Business Intelligence and Process Modelling

Business Intelligence and Process Modelling F.W. Takes Universiteit Leiden Lecture 7: Network Analytics & Process Modelling Introduction BIPM Lecture 7: Network Analytics & Process Modelling Introduction 1 / 54

Where are we? Business Intelligence: anything that aims at providing actionable information that can be used to support business decision making Business Analysis Business Analytics Visual Analytics Descriptive Analytics Predictive Analytics Network Intelligence: Network Science in a BI context Process Modelling BIPM Lecture 7: Network Analytics & Process Modelling Introduction 2 / 54

Network Science for BI BIPM Lecture 7: Network Analytics & Process Modelling Introduction 3 / 54

Data Network Science (recap) Data Data Analysis Data Mining Data Science Big Data Network science: analyzing big structured data consisting of objects connected via certain relationships, in short: networks Interest from: mathematics, computer science, physics, biology, public administration, social sciences,... BIPM Lecture 7: Network Analytics & Process Modelling Introduction 4 / 54

Notation (recap) Concept Symbol Network (graph) G = (V, E) Objects (nodes/vertices) Relations (links/edges) Directed E V V Undirected Number of nodes V Number of edges E We assume no self-edges (u, u) and no parallel edges V E n m BIPM Lecture 7: Network Analytics & Process Modelling Introduction 5 / 54

Small World Networks (recap) 1 Sparse networks density 2 Fat-tailed power-law degree distribution degree 3 Giant component components 4 Low pairwise node-to-node distances distance Many real-world networks: communication networks, citation networks, collaboration networks (Erdös, Kevin Bacon), protein interaction networks, information networks (Wikipedia), webgraphs, financial networks (Bitcoin)... BIPM Lecture 7: Network Analytics & Process Modelling Introduction 6 / 54

Topics Graph Representation and Structure Paths and Distances Graph Evolution, Link Prediction Spidering and Sampling Centrality Visualization Algorithms and Tools Graph Compression Community Detection Contagion, Gossipping and Virality Privacy, Anonymity and Ethics BIPM Lecture 7: Network Analytics & Process Modelling Introduction 7 / 54

Network evolution Graphs evolve over time Social networks: users join the network and create new friendships Webgraphs: new pages and links to pages appear on the internet Scientific networks: new papers are being co-authored and new citations are made in these papers Interesting: small world properties emerge and are preserved during evolution! BIPM Lecture 7: Network Analytics & Process Modelling Introduction 8 / 54

Evolving graphs Graph G t = (V t, E t ) Time window 0 t T 1 Usually at t = 0, either V 0 = and a new edge may bring new nodes, or V 0 = V T 1 and only edges are added at each timestamp Timestamp on node v V : t(v) [0; T 1] Timestamp on edge e E: t(e) [0; T 1], or as common input format: e = (u, v, t (u,v) ) with u, v V and t (u,v) [0, T 1] u v t as line contents of an edge list file BIPM Lecture 7: Network Analytics & Process Modelling Introduction 9 / 54

LIACS collaboration network (v2012) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 10 / 54

Two schools Synthetic graphs model-driven Model or algorithm to generate graphs from scratch Tune parameters to obtain a graph similar to an observed network Statistical analysis Real-world graphs data-driven Obtain data from an actual network Compute and derive properties and determine similarity with other networks Computational analysis BIPM Lecture 7: Network Analytics & Process Modelling Introduction 11 / 54

Apple collaboration network http://www.kenedict.com/apples-internal-innovation-network-unraveled/ BIPM Lecture 7: Network Analytics & Process Modelling Introduction 12 / 54

Link prediction Link prediction problem: given a network G t = (V t, E t ), denoting the network at time t, predict the newly formed links in the evolved network G t = (V t, E t ) at time t > t, i.e., predict the contents of E t \E t. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 13 / 54

Link prediction Link prediction problem: given a network G t = (V t, E t ), denoting the network at time t, predict the newly formed links in the evolved network G t = (V t, E t ) at time t > t, i.e., predict the contents of E t \E t. Applicable to weighted and unweighted, directed and undirected networks Supervised learning problem Features based on the structure of the network Train on first 95%, test on last 5% (randomized) Validate result using AUROC J.E. van Engelen, H.D. Boekhout and F.W. Takes, Explainable and Efficient Link Prediction in Real-World Networks (working paper), 2016. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 13 / 54

Feature set goals efficient in terms of time complexity; accurate in its future link predictions; explainable in its performance based on simple features; consistent in its accuracy relative to larger feature sets across networks; generic, yielding reliable results across a broad range of networks. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 14 / 54

Link prediction features Compute features for each possible future edge (i, j) / E t Node features: degree, volume (total weight) Neighborhood features: neighbor count, common neighbor count, transitive common neighborhood, Jaccard coefficient, preferential attachment, and others Path features: shortest path length, number of shortest paths, restricted Katz measure, and others BIPM Lecture 7: Network Analytics & Process Modelling Introduction 15 / 54

Efficient Feature Set (EFS) Large number of features Black box type of approach Cover individual, local and global properties Explainable result Efficient Feature Set BIPM Lecture 7: Network Analytics & Process Modelling Introduction 16 / 54

Node features Feature Variant Complexity EFS Degree (source) - O(1) Degree (source) d in O(1) Degree (source) d out O(1) Degree (target) - O(1) Degree (target) d in O(1) Degree (target) d out O(1) Volume (source) - O(m/n) Volume (source) d in O(m/n) Volume (source) d out O(m/n) Volume (target) - O(m/n) Volume (target) d in O(m/n) Volume (target) d out O(m/n) Neighbourhood features Total neighbours - O(m/n) Total neighbours Γ in O(m/n) Total neighbours Γ out O(m/n) Common neighbours - O(m/n) Common neighbours Γ in O(m/n) Common neighbours Γ out O(m/n) Transitive comm. neigh. - O(m/n) Jaccard Coeff. - O(m/n) Jaccard Coeff. Γ in O(m/n) Jaccard Coeff. Γ out O(m/n) Transitive Jacc. Coeff. - O(m/n) Adamic/Adar - O(m/n) Preferential attachment - O(1) Preferential attachment Γ in O(1) Preferential attachment Γ out O(1) Opposite direction link - O(1) Path features Shortest path length - O(m + n) Num. shortest paths l max = 3 O(m + n) Restricted Katz measure l max = 3, O(m + n) β = 0.05 PropFlow l max = 3 O(m + n)

Datasets Table : Characteristics of network data sets used for testing Data set Nodes Links CC Type Dist 3N digg 30,398 86,404 0.01 + D 4.68 45% fb-links 63,731 817,035 0.22 - U 4.31 88% fb-wall 46,952 274,086 0.11 + D 5.71 61% infectious 410 2,765 0.46 + U 3.57 83% liacs 1,036 4,650 0.84 + U 3.86 100% lkml-reply 27,927 242,976 0.30 + D 5.19 99% slashdot 51,083 131,175 0.02 + D 4.59 75% topology 34,761 107,720 0.29 + U 3.78 97% ucsocial 1,899 20,296 0.11 + D 3.07 99% wikipedia 100,312 746, 114 0.21 - D 3.83 89% BIPM Lecture 7: Network Analytics & Process Modelling Introduction 18 / 54

Experiments Large candidate set of size ( V V 1 ) E Restrict based on maximum distance a new edge bridges Class imbalance Randomly leave out edges in training to get to 9 : 1 ratio Measure result using AUROC Determine difference between All features, Node features, Neighborhood Features and EFS BIPM Lecture 7: Network Analytics & Process Modelling Introduction 19 / 54

Results BIPM Lecture 7: Network Analytics & Process Modelling Introduction 20 / 54

Results Features digg fb-links fb-wall infectious liacs lkml slashdot topology ucsocial wikipedia All 0.830 0.933 0.887 0.967 0.997 0.975 0.928 0.967 0.913 0.970 Node 0.827 0.700 0.710 0.955 0.969 0.971 0.922 0.949 0.911 0.941 Neighbourhood 0.761 0.911 0.866 0.794 0.986 0.974 0.920 0.961 0.920 0.926 Path 0.632 0.897 0.819 0.579 0.979 0.925 0.777 0.940 0.673 0.827 EFS 0.825 0.930 0.876 0.958 0.995 0.973 0.921 0.965 0.910 0.967 EFS Performance 99.4% 99.6% 98.8% 99.1% 99.8% 99.8% 99.2% 99.8% 99.7% 99.7% Table : AUROC for each network and each set of features. EFS Performance lists performance of EFS relative to All features. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 21 / 54

Conclusions Network science treats data as an annotated set of objects and relationships The structure of the network provides new insights in the data Centrality measures are able to identify prominent actors in the network solely based on its structure Community detection algorithms reveal groups and clusters based on the network structure Link prediction is a form of predictive analytics in network data BIPM Lecture 7: Network Analytics & Process Modelling Introduction 22 / 54

Process Modelling BIPM Lecture 7: Network Analytics & Process Modelling Introduction 23 / 54

Recap Business Intelligence Process Modelling Business process modelling Modelling languages Process discovery Applications in financial industry BIPM Lecture 7: Network Analytics & Process Modelling Introduction 24 / 54

Business Process Management (recap) Process: a set of related actions and transactions to achieve a certain objective Business process: a sequence of activities aimed at producing something of value for the business (Morgan02) Management processes Operational processes Supporting processes Business Process Management: the discipline that combines knowledge from information technology and knowledge from management sciences and applies this to operational business processes (v.d. Aalst) Extension of WorkFlow Management (WFM) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 25 / 54

Business Process Modelling (recap) Business Process Model: abstract representation of business processes, functionality is: Descriptive: what is actually happening? Prescriptive: what should be happening? Explanatory: why is the process designed this way? In practice: formalize and visualize business processes Process Discovery: derive the process from a description of activities Process Mining: the task of converting event data into process models (discovery, conformance, enhancement) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 26 / 54

Why Model Processes? (recap) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 27 / 54

Classical BPM Lifecycle (recap) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 28 / 54

Process Mining (recap) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 29 / 54

Business Process... Intelligence? M. Castellanos et al., Business process intelligence, Handbook of research on business process modeling, pp. 456 480, 2009. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 30 / 54

Process Modelling Informal models: used for discussion and documentation (process descriptions) Formal models: used for analysis or enactment Petri Nets today PN Business Process Model Notation later BPMN BIPM Lecture 7: Network Analytics & Process Modelling Introduction 31 / 54

Business Process Model Notation BIPM Lecture 7: Network Analytics & Process Modelling Introduction 32 / 54

Petri Nets BIPM Lecture 7: Network Analytics & Process Modelling Introduction 33 / 54

Event logs (1) Case ID Event ID dd-mm-yyyy:hh.mm Activity Resource Costs 1 35654423 30-12-2010:11.02 register request Pete 50 1 35654424 31-12-2010:10.06 examine thoroughly Sue 400 1 35654425 05-01-2011:15.12 check ticket Mike 100 1 35654426 06-01-2011:11.18 decide Sara 200 1 35654427 07-01-2011:14.24 reject request Pete 200 2 35654483 30-12-2010:11.32 register request Mike 50 2 35654485 30-12-2010:12.12 check ticket Mike 100 2 35654487 30-12-2010:14.16 examine casually Sean 400 2 35654488 05-01-2011:11.22 decide Sara 200 2 35654489 08-01-2011:12.05 pay compensation Ellen 200 3 35654521 30-12-2010:14.32 register request Pete 50 3 35654522 30-12-2010:15.06 examine casually Mike 400 3 35654524 30-12-2010:16.34 check ticket Ellen 100 3 35654525 06-01-2011:09.18 decide Sara 200 3 35654526 06-01-2011:12.18 reinitiate request Sara 200 3 35654527 06-01-2011:13.06 examine thoroughly Sean 400 3 35654530 08-01-2011:11.43 check ticket Pete 100 3 35654531 09-01-2011:09.55 decide Sara 200 3 35654533 15-01-2011:10.45 pay compensation Ellen 200 4 35654641 06-01-2011:15.02 register request Pete 50 4 35654643 07-01-2011:12.06 check ticket Mike 100 4 35654644 08-01-2011:14.43 examine thoroughly Sean 400 4 35654645 09-01-2011:12.02 decide Sara 200 4 35654647 12-01-2011:15.44 reject request Ellen 200... Table : Event logs of a helpdesk handling customer compensations BIPM Lecture 7: Network Analytics & Process Modelling Introduction 34 / 54

Event logs (2) Case ID Event ID dd-mm-yyyy:hh.mm Activity Resource Costs... 5 35654711 06-01-2011:09.02 register request Ellen 50 5 35654712 07-01-2011:10.16 examine casually Mike 400 5 35654714 08-01-2011:11.22 check ticket Pete 100 5 35654715 10-01-2011:13.28 decide Sara 200 5 35654716 11-01-2011:16.18 reinitiate request Sara 200 5 35654718 14-01-2011:14.33 check ticket Ellen 100 5 35654719 16-01-2011:15.50 examine casually Mike 400 5 35654720 19-01-2011:11.18 decide Sara 200 5 35654721 20-01-2011:12.48 reinitiate request Sara 200 5 35654722 21-01-2011:09.06 examine casually Sue 400 5 35654724 21-01-2011:11.34 check ticket Pete 100 5 35654725 23-01-2011:13.12 decide Sara 200 5 35654726 24-01-2011:14.56 reject request Mike 200 6 35654871 06-01-2011:15.02 register request Mike 50 6 35654873 06-01-2011:16.06 examine casually Ellen 400 6 35654874 07-01-2011:16.22 check ticket Mike 100 6 35654875 07-01-2011:16.52 decide Sara 200 6 35654877 16-01-2011:11.47 pay compensation Mike 200 Table : Event logs of a support desk handling customer compensations BIPM Lecture 7: Network Analytics & Process Modelling Introduction 35 / 54

Simplified event log Case ID Trace 1 a, b, d, e, h 2 a, d, c, e, g 3 a, c, d, e, f, b, d, e, g 4 a, d, b, e, h 5 a, c, d, e, f, d, c, e, f, c, d, e, h 6 a, c, d, e, g Table : Simplified event log of a support desk handling customer compensations (a = register request, b = examine thoroughly, c = examine casually, d = check ticket, e = decide, f = reinitiate request, g = pay compensation, h = reject request) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 36 / 54

Simplified event log Case ID Trace 1 a, b, d, e, h 2 a, d, c, e, g 3 a, c, d, e, f, b, d, e, g 4 a, d, b, e, h 5 a, c, d, e, f, d, c, e, f, c, d, e, h 6 a, c, d, e, g Table : Simplified event log of a support desk handling customer compensations (a = register request, b = examine thoroughly, c = examine casually, d = check ticket, e = decide, f = reinitiate request, g = pay compensation, h = reject request) In short: { a, b, d, e, h, a, d, c, e, g, a, c, d, e, f, b, d, e, g, a, d, b, e, h, a, c, d, e, f, d, c, e, f, c, d, e, h, a, c, d, e, g } BIPM Lecture 7: Network Analytics & Process Modelling Introduction 36 / 54

Example (1) Case ID Trace 1 a, b, d, e, h 2 a, d, c, e, g 3 a, c, d, e, f, b, d, e, g 4 a, d, b, e, h 5 a, c, d, e, f, d, c, e, f, c, d, e, h 6 a, c, d, e, g BIPM Lecture 7: Network Analytics & Process Modelling Introduction 37 / 54

Example (2) Figure : Petri net based on event log { a, b, d, e, h, a, d, b, e, h } BIPM Lecture 7: Network Analytics & Process Modelling Introduction 38 / 54

Play in BIPM Lecture 7: Network Analytics & Process Modelling Introduction 39 / 54

Play out BIPM Lecture 7: Network Analytics & Process Modelling Introduction 40 / 54

Replay BIPM Lecture 7: Network Analytics & Process Modelling Introduction 41 / 54

Replay Connecting models to real events is crucial Possible uses Conformance checking Repairing models Extending the model with frequencies and temporal information Constructing predictive models Operational support (prediction, recommendation, etc.) BIPM Lecture 7: Network Analytics & Process Modelling Introduction 42 / 54

Petri Nets BIPM Lecture 7: Network Analytics & Process Modelling Introduction 43 / 54

Automata (remember?) Finite automaton FA = (Q, Σ, q o, A, δ) Q is a finite set of states Σ is a finite alphabet of input symbols q o Q is the initial state A Q is the set of accepting states δ : Q Σ Q is the transition function BIPM Lecture 7: Network Analytics & Process Modelling Introduction 44 / 54

Automata (remember?) Finite automaton FA = (Q, Σ, q o, A, δ) Q is a finite set of states Σ is a finite alphabet of input symbols q o Q is the initial state A Q is the set of accepting states δ : Q Σ Q is the transition function Figure : Deterministic Finite Automaton for the function x mod 3 BIPM Lecture 7: Network Analytics & Process Modelling Introduction 44 / 54

Petri Nets Petri net N = (P, T, F ) P is a finite set of places T is a finite set of transitions F (P T ) (T P) is a finite set of directed arcs called the flow relation BIPM Lecture 7: Network Analytics & Process Modelling Introduction 45 / 54

Labeled Petri Nets Petri net N = (P, T, F, A, l) P is a finite set of places T is a finite set of transitions F (P T ) (T P) is a finite set of directed arcs called the flow relation A is a set of activity labels l : T A is a labeling function BIPM Lecture 7: Network Analytics & Process Modelling Introduction 46 / 54

Enabling A transition is enabled if each of its input places contains at least one token BIPM Lecture 7: Network Analytics & Process Modelling Introduction 47 / 54

Firing An enabled transition can fire (i.e., it occurs), consuming a token from each input place and producing a token for each output place. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 48 / 54

Petri Nets Connections are directed No connections between two places or two transitions Places may hold zero or more tokens At most one arc between nodes (for now) Firing is atomic Multiple transitions may be enabled, but only one fires at a time During execution, the number of tokens may vary if there are transitions for which the number of input places is not equal to the number of output places The network is static BIPM Lecture 7: Network Analytics & Process Modelling Introduction 49 / 54

Example (1) Petri net for a traffic light BIPM Lecture 7: Network Analytics & Process Modelling Introduction 50 / 54

Example (1) Petri net for a traffic light States: red, orange and green BIPM Lecture 7: Network Analytics & Process Modelling Introduction 50 / 54

Example (1) Petri net for a traffic light States: red, orange and green Transitions from red to green, green to orange, and orange to red BIPM Lecture 7: Network Analytics & Process Modelling Introduction 50 / 54

Example (1) Petri net for a traffic light States: red, orange and green Transitions from red to green, green to orange, and orange to red BIPM Lecture 7: Network Analytics & Process Modelling Introduction 50 / 54

Example (2) Petri net for 2 traffic lights BIPM Lecture 7: Network Analytics & Process Modelling Introduction 51 / 54

Example (2) Petri net for 2 traffic lights BIPM Lecture 7: Network Analytics & Process Modelling Introduction 51 / 54

Example (3) Petri net for 2 traffic lights BIPM Lecture 7: Network Analytics & Process Modelling Introduction 52 / 54

Lab session Continue with Assignment 2 Do the pandas, scikit-learn and Algorithmia tutorials Create features Machine learning Implement (a small part of) your data mining algorithm on Algorithmia, and add it to your dashboard Write the (scientific!) report for the assignment Start reading relevant book chapters... BIPM Lecture 7: Network Analytics & Process Modelling Introduction 53 / 54

Credits Lecture based on slides belonging to the course book W. van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer, 2011. BIPM Lecture 7: Network Analytics & Process Modelling Introduction 54 / 54