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Application of graph databases and graph theory concepts for advanced analysing of BIM models based on IFC standard

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In this paper we present a workflow for automatic transformation of IFC schema and IFC models into an IFC Meta and object graph databases. The aim of this research is to study and demonstrate the potential of using graph theory concepts and graph databases in order to manage, visualize and analyse the huge information and complex relationships of BIM models based on IFC standard. For the validation a set data retrieval queries and advanced model analysis for model topology analysis and comparison of different IFC models are carried out in order to demonstrate the flexibility and advantages of the suggested approach.
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Application of graph databases and graph theory concepts for advanced
analysing of BIM models based on IFC standard
Ali Ismail, Ahmed Nahar, Raimar Scherer
TU Dresden, Germany
ali.ismail@tu-dresden.de
Abstract. In this paper we present a workflow for automatic transformation of IFC schema and
IFC models into an IFC Meta and object graph databases. The aim of this research is to study and
demonstrate the potential of using graph theory concepts and graph databases in order to manage,
visualize and analyse the huge information and complex relationships of BIM models based on
IFC standard. For the validation a set data retrieval queries and advanced model analysis for model
topology analysis and comparison of different IFC models are carried out in order to demonstrate
the flexibility and advantages of the suggested approach.
1. Introduction
The very fast development in the sector of information technology has been successfully
exploited in construction and engineering field to adopt new digital methods such as Building
Information Modelling (BIM) for construction project managing. However, BIM models may
contain a huge amount on information with complex relationships between the model entities.
This information could remain inaccessible in several cases due to the use of closed property
formats or the absent of suitable data management tools in case of using open standard
formats like Industry Foundation Classes (IFC).
A lot of data retrieval queries are hard to be accomplished using currently available software’s
and most of them operate on single IFC models. The rigid and complex hierarchical structure
of the IFC schema prevents simple manual extraction of building information and requires
deep understanding of the IFC object model itself. The BIM query languages introduced so
far have certain limitations, particularly with respect to the high level of knowledge about the
IFC object model and about data mapping mechanisms required by the user (Tauscher,
Bargstädt, & Smarsly, 2016).
Evidently, graphs have shown great capabilities in understanding and accessing complex and
rich datasets in many different domains. Graph models are extremely useful for representation
and description of the complex relationships among building elements and data within BIMs
(Isaac, Sadeghpour, & Navon, 2013), hence converting of BIM models based on the IFC
standard into an effective information retrievable model based on graph databases could
significantly facilitate the efforts of exploring and analysing BIM highly connected data.
A graph-based schema, termed the graph data model (GDM) was presented by (Khalili &
Chua, 2015). This schema can be used to employ semantic information, to extract, analyse,
and present the topological relationships among 3D objects in 3D space, and to perform
topological queries faster. Another generic approach towards information retrieval using the
IFC object model based on graph theory was presented recently by (Tauscher, Bargstädt, &
Smarsly, 2016). In this approach a directed graph was generated that serve as semantic data
pools facilitating generic queries. This approach is limited to apply queries on single IFC
models.
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This paper presents a workflow for complete and automatic transformation of IFC models
into a labelled property graph-based model using the world leading graph database Neo4J
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as
a graph database framework.
The proposed approach targets two kinds of graph models:
- IFC Meta Graph (IMG) based on the IFC EXPRESS schema
- IFC Objects Graph (IOG) based on STEP Physical File (SPF) format.
Beside the automatic conversation of IFC models into graph database this paper aims to
demonstrate the potential of using graph databases and concepts of graph theory in order to
(1) explore, check and analyse the complex relationships inside one or multiple BIM models,
(2) run complex queries for information retrieval and (3) carry out advanced analysis of the
building topology like escape route analysis and comparing of different IFC models.
2. IFC data model
IFC is a data model developed by buildingSMART International to support the exchange of
building information in Architecture, Engineering and Construction (AEC) industry to
improve collaboration, scheduling, cost and delivery time activities throughout the whole life
cycle of the building project. It provides an object-oriented and semantic data model for
storing and exchanging building information. This enables actors from different disciplines to
effectively use BIM data and to fully exchange information in construction or facility
management projects. IFC schema is described using EXPRESS specification language that
defined by ISO 10303-11, as conceptual information modelling language. The specification
specified also a graphical representation known as EXPRESS-G to provide graphical subset.
The IFC data schema can be saved as Extensible Markup Language (XML) file structure per
ISO10303-21 or STEP Physical file (SPF) document structure following ISO10303-28.
Each IFC model is composed of IFC entities built up in a hierarchical order, where each IFC
entity includes a fixed number of IFC attributes, plus any number of additional IFC
properties. The IFC attributes are the main identifiers of the entities, while the names of these
attributes are fixed, having been defined by buildingSMART (buildingSMART) as part of the
IFC standard code. The IFC data schema has three fundamental entity types as shown in
Figure 1. These entity types are the first level structure of the IfcRoot entities hierarchical.
Figure 1: Fundamental entity types derived from IfcRoot class
IfcPropertyDefinition describes all characteristics that may attach to objects. Thus,
valuable information can be shared among multiple object instances. However, it may
express the occurrence information of the actual object in the project context, in case
that it is attached to a single object instance.
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www.neo4j.com
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IfcObjectDefinition stands for all handled objects or process. Where, all physical
items and products such as roofs, windows, and slabs that can be touched and seen are
classified as IfcObjectDefinition.
IfcRelationship summarizes all the relationships among objects. This can enable users
saving relationship specific properties directly at the relationship object and avoid
duplication of relationship semantics from the object attributes. The abstract
objectified relationship IfcRelationship and its subtype relationships are responsible
for connectivity among objects, in which several properties can be attached to each
relationship.
Figure 2 demonstrates the different types of objectified relationship within the IFC (Version
2x3) and their abstract super type IfcRelationship.
Figure 2: IFC abstract objectified relationship
IFC classes have direct attributes attached to them which could indicate a relationship to
another object or they could just be attached as simple data type attribute, e.g. integer, string,
logical, or Boolean. Therefore, IFC models distinguish between the attributes that are directly
attached to object as entity attributes, and attributes that assigned to indicate a relationship to
other objects. The second of two attributes are the most adopted approach to extend viable
properties. However, as it is depicted Figure 3, each IFC class could have simple data
attributes with referenced object attributes as in the case of the IfcRoot, or referenced object
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with relationship attributes as in the case of IfcProduct, or entity attributes and relationship
attributes together as in the case of IfcObject.
Figure 3 IFC Entity attributes and relationship attributes
In addition to the direct attached attributes, the objects can inherit attributes from their
supertype classes, where, property pair can be assigned to almost any type of objects and thus
to support enlarging their attributes base.
3. Graph data management
Graph theory is the branch of mathematics that dealing with the study of graphs (Hughes,
2016). While the concept of graphs itself is defined by graph theory as a diagrammatic
representation of real-world scenarios in a form of points and lines (Wilson, 1996). The points
are called vertices or nodes, while the lines that connect them are so-called edges, arcs or
relationships. Each vertex in the graph is represented by drawing a dot or a circle, while the
relationship between each pair of vertices is indicated by drawing an arc or line if they are
connected by an edge.
The application of graphs has become an important technique to describe several scenarios in
the real-world. One of the applications of graphs is to provide a simplified description of
scenarios datasets in a way that produce a useful understanding of a complicated data. This
has led to the birth of a special form of graph model, the so-called labelled property graph
(Robinson, Webber , & Eifrem, 2015 "Second Edition"). Labelled property graphs are similar
to simple graphs; consist of nodes and relationships which are often expressed as vertices and
edges. However, labeled property graphs provide additional characteristics to facilitate graph
understanding, where, nodes could have a single or multiple labels; in addition, they could
have properties (key-value pairs). Relationships can also be named and contain properties
while connecting each two nodes as start and end node.
Several graph processing systems have been developed in the last decade to meet the modern
graph modelling and analysis tasks. Doekemeijer (Doekemeijer & Varbanescu, 2014- PDS-
2014-003) has declared that more than 80 systems have been introduced in the period from
2004 to 2014, by academia and industry sectors together. However, the currently available
systems can be divided into two main kinds, graph databases, and graph processing. In this
section and for the objectives of the present study, we will express the concepts of graph
database systems in general, with a focus on Neo4j graph database system particularly in
some cases. However, all these efforts in the field of graph modelling express the importance
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of graphs for real-world scenarios. Angles (Angles & Gutierrez, February 2008 ) summarized
the advantages of using graphs as modelling mechanism for data management as following:
1. Graphs enable users to model data exactly as they are represented in the real-world
scenario, this can significantly enhance the operations on data. Thus, graphs can keep
all the information about an object in a single node and display the related information
by relationships connected to it.
2. Queries can be developed based on the graph structure. For instance, the finding of the
shortest path can be considered as sub graph from the original graph.
3. Operationally, graphs can be stored efficiently within databases using special graph
storage structures, and functional graph algorithms for application of specific
operations.
4. Methodology and Transformation Workflow
The proposed methodology introduces a workflow to develop and build IFC-based graph
models using IFC EXPRESS schema and IFC models in STEP physical file format. In this
workflow the whole transformation is automated and done through connected web services
(dynamic EXPRESS parser, IFC web data server and web-based graph database import
interface) without the need for any local tools. The transformation supports any valid IFC
schema.
In our approach a single graph database is used to storage various IFC models at the same
time, where each node (object) will has a special attribute to identify the belonged model. In
this way queries and advanced analysis are not limited to one IFC model. This feature allows
to do merging queries on different disciplines BIM models (architecture, structural design,
MEP) of the same project, or to compare different versions of the same model over time.
Figure 4: IFC to graph database conversation workflow
4.1 IFC Meta Graph Model
The workflow starts with analysing the IFC EXPRESS schema and developing a generic
approach to generate an IFC Meta Graph Model (IMG), which represents all IFC classes, their
attributes and the relationships between them (Figure 5).
IMG model will be used later in order to generate and check the relationships between IFC
instance entities and validate IFC models. It can be also used to investigate changes between
different IFC versions (e.g. IFC2X3 vs IFC4) or to run filters and information retrieval
queries for a better understanding of IFC schema and analyzing its complex data connectivity.
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Figure 5 Mapping IFC EXPRESS into a Meta Graph Model
The mapping of IFC EXPRESS model into IMG is carried out through writing a special
server script (in Ruby language) and run it on IFCWebServer.org (Ismail, 2011) which
provides a dynamic EXPRESS parser and web script console. This script generates a set of
Cyber commands, which create the Meta graph inside Neo4j database, where Cypher is a
declarative, SQL-inspired language for describing patterns in graphs visually using an ascii-
art syntax.
Figure 6 shows the scope of mapping for IFC classes, their attributes, data types and the
relationships between classes. Both of IFC classes and the attributes are mapped into nodes
and they are connected through various relationships like “has_property” to connect a class
node with its direct attributes or “subtype_of” to connect a class with its sub-classes.
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Figure 6 Mapping of classes, data types and relationships within IMG model
4.2 IFC Object Graph model
The next step in the workflow is to convert IFC models into IFC Object Graph (IOG). In this
graph each IFC entity expect the relationships will be represented as a node and will hold
information about the class of the entity and its basic attributes and will be connected to other
entities through named relationships.
A special server script based on IFCWebServer.org has been developed in order to automate
the process of extracting IFC data (objects, relationships) and creating all necessary Cypher
commands for the data import and also relationship generation. The script include some
options to exclude certain IFC classes, for example all low level geometry classes.
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Figure 7 Creation of IFC Object Graph
IFC data are extracted and saved at first in CSV format then imported into Neo4j to create the
graph-based models (Figure 7).
The Cypher snippet bellow shows the import command for IfcBuilding.
After importing the IFC data and with help of the Meta graph model the relationships for
referenced, inversed and derived attributes will be created automatically. The scope of
relationships transformation includes all IFC relationships, for example: (1) aggregation, (2)
spatial and element composition, (3) spatial and element decomposition, (4) zone and group
assignment, (5) building system assignment, (6) spatial structure, (7) element and path
connectivity and (8) element filling.
The next step creates direct relationships between graph nodes and their connected
information and deletes all redundant relationship. For example the building element nodes
will be connected directly with their corresponding properties through a
“isDefinedByProperties” relationships and all IfcRelDefinesByProperties and
IfcRelDefinesByType can be deleted. As a result, the graph queries can be simplified by
normalizing the property sets and non-direct attributes of building elements to be assigned as
direct node attributes.
A further step will be assigning each node in the graph with a set of labels of all its parent
classes and running a set of pre-defined queries for further classification and normalization of
the building elements attributes according to their classes and property sets. For example all
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walls which have the property “Load bearing” set to true will be linked into a node “Load
bearing walls” through a relationship “is-load-bearing”.
5. Validation and Case Studies
After converting IFC models into a graph database it can be used as an IFC data server to run
data retrieval queries and advanced analysis of BIM models taking in account the advantages
of applying graph theory concepts and algorithms like path finding and shortest path method.
In the following sections we present some simple and advanced examples.
5.1 Examples of Simple Queries
- Retrieve the assigned property sets of a certain object: This query returns all
propery sets which are assigned to a ceartin object through its globalId value.
Figure 8 getting the assigned PropertySets for a certain object
- Filter objects based on material: This query returns a list of all doors and their
material names. The connection between doors and material nodes is done
automatically by the graph database engine (through IfcDoor[*1..5]-IfcMaterial
relation command) without the need to know how both IfcDoor and IfcMaterial are
related to each other in the IFC schema.
MATCH
(wall:IfcWallStandardCase{Model:'M.ifc',GUID:'3wP5TgnCHEPwsgi3SxIcXs'})-
[rel]-(property:IfcPropertySet)
RETURN DISTINCT wall,rel,property
MATCH (door:IfcDoor{Model:'Muster003.ifc'})-[*1..5]-
(material:IfcMaterial)
RETURN DISTINCT (material.IFCID)AS IFCID,(material.name)AS Material_name
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MATCH p=(d:IfcDoor{IFCID:'9824',Model:'Muster003.ifc'})-[r:BoundedBy]-
>(s:IfcSpace)
RETURN p
UNION
MATCH p=(s1:IfcSpace{Model:'Muster003.ifc'})-
[r1:BoundedBy]-(d1:IfcDoor)-[r2:BoundedBy]-(s2:IfcSpace)
WHERE s1.IFCID > s2.IFCID
RETURN p
UNION
MATCH p=(s1:IfcSpace {Model:'Muster003.ifc'}) <-[r1:RelatingSpace]-
(r:IfcRelSpaceBoundary)-[r2:RelatingSpace]->(s2:IfcSpace)
WHERE s1.IFCID > s2.IFCID RETURN p
- Create new relationships to assign objects to layers: This query creats the
relationship “assignedItem” between defined layers in the model and their
coresponding elements.
Figure 9 Assign objects to layers
5.2 Analysis of BIMs for Emergency Routes
In this example we used the graph database to do a simplified topology analysis of BIM
models in order to analyse and generate emergency routes. The construction of possible
escape paths is done in 2 steps:
(1) Through the relationship “BoundedBy” between IfcSpace and IfcDoor entities
(2) Through the relationship “RelatingSpace” between 2 IfcSpace neighbour objects.
MATCH (n:IfcPresentationLayerAssignment{Model: "Office_A.ifc"})
UNWIND split(replace(replace(n.assignedItems,"(",""),")",""),",") as o
MERGE (assignedItems {Model: "Office_A.ifc",IFCID: replace(o,"#","")})
MERGE (n)-[:assignedItems]->(assignedItems);
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MATCH (W30 { Model: 'Week30.ifc' }),(W37 { Model: 'Week37.ifc' })
WHERE W30.tag = W37.tag
WITH collect(distinct W30.tag) as tags
MATCH (new) WHERE new.tag <> "" AND NOT new.tag IN tags
AND new.label = "IfcWindow"
RETURN distinct new.label as Class, new.globalId, new.IFCID as
STEPID,new.name, new.tag ORDER BY new.tag
Figure 10 Creation of escape routes through graph query
5.3 Compare Different IFC Models
The following example compares 2 versions of IFC models for the same building and returns a list of
all windows which have been added in the second model.
Figure 11 Compare IFC models using graph query
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6. Conclusions and Future Work
This paper presented a workflow for automatic transformation of IFC schema and models into
a property graph database. The suggested approach have been applied on many IFC
schema/models in order to explore the capabilities of the IFC-based graph models for data
query and advanced analysis of the BIM models.
The current scope of transformation and queries doesn’t take in account the geometry
information or the process of creating geometry objects based on parameters or Boolean
operations. A future research is needed in order to include the geometry information to be a
part of the pre-defined queries by developing an interface between the graph database and an
IFC geometry engine.
Future research could involve developing a user friendly web application for editing the
information of graph database and developing an IFC export interface for sub-models or
merging models. The future work will include also developing special stored procedures
which allow accessing the Java API of Neo4j directly and running the import and data
retrieval queries much faster compared with solely using of Cypher commands and followed
with a benchmark to compare the performance with other existing query approaches.
Graph models can be considered a satisfactory tool to manage building information. However,
writing user-defined queries for users without IFC and graph skills still quite challenging
where advanced queries should be written by IFC and graph DB experts. This research can be
considered as an initial step in the direction of IFC-based graph models formation
management.
References
Angles, R., & Gutierrez, C. (2008 ). Survey of Graph Database Models. ACM Computing Surveys, 40(1).
Doekemeijer, N., & Varbanescu, A. (2014). A Survey of Parallel Graph Processing Frameworks. Delft: Parallel
and Distributed Systems Group- Delft University of Technology.
Hughes, J. (2016). ECE 3020 Mathematical Foundations of Computer Engineering. Atlanta: Lecture notes from
Georgia Institute of Technology.
Isaac, S., Sadeghpour, F., & Navon, R. (2013). Analyzing Building Information using Graph Theory.
International Association for Automation and Robotics in Construction (IAARC)- 30th ISARC, (S. 1013-1020).
Montreal.
Ismail, A. (2011). IFCWebServer. IFC Data model server and online viewer: http://ifcwebserver.org
Khalili, A., & Chua, D. (2015). IFC-Based Graph Data Model for Topological Queries on Building Elements .
American Society of Civil Engineers, Vol.29 Issue3.
Robinson, I., Webber , J., & Eifrem, E. (2015). Graph Databases. Sebastopol: O’Reilly Media.
Tauscher, E., Bargstädt, H.-J., & Smarsly, K. (2016). Generic BIM queries based on the IFC object model using
graph theory. The 16th International Conference on Computing in Civil and Building Engineering. Osaka,Japan.
Wilson, R. (1996). Introduction to Graph Theory (Fourth edition Ausg.). Harlow- England: Longman Group Ltd.
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Although Building Information Modeling (BIM) is widely adopted in the Architecture, Engineering, and Construction (AEC) industry, difficulties in model interoperability remain, hindering domain-specific applications. Semantic enrichment supplements a model's semantic information automatically as an alternative approach to solve the problem. In this research, a part of the EU funded 'Cloud-BIM' project, we applied a novel deep learning technique, graph neural networks (GNNs), to enrich a BIM model, with the goal of testing the applicability of graph representation and the use of GNNs for semantic enrichment. The application scenario concerned classification of room types in apartments. An apartment layout graph dataset containing seven room types and room relationships was compiled from 224 apartment layouts. A high-performance GNN model, GraphSAGE, was selected to train and predict room classes. Despite the small dataset and the sparse relationship features used in the experiment described here, GraphSAGE succeeded in classifying room types with an accuracy of 73%. This was 12% higher accuracy than two non-graph machine learning algorithms with which it was compared, and the prediction ability was better balanced. The findings provide clear directions for significantly improving the degree of accuracy by expanding the feature set of both node and edge properties, and this is the subject of ongoing work to refine the use of GNNs for semantic enrichment of BIM models.
Conference Paper
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In the architecture, engineering and construction industry, building information modeling (BIM) has emerged as a viable means to support information exchange and interoperability through open standards. As a technical basis, the Industry Foundation Classes (IFC), an open, platform-independent ISO standard, are commonly used as a collaboration format for building information modeling. With recent advances in BIM technologies, substantial research efforts have been devoted to the development of formal IFC-based query languages supporting the retrieval of building information (such as parametric information, structural information, or management information). However, existing approaches towards IFC-based BIM query languages are usually based on model structures requiring predefined queries or schema-based filters, which imply a profound knowledge about the IFC object model. This paper presents a generic approach towards information retrieval using the IFC object model based on graph theory. Specifically, the internal relations of the IFC object model are used to generate directed graphs that serve as semantic data pools facilitating generic queries. Using a prototype software framework, the results of several queries are checked for their correctness, proving the proposed approach. Without requiring a high level of user-side knowledge about the IFC model on the user side, the proposed approach may serve as a basis to BIM query languages becoming more user-friendly and efficient, as compared to existing solutions.
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Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models took off in the eighties and early nineties alongside object-oriented models. Their influence gradually died out with the emergence of other database models, in particular geographical, spatial, semistructured, and XML. Recently, the need to manage information with graph-like nature has reestablished the relevance of this area. The main objective of this survey is to present the work that has been conducted in the area of graph database modeling, concentrating on data structures, query languages, and integrity constraints.
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The adoption of building information modeling (BIM) in construction has led to greater integration of architecture, engineering, construction/facility management (AEC/FM) stakeholders at the project design stage; the result being the incorporation of new complex tasks into construction applications. However, conventional two-dimensional (2D) and nonsemantic three-dimensional (3D) models cannot handle the topological analysis of 3D objects that is required by BIM, especially with regard to building elements. This article describes a new schema, termed the graph data model (GDM) that can be used to employ semantic information, to extract, analyze, and present the topological relationships among 3D objects in 3D space, and to perform topological queries faster. This GDM uses weighted graph principles for simplicity and incorporates an industry foundation classes (IFC)-based algorithm for automatic deduction of topological relationships. A prototype of GDM is implemented in a C# platform and verified using two types of queries in a case study of a commercial building.
A Survey of Parallel Graph Processing Frameworks
  • N Doekemeijer
  • A Varbanescu
Doekemeijer, N., & Varbanescu, A. (2014). A Survey of Parallel Graph Processing Frameworks. Delft: Parallel and Distributed Systems Group-Delft University of Technology.
Analyzing Building Information using Graph Theory. International Association for Automation and Robotics in Construction (IAARC)-30th ISARC
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Isaac, S., Sadeghpour, F., & Navon, R. (2013). Analyzing Building Information using Graph Theory. International Association for Automation and Robotics in Construction (IAARC)-30th ISARC, (S. 1013-1020).
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Ismail, A. (2011). IFCWebServer. IFC Data model server and online viewer: http://ifcwebserver.org
ECE 3020 Mathematical Foundations of Computer Engineering. Atlanta: Lecture notes from Georgia Institute of Technology
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Hughes, J. (2016). ECE 3020 Mathematical Foundations of Computer Engineering. Atlanta: Lecture notes from Georgia Institute of Technology.