

domain level theory(ies)) to coexist is channel logic

(Barwise & Seligman, 2008) - according to which,

given the category of situations representing situation

types, there is a mapping that regulates the way

complete lines of reasoning can be ‘transplanted’

from one situation type to another.

This transplanting works as follows: when there

exists a logic in a known situation type S

and the

facts suggest that the situation is of a related type S

many (however typically not all) facts and inferences

should also be valid in type S

(see Figure 3).

As a result, if we have a known situation of type

with facts supporting this claim, and we only have

scarce data about another situation type of interest (of

type S

), channel logic allows us to deduce the need

for data that can be used to ‘fill in the details’ about

this second situation of type S

. The resulting

mapping is a so-called morphism between categories

and can be implemented using functional

programming techniques. In Figure 3, this (info-)

morphism is represented by the double-headed

arrows shown carrying information (while possibly

losing some, as previously stated) from one situation

type to another type to another.

In the case described in section 4.1, this morphism

may mean analysing particular sensor manufacturing

data and/or simulating events on the BMS in question

while observing the effects (as the original events

appear to be of a transient and random nature, either

due to random sensor drift or complex unwanted

interaction within BMS software or even sensor

embedded software).

The practical consequence is that the decision

maker can use this analogical reasoning to come to

valid conclusions in an otherwise inaccessible

domain; should this not be possible, it allows to at

least narrow down the need for specific data that can

support a valid conclusion.

The above also illustrates in a simplified way the

ability of the situation theoretic logic to infer that for

decision making, there is a need for specific, but yet

unavailable data that can disambiguate the

interpretation of what is known at the time.

5 CONCLUSIONS AND FURTHER

WORK

In the context of the increasing rate of change and the

resulting flood of data, decisions, even in traditionally

local and non-time-critical domains such as BMS will

have increasingly far-reaching consequences and

need to be promptly made - often, in real time.

The work presented in this paper can be used towards

creating an ongoing situational awareness capability.

The effective use of ‘proven repertoires’ nowadays

increasingly depends on their fast, near-automated

ICEIS 2020 - 22nd International Conference on Enterprise Information Systems

Figure 3: Simplification of Category Theoretic approach of Situation Theory (Noran & Bernus, 2018).

deployment, which is traditionally based on human

tacit skills and knowledge.

The paper has shown how novel paradigms can

assist traditional data analytics approaches in order to

achieve adequate BIM situation awareness and thus

properly support decision-making within complex

Building Management Systems-of-Systems.

Further work will continue to focus on the

principles underpinning situation theory-based

decision-making and related supporting technology.

The results will be used to demonstrate their use in

complex Building Management Systems-of-Systems

within clusters of Intelligent Buildings present in the

emerging Smart City paradigm.

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ICEIS 2020 - 22nd International Conference on Enterprise Information Systems