Cédric Méaux

Giampiero Mancinelli

Justine Serrano

Aoife Bharucha

Diogo Boito

"Rare heavy mesons decays to leptons"

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

We are focusing on the mode

D + → π + c W u b b g q q ρ 0 , ω 0 , φ ... γ q 2 = m 2 [Grinstein]
Rare heavy mesons decays to leptons Cédric Méaux

Phenomenology project on D → π : Framework

• Multiscale problem (µ W , µ b , µc , Λ QCD ) ⇒ Weak
q ρ 0 , ω 0 , φ ... γ EFT ⇒ c u C i q L(q 2 ) q γ
This leads to an effective hamiltonian :

H eff = G F √ 2 q=b,d V * cq Vuq CKM elements   i C i Wilson coefficients Operators O i +C i O i  
whose amplitude can schematically be written (QCD factorization approach) : Phenomenology project on D → π : Work in progress...

π + | H eff | D + = C • ff + Φ D + ⊗ T ⊗ Φ π +
π 3 R(q 2 ) = Im[L](q 2 ) dispersion relation → L(q 2 ) (
d 2 Γ
dq 2 d cos θ (D + → π + µµ) = a(q 2 ) + b(q 2 ) cos θ + c(q 2 ) cos 2 θ depends on C 7 , C 9 and NP : C 10 , C S , C P , C T , C T 5

The branching ratio spectrum :

Prospects :

Define observables sensitive to NP that could be improved by our work thanks to :

• QCD factorization approach • Resonances modelling technique Estimate the uncertanties from our predictions (and compare it to previous work).

Rare heavy mesons decays to leptons Cédric Méaux

Experimental project on B 0 (s) → τ + τ -: Introduction

• B 0 (s) → are golden channels, theoretically clean and with clear experimental signature.

B(B 0 s → µµ) = (3.0 ± 0.6± +0.3 -0.2 ) × 10 -9 in agreement with SM [LHCB-PAPER-2017-001]
• Nevertheless, several models predict BR higher than SM for τ lepton [ arXiv 1505.05164 , 1609.09078]

• But the τ lepton final state is more challenging :

B 0 s → τ + τ - B 0 → τ + τ - SM 7.7 × 10 -7 2.2 × 10 -8 Limit at 95% C.L. < 6.8 × 10 -3 < 2.1 × 10 -3
Best world limit set on (3π, 3π) final state with LHCb Run 1 data (Lumi 3fb -1 ) [LHCB-PAPER-2017-003]

2 axes to improve these limits : 1 Explore another channel with Run 1 data :

B 0 (s) → τ (3π ± )τ (µ) 2 Run 2 data (Lumi 7fb -1 , energy × 2)
Experimental project on B 0 (s) → τ + τ -: Future Back-Up

Back-Up

Rare heavy mesons decays to leptons Cédric Méaux

Experimental project on B 0 (s) → τ + τ -: Axe 1 : (3π, µ) Run1, Workflow Workflow, inspired from the (3π, 3π) Run 1 analysis :

1 Selection : Loose cut-based + BDT-based Based on kinematical, geometric variables and custom-made variables, mainly isolation variables.

2 1D binned Maximum Likelihood fit :

• Fit a new BDT output for data in the signal region.

• Fit error for zero signal events estimated via a toys study.

3 Normalisation : channel :

B 0 → D + (K -π + π + )π - B(B 0 (s) → τ + τ -) = N obs τ τ tot τ τ N B 0 ≡ α • N obs

ν••

  In the Standard Model (SM), Flavour Changing Neutral Current (FCNC) processes are forbidden at tree level ⇒ They can proceed only via loops.• The rare (B ∼ 10 -6 ) decays b → s + -( = e, µ, τ ) and c → u + -(B ∼ 10 -9 ) are FCNC processes that proceed via box and penguin diagrams in SM : Rare heavy mesons decays to leptons Cédric Méaux processes that proceed via box and penguin diagrams in SM : In new physics (NP) beyond the SM, new particles can enter into the loop diagram or generate new diagrams. FCNC meson decays can be sensitive probes of NP and allows us to test scales potentially beyond the direct searches at LHC Motivation : The FCNC anomalies • The three most significant b → s deviations from SM : R K 0( * ) = B(B 0 →K 0( * ) µ + µ -) B(B 0 →K 0( * ) e + e -) Angular observable P 5 in B * → K * µµ In this light, I am working on 2 projects : Experimental : Search for B 0 (s) → τ + τ - • Purely leptonic b → s transition • CPPM, LHCb Phenomenology : Improve predictions for D → π • Rare c → u transition • CPT and D. Boito (São Paulo University) Phenomenology project on D → π : Introduction Predictions of c → u meson decay are more challenging than for b → s decays : 1) Factorization doesn't work as well as for B decays. 2) Because of resonances entering the quark loop in one of the leading order diagram. ⇒ For D decay, we need to model these resonances.

  Effective Field Theory (EFT) : Integrate out heavy fields (b, W, NP particles...) wrt the scale of interest µ ∼

  ff stands for form factors (parametrize the role of quarks within hadrons) Φ are the light-cone distribution amplitudes • QCD factorization inspired from B decay [ Beneke, Feldmann, Seidel, arXiv 0106067] Used first time for D decays in [Feldmann, Muller, Seidel, arXiv 1705.05891] But doesn't work as well as for B decays since expansion in Λ QCD mc instead of Λ QCD m b Leading Order : Next-to-Leading Order form factor correction : Next-to-Leading Order hard spectator scattering correction : marks the possible insertions of the virtual photon line. Phenomenology project on D → π : Treatment of resonances In [De Boer, Hiller, arXiv 1510.00311] and [Fajfer, Kosnik, arXiv 1510.00965], resonances are treated by simple Breit-Wigner peaks. Our strategy : Improve resonances modelization by using a Shifman model, inspired from [Feldmann, Muller, Seidel, arXiv 1705.05891] Resonances modelled by an infinite tower of equidistant vector resonances [Shifman, hep-0009131] Parameters of the model extracted from a fit to e + e -experimental data R by virtue of the optical theorem, inspired from [Lyon, Zwicky, arXiv 1406.0566]

  function describing the quark loop) ⇒ Rare heavy mesons decays to leptons Cédric Méaux

  Integrated branching ratio measured by LHCb [LHCB-PAPER-2012-051]Angular observables :dΓ d cos θ (D + → π + µµ) = A + B cos θ + C cos 2 θ Two clean null tests : A FB = B Γ and F H = 2 A+C Γ with Γ = 2 A + C 3If C T and C S real and the other NP C i null, we get : Preliminary plot (doesn't include either n.f. corrections or hadronic contributions)

[

  Courtesy of Kristof De Bruyn]If not for LHC, B 0 (s) → τ + τ -will be a golden mode for the future collider (like FCC). • Comparison with sister processes with e or µ → test of LFU.• Richer phenomenelogy (angular observables) Definitively worthwhile to work on it and to prepare the future of this channel ! ! Rare heavy mesons decays to leptons Cédric Méaux

  → τ + τ -: Axe 1 : (3π, µ) Toy estimate obtained by : U L = 2 • α • signal-yield-error (no systematics, no efficiency correction, no template correction) Axe 1 explored : U L(3π,µ) U L(3π,3π) for B 0 (s) : 3.7(4.5) • Appeared to be difficult to fight the B and D substantial background. • Internal note delivered on June 2018 LHCb-INT-2018-021

Rare heavy mesons decays to leptonsCédric Méaux

Experimental project on B 0 (s) → τ + τ -: Results and prospects

Axe 1 : (3π, µ) final state, Run 1 data U L(3π,µ) U L(3π,3π) for B 0 (s) : 3.7(4.5) • Appeared to be difficult to fight the B and D substantial background.

• Internal note delivered on June 2018 [LHCb-INT-2018-021] Axe 2 : (3π, 3π) final state, Run 2 data The signal yield obtained for Run 1 data is s = -15 +67

-56 (stat) +44 -42 (syst) Uncertainty mainly statistical, hence adding Run 2 data is highly motivated.

Two strategy explored in parallel :

• Apply the exact same selection than for Run 1.

• Re-optimize the selection, the regions, etc...

What has been done :

• Normalisation channel (except systematics) • Data-MC comparison.

• Apply the Run 1 selection (ongoing).

• A possible re-selection has been designed (performance estimation ongoing).

Prospects :

• Deliver soon an estimate of the limit that we could reach for both strategy Motivations for the (3π, µ) channel : ⊕ Higher effective branching ratio : 17.4% for τ → µνν vs 9.3% for τ → 3πν ⊕ Only 4 tracks required in the detector acceptance. ⊕ µ trigger more efficient than the hadronic trigger.

Plus one ν / only 1 τ vertex, less handle to discriminate signal and background. Substantial semi-leptonic B and D decays as background.

Regions : 3 ν in the final state ⇒ no narrow mass peak to fit ! Idea : Exploit the ρ 0 resonances of the τ → 3π decay

Signal τ in 5 τ + and τ -in 5 Bkg.

τ in 1/3/7/9 τ + (or τ -) in 1/3/7/9 Control τ in 4/8 τ + in 4/5/8 and τ -in 4/8 The decay rate can be expressed like :

3 with θ angle between the D and -direction of motion in the dipleton center of mass frame.

Two clean null-tests can be defined (more deltails in arXiv:0709.4174) • Forward-backward asymmetry :

• Flat term :


Sina Namaki

Araghi Franck

Fontanili Elyes

Lamine Frederick Benaben

An automatized Data Extraction approach for Process Mining and Business Process Analysis

Keywords: Process Mining, Discrete Event Simulation, Real Time Location System, Business Process Management 

In this paper we explain an abstracted version of our approach to provide a rapid business process modelling, and diagnosis for operational processes within organizations. This approach is oriented toward designing a set of tools which has three main functions; Tracking, Modelling, and Assessing. As a result, first we would gather the events automatically (Tracking), thanks to an Indoor Positioning Systems. Second, by using Process Mining we would be able to get the business process models (Modelling). Third, by using case-based, or heuristics algorithms and Discrete Event Simulation we want to propose a tool for process diagnosis and improvements (Assessing). This research project targeted three main fields which are Healthcare, Supply Chain Management, and Sport.

INTRODUCTION

Today's organizations in numerous areas either in service domain, or industry are competing in a dynamic business world, where each day customers can receive a new product or service with different and more improved characteristics. Thus an organization is obliged to provide a continuous improvement environment within its processes to be able to compete at this level. This goal is accessible by using a Process Aware Information System (PAIS) [START_REF] Dustdar | Process-Aware Information Systems: Bridging People and Software Through Process Technology[END_REF]. Acquiring a realistic and relevant model of the real world processes is an essential prerequisite in any continuous improvement approach. Nowadays to reach that competence, it is usually useful to proceed with data extraction from multiple data bases in the information system. But current approaches rarely gathers all the data, which could be useful to build a process model. Some activities, particularly those, which are not automated or instrumented, such as manual tasks, are not always tracked or recorded. For instance, hospitals information systems do not gather all the data used to model the different pathways followed by patients. It is the same problem in a manufacturing workshop, or on a larger scale in a supply chain, where the different steps of operation, delay, transfer, and inspection of products are not systematically recorded. Therefore, when the data for modelling are not sufficient or even non-existent, it is a necessity to extract the information either by using Interview-Based, or Evidence-Based discovery. As it already had been discussed in previous research works, these two ways have various shortcomings in case of gathering the exact and clean information (Fundamentals of Business Process Management | Marlon Dumas | Springer, 2013). Equally important if these observations are collected, they could be misinterpreted due to the limitation of a sample which does not ensure a true representation of the entire population. Consequently, the modelling could be inconsistent compared to the real processes, followed by a risk of a wrong diagnosis and unsuitable improvement solutions. This is the first challenge regarding this research work. The next one is choosing a proper modelling language, which could represent the processes in relevance with their field. On the other hand, projects about the approach to transform a real process to a business model as accurately as possible are more unusual. The third challenge in this research work is providing a Business Process Analysis (BPA). This will focus on obtaining diagnosis and relevant key performance indicators. Regarding these challenges, we aim at developing a process mining tool, which would be able to extract data automatically from actual situations, generate business process models, and propose improvement alternatives. This tool will focus on operational processes within organization. In this order, we could provide a better organizational perspective for the enterprises, to understand their real time situation. This paper is organized through four main sections. The first section aims at explaining the framework of a project, which is oriented toward a PhD thesis ("A Tool-Based approach for a Fast Business Process Modelling and Diagnosis"). In the next section, the "detailed proposal" is presented. It concerns the approach to overcome the described challenges. In the "state of the art" section, some results from previous works are provided. Finally, "conclusion" describes the possible outcome of this project.

DETAILED PROPOSAL

This tool based approach aims at defining elements to automate, accelerate, and improve the Business Activity Monitoring (BAM) and Business Process Analysis (BPA). The extracted processes must be an applicable and dynamic model of reality. In figure 1, you can find an IDEF 0 model which shows the targeted approach in this paper. This approach is based on three main functions labelled as Tracking, Modelling, and Assessing. These three functions are naturally linked, and will be run in an integrated way by using the relevance tools. In this order we will be able to generate a solution for each described challenge.

Tracking

The three targeted areas (healthcare, supply chain, sport) are oriented toward multiple processes, which contain numerous entities and activities. Acquiring a dynamic awareness of their elements is necessary, to improve the outcome of these processes. RTLS (Real Time Location System) is a system dedicated to support the Tracking function and could be an answer on automatized data extraction. RTLS is an adapted technology for indoor localization. Its main function is similar to GPS (Global Positioning System) for locating a mobile object (People, or any other Physical Objects), which wears a radiofrequency tag. However, RTLS uses indoor sensors for tracking objects, while GPS uses satellites. In addition, RTLS uses different algorithms based on signal triangulation. While using this technology we will be able to overcome the first obstacle, which consists in adapting and testing this technology to automatically generate a clean and detailed log file. This log file could consist of all the necessary entities for us to trace and model the processes. Figure 2 displays our vision for the two primary functions (Tracking and Modelling). This figure shows different activities regarding extraction of data from multiple data bases and indoor localization sensors, and finally generating the model. Besides automated tracking of physical flow, one of the biggest challenges in this work would be to enhance the robustness of the gathered log files. It is necessary to infer multiple rules to control the process, such as routing or prioritization rules, dynamic assignment of staff, use of extra resources, etc. Also there would be a need to merge different types of data to be able to track the elaborated processes. Consequently, we need to use various types of Data Mining algorithm to have a proper log file, which displays the reality. Fig. 2. An illustration for the two primary functions of the approach (Data extraction, Modelling).

Modelling

At the second phase of this research work we aim at extracting information from the event logs. This idea is based on discovering activities while using a Process Mining tool. As a proof of concept, for beginning of this work we will concentrate on tools which have already made their ways into the process mining market, such as ProM 1 and Disco 2 . To improve the execution of processes, we would like to couple Business Process Management and Lean Management. The customer orientation of Lean is similar to that of BPM (4). These two approaches would aid us to improve the satisfaction level of users, and to enhance the process flow and agility of the organization. The prospected modelling language for this approach would be BPMN 2.0 (Business Process Model and Notation) which has many competencies regarding our objectives. BPMN is widely used, because one Moreover, it allows the users to build a conventional and comprehensive process models [START_REF] Kalenkova | Process mining using BPMN: relating event logs and process models[END_REF].

Assessing

It's been cited that one of the main shortcomings in process mining tools is disability to provide compatible diagnosis and improvement alternatives for process models [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF]. Process Mining focuses on extracting knowledge from data generated and stored in corporate information systems in order to analyse executed processes. In the healthcare domain, process mining has been used in different case studies, with promising results. Accordingly, we have conducted a literature review of the usage of process mining in healthcare. The scope of this review covers 74 papers with associated case studies, all of which were analysed according to eleven main aspects, including: process and data types; frequently posed questions; process mining techniques, perspectives and tools; methodologies; implementation and analysis strategies; geographical analysis; and medical fields. The most commonly used categories and emerging topics have been identified, as well as future trends, such as enhancing Hospital Information Systems to become processaware. This review can: (i) provide a useful overview of the current work being undertaken in this field; (ii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches for their own applications; and (iii) highlight the use of process mining to improve healthcare processes [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF]. Thus, strengthen this point in business intelligence tools is a priority in this research work. For instance in healthcare processes, we would like to propose different algorithms in order to discover the bottlenecks, thanks to the key performance indicators, such as longest activity, critical path, distance that each patient takes, etc. So the first step of our work concerns the study of existing algorithms and results, which they could provide. Finally we would like to present our new algorithms to generate diagnosis for each process. We also aim at using a discrete event simulation approach. Consequently, the user will be able to execute the process and see its weaknesses before implementing the process in real world.

Applicability

This work would like to target three different areas. First, it would be healthcare. With the importance of efficiency in this area many healthcare organizations are under major pressure to reduce their costs and enhance their services. On the other hand, these organizations are troubled with vast amount of data, caused by the improvement in using information systems to record all the internal events. Previously these data were gathered by using interview-based discovery. Now we would like to use indoor localisation to generate the log files, and step by step (based on figure 1) try to provide insight for each process. As a result, this approach would help healthcare organizations to enhance their information system to become process-aware [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF]. Eventually, they could reduce their costs, simplify the elaborated processes, find the bottlenecks, and visualize the process paths. Thus, it could improve the patient safety and satisfaction level.

The second area is Supply Chain Management. Nowadays, this subject is attached to data mining and process mining. The difference between supply chain and healthcare is that in healthcare we trace the map of one object which is a patient, however in supply chain we are gathering data about multiple objects which are either assembled or disassembled in the chain among different actors. So, this high frequency of changes and fluctuations, caused by vast number of elements in logistic processes could be an evidence for us to target this area [START_REF] Becker | Process Maintenance of Heterogeneous Logistic Systems-A Process Mining Approach[END_REF].

The third area would be Team Sports. This area is oriented toward enormous amount of collected data for each player during games, or in training sessions. Many sports as basketball, soccer, volleyball, and American football feel the need to have a perspective about their strategies at each action. It could be very useful to have a tool which in correspondent with indoor localization system provides insights for team staffs about the outcomes of their plays, such as the distance that has been covered by each player, efficiency in the motion of players, and blind spots of each position.

Eventually, figure 3 presents our goal in this research work, which is to apply a Business Process Intelligence (BPI) approach to gain improved processes from raw event logs. Based on this approach the tracking step and preparation of log files would transfer the raw information into insight for organization. Then, by creating process models and choosing relevant KPI's, one could obtain a proper diagnosis. Finally, one could evaluate the performance of processes thanks to KPI's, and by using simulation we can run several scenarios in order to find the best way of executing processes. We can consider process mining as a tool which represents the BPI (van der Aalst and Weijters, 2004). In this section we would like to provide some results from the previous works in this area by answering these questions:

1. What is the advantage of using indoor localization for extracting data?

2. Why is necessary to use a Business Process Management approach?

3. How Process Mining could help?

4. What are the advantages in using Discrete Event Simulation?

(i) To answer the first question needless to say that the very first step to execute a process mining tool is to use a log file. It has been cited [START_REF] Suriadi | Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs[END_REF] in many use cases the major problem was to gather detailed log file which corresponds to the reality. Therefore, using indoor positioning could be an applicable solution for our problem. This way we could get the data automatically with exact level of details that we wanted [START_REF] Miclo | RTLS-based Process Mining: Towards an automatic process diagnosis in healthcare[END_REF].

(ii) Business Process Management is oriented toward operational processes. It aims at extracting information from raw data and providing a map of the operational processes which could help us to gain a perspective of our organizations. Moreover, based on some citations (Aalst and P, 2013) using BPM and Process Mining could help us in discovering, automatizing, monitoring, and analysing the processes. On the other hand, BPM could be defined as an applicable approach while using simulation. Eventually these could be the reasons for us to consider BPM as a helpful approach in our research work.

(iii) The enterprises are targeting the idea of correlating the strategical management level (Decision) and support management level by executing the operational processes in an efficient way. This could be feasible by having a better vision on these processes. The very first step could be extracting data from information systems and event logs to capture the operational business processes as they are being executed (van der Aalst and Weijters, 2004).

(iv) In our review on previous works on process mining tools (Aalst and P, 2013), it has been noted that many tools are considering the discovering, and conformance checking activities, but the last section which is enhancement, has been neglected. To solve this problem, we propose to use Discrete Event Simulation. As a result we would be able to get an insight about the outcome of each process. Albeit, it has been mentioned because of the lack of experiences in process simulation area, we don't see many usage of these tools. On the other hand, enterprises believe this kind of tools simplify the reality and they are not applicable. But in contrary, providing compatible diagnosis and a perspective of reality for each process could be pragmatic.

To summarize, using Indoor Localization System and Process Mining could help service, entertainment, or industry organizations to visualize the processes, bottlenecks, and interactions among all the entities within processes (such as medical staff, tools, and patient) (Fernandez-Llatas et al., 2015). However, based on our observation, it is rare to find a tool which could complete the Business Intelligence approach (figure 3). As a result, the enhancement of processes still is time consuming and challenging for organizations.

CONCLUSION

The presented paper subjected to describe the main characteristics of a PhD thesis which aims at defining an approach to identify components to automate, accelerate, and improve the design of virtual processes, which must be a virtual and dynamic copy of the real process. Thanks to this research work, improvement solution will be assessed before being implemented on the real system. It will also provide interesting opportunities in terms of agile and predictive control in the near future. This topic is close to ongoing research work on Industry 4.0 [START_REF] Rennung | Service Provision in the Framework of Industry 4.0[END_REF].

AKNOWLEDGEMENT

This research work is supported by Maple High Tech Company. We are thankful to our colleagues specially Mr. Ludovic Tancerel who is supporting us by his great knowledge on Indoor Localizations Systems.

Fig. 1 .

 1 Fig. 1. The IDEF 0 representation for each step of the research work

  1 www.processmining.org 2 https://fluxicon.com flat control flow perspective, sub processes, data flows, and resources within one BPMN diagram.

Fig. 3 .

 3 Fig. 3. The Business Intelligence approach


Sina Namaki

Araghi * Franck

Elyes Lamine

Ludovic Tancerel

Frederick Benaben

Monitoring and analyzing patients' pathways by the application of Process Mining, SPC, and I-RTLS

Keywords: Indoor-Real-Time Localization Systems, Process Mining, Statistical Process Control. 

The strive of healthcare organizations for learning how their processes are influencing the quality of their services and mostly the satisfaction level of their patients is encouraging this research work to propose a method which provides comprehensive knowledge in that regard for healthcare experts. This paper aims at suggesting an approach to model the pathways of patients as business processes and to measure the performance level of their executions based on the distance that patients take. In this order, Indoor-Real Time Localization Systems (I-RTLS) has been introduced as the tool to monitor patients' pathways; Process Mining techniques will enrich the level of information by discovering the process models. Statistical Process Control (SPC) methods will help to measure the performance stability of processes. An outline of the approach is highlighted by a study case which had been designed at a hospital in France. The results show that the proposed approach could provide accurate data and comprehensive knowledge about patients' pathways and the execution of the processes in hospitals.

INTRODUCTION

Healthcare organizations are pursuing different techniques, technologies and scientific approaches to understand how their processes are being executed and to observe the influence of their decisions on the quality of their services. Clinical or patients' pathways are highly dependent on the structure of processes in the healthcare organizations. The clinical pathways have been defined as a structured, multidisciplinary care plan which defines the steps of patient care for a certain disease in a specific hospital [START_REF] Rotter | The effects of clinical pathways on professional practice, patient outcomes, length of stay, and hospital costs: Cochrane systematic review and meta-analysis[END_REF].

The patients' pathways could contain numerous amount of information such as duration of activities for patients, and the taken distance by the patients, which should be extracted and presented with a meaningful approach. Several methods have been used to improve the management of clinical processes and pathways. It has been indicated that the objective of these methods is to enhance the quality of healthcare services by improving the risk-adjusted patients' outcome, promoting patients' safety, increasing patients' satisfaction, and optimizing of resources [START_REF] Vanhaecht | The impact of care pathways for exacerbation of Chronic Obstructive Pulmonary Disease: rationale and design of a cluster randomized controlled trial[END_REF]. Corresponding to this desire of healthcare organizations, this paper describes the possible application of several technologies and techniques to monitor and investigate different aspects of patients' pathways. The I-RTLS could provide a real-time monitoring of patients, staff, and medical instruments. The output of these systems could be used by process discovery algorithms to generate automatically a model of the patients' pathways. Consequently, there is no need to monitor, record and model the process executions manually. Additionally, a set of statistical process control techniques is proposed to enrich the extracted knowledge. The motivation for this work is to equip healthcare experts with capable tools and powerful techniques to facilitate and support their managerial actions. This paper is structured to identify some aspects of I-RTLS and process mining in section 2. Also, the approach of this paper is identified through a study case in section 3. Finally, a summary and future perspective of the research work is presented in the last section.

STATE OF THE ART

I-RTLS

Indoor-real-time localization systems are emerging in the context of smart healthcare [START_REF] Fernandez-Llatas | Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems[END_REF] to help healthcare organizations to acquire a location awareness regarding the patients, staff, and their assets. The principle of indoor localization systems is similar to the Global Positioning Systems (GPS) [START_REF] Van Haute | Performance analysis of multiple Indoor Positioning Systems in a healthcare environment[END_REF]. They consist of two main parts that communicate with each other thanks to a radiofrequency signal. The first component is the mobile nodes or tags which could be attached to the objects that need to be located. Second, satellites (for GPS) or sensors (for indoor localization systems) which find the position of the mobile nodes or tags. Concerning GPS, NAVSTAR satellites are being used to locate the objects in outdoor environments. However, for localizing objects inside facilities, multiple communication technologies have been suggested. For instance, in hospitals' environments, RFID (radio frequency identification) and wireless local area networks (Wi-Fi, ZigBee, Z-Wave, Bluetooth, etc) are playing key roles. These technologies are using electromagnetic wave frequencies in different ranges (LF, HG, UHF, UWB). This research work sought the application of a real-time localization system which is able to provide a robust event log related to the patients' movements. This leads us to explain the applicability of I-RTLS. These systems provide log files which correspond to the coordinates of the objects (x, y, and z) with a given frequency (for example one event per second). Generally, these systems function by three main components:

1. Tags (or signal transmitters): these are the objects that could be attached to patients, staff, or assets. These tags are active that means they would emit signals.

2. Sensors (or anchors, or beacons, or antennas): these devices would receive the signal of the tags. At least three sensors would receive the signal of one tag then transfer it to a location engine in order to calculate the geometric location (x, y, z) of the tag. The application of I-RTLS could benefit the healthcare organizations in several ways. For instance, the addition of location awareness could help the healthcare staff to know the exact and real-time position of each patient and the mobile instruments. Also, it could increase the level of safety in the organization. Additionally, by cross-matching the position of tags, it is feasible to monitor which medical device is being used for which patient. This paper aims at applying the localization technology to analyze and monitor the patients' data (especially related to the patients' pathways) in real-time, which will be highlighted in the followings.

Process Mining

The application of process mining is basically concerned with the extraction of knowledge from event logs which are recorded in an information system. Process mining could be categorized as an evidence-based approach which uses the logs related to the execution of processes. Generally, to discover and map the process models there are several approaches such as interview-based, evidence-based, and workshop-based process discovery (Dumas et al., 2013). The interview-based method is related to extracting knowledge on the way processes are being executed in an organization. This method could be extremely time consuming and deceptive, as several actors in a process could have different visions of the process. The workshop-based method is about enriching the maturity of information by discussing different aspects of the process with process owners, stakeholders, and users of the process prior to the modeling phase. Here unlike the interview-based method, the modeler seeks the visions and knowledge of the bigger roles in the organization too, such as the domain experts, and process analysts. The evidence-based approach studies the execution of processes by going through the existing evidence. This evidence could be documentation of the process, observing the process at runtime, and automatic process discovery. The extracted knowledge by process mining is based on three activities: process discovery, conformance checking, and enhancement of business processes [START_REF] Aalst | Process Mining: Data Science in Action[END_REF].

The evolution of process mining begins with the work of Agrawal et al [START_REF] Agrawal | Mining process models from workflow logs[END_REF], where they explained an algorithmic method to generate process flow graphs from executions of processes which have been registered as workflow logs. Cook and Wolf also became one of the pioneers to use process mining techniques on event logs [START_REF] Cook | Discovering Models of Software Processes from Event-based Data[END_REF]. Weijters and Van der Aalst enriched the concept of process mining by proposing a discovering technique which deals with the noise and also capable to validate the workflow processes by measuring the differences between the perspective model and the real execution of the process [START_REF] Weijters | Process Mining Discovering Workflow Models from Event-Based Data[END_REF].

Currently, the application of process mining in healthcare provides four main perspectives for the experts: control flow, performance, conformance, and organizational perspective [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF]. The control flow perspective shows the execution of processes. The performance perspective provides an understanding related to the possible bottlenecks in a process and it evaluates performance level of a process.

The conformance checking gives a view to compare the designed processes with the real discovered behavior of processes. The organizational perspective helps to see how an organization uses its resources to execute the processes. Process mining in healthcare has mainly received attentions towards designing and applying discovering algorithms. The enhancement aspect of process mining needs to be evolved by the application of robust techniques. Equally, process mining techniques need to be evolved for analyzing patients' treatment data. As it has been seen in [START_REF] Yang | Process mining for clinical pathway: Literature review and future directions[END_REF] the current process mining techniques are not adequately capable of dealing with unstructured processes such as in healthcare. This issue is also implied from the previous cases of analyzing patients' pathways and in general patients' behavior.

Recently the research team of Pontificia Universidad Catolica de Chile described several cases of using real-time location data and process mining for analyzing different aspects of healthcare processes, such as monitoring patients and staff in a hospital [START_REF] Fernandez-Llatas | Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems[END_REF]. In their research work, they have designed a case study in a hospital in Valencia, Spain. They applied the PALIA (parallel activity based log inference algorithm) algorithm to analyze the location data generated by MySphera system. This system uses the ZigBee technology and RSSI method to localize the tags. Moreover, by the application of MY HOSPITAL, they were able to analyze different types of healthcare data. The PALIA algorithm has advantages in dealing with the healthcare data and especially the location data. In their analyses, they described the pathways as process maps and additionally they were able to monitor the accumulated duration that each room has been occupied. Furthermore, Fernandes-Llatas et al. succeeded to analyze the behavior of patients in a nursing home by enriching PALIA algorithm with the eMotiva additional analyzing algorithms [START_REF] Fernández-Llatas | Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes[END_REF]. In their study case, they were able to visualize the execution of processes. Also by applying the heat map analysis, the motions of the patients in the nursing home were monitored.

Miclo presented a similar approach for the application of process mining and real-time location systems [START_REF] Miclo | RTLSbased Process Mining: Towards an automatic process diagnosis in healthcare[END_REF]. Based on their work, it could be inferred that use of process mining and localization systems could help information systems to initially gather events with a higher level of details and as a result, the discovered process models could be more accurate and compatible with reality. They have encountered several challenges during their experiment like the calibration of the system. From their experience, it seems that the configuration of the system and calibration of antennas and tags could be time-consuming. The implemented I-RTLS system was supplied by Purelink which uses RFID-UWB technology.

Nonetheless, the application of process mining for analyzing different steps of processes in hospitals' sectors is emerging. It could be inferred that most of the work in this area are related to offering observational analyses. These analyses are highly dependent on the discovered process model. However, the quantitative analyses aspect of patients' processes is not sufficiently evolved. Therefore, the following approach tries to step forward to fill the illustrated gap.

DETAILED PROPOSAL AND THE EXPERIMENTAL RESULTS

This research work aims at enriching the control-flow and performance evaluation perspectives for analyzing clinical processes by the application of I-RTLS and SPC. These processes are related to the movements of the patients in the hospitals, basically categorized as a part of patients' pathways. To reach these objectives, an approach had been designed previously to this work. This approach has three main steps known as tracking, modeling, and assessing in [START_REF] Namaki Araghi | An automatized data extraction approach for process mining and business process analysis[END_REF]. Recently, this approach has been developed with the attention on the hierarchy of data, information, and knowledge [START_REF] Kanehisa | Data, information, knowledge and principle: back to metabolism in KEGG[END_REF]. Figure 1 presents this approach which has three levels. These levels represent the transformation of data to the knowledge through running six functions; data collection, log refining, modeling, analyzing, diagnosing, and prognosing.

Fig. 1. The approach for monitoring and analyzing patients' pathways

In the first function (data collection), thanks to the I-RTLS, movements of the patients will be analyzed, and the generated location data by the location engine will be collected in the information system. These sets of data should go through several actions in the log refining function to be prepared as event logs which could be the compatible inputs for modeling and analyzing functions. The log refining function would transform the primary data (as it has been shown by the example) to the usable information. These primary log files consist of noises and inconsistencies which should be removed for discovering the process models. In the modeling and analyzing functions, process models would be discovered and analyzed thanks to several techniques such as process mining and SPC. The results of these functions would be sets of qualitative and quantitative analyses that could evaluate the performance level of the processes. Simultaneously, by acquiring the process models of pathways, one could see how processes are being executed and what the different steps within each patient's pathway are. Additionally, in the diagnosing function, several root cause analyses would be performed to extract the reasons behind shortcomings of the processes. After identifying the causes, a simulation model will be generated in the prognosing function. This model would provide different improvement suggestions for enhancing the performance of processes and eventually acquiring a To-Be version of the process. Detailed illustration of all of these functions are beyond the limits of this paper, thus our focus here will be on the modeling and analyzing functions.

To evaluate this approach, an experiment has been designed at a hospital in France. The value of this study case for the hospital was to acquire a method which helps to shorten the duration of medical processes. Additionally, the healthcare experts needed more visibility of the processes within the hospital's information system.

To carry out this work, several actions had been performed. First and foremost, the map of the facility has been observed carefully and a department has been chosen for monitoring the patients' pathways. This department (Nutrition and Dietetics) was a proper start for this project since it has the circulation of patients with almost similar profiles and there were not a lot of unexpected situations. Secondly, in each zone of the department, the I-RTLS antennas had been installed. The number of installed antennas for each zone was related to the space and the material inside it. The Maple High Tech system has been used for the data collection phase. It provides the location of the tagged objects thanks to the UWB technology that can use a very low energy level.

The transmitted signals would be analyzed in the location engine by the TDOA and AOA measurement techniques. One of the advantages of using this system was in the fast installation of sensors. Essentially there was no need for calibration of tags and antennas, only there was a step of uploading the map of the facility and identifying the virtual zones.

During ten days of this experiment, each day nine tags had been distributed to the patients with almost similar profiles. The reason behind this selection was to see how the pathways could differentiate from each other for the patients with almost an identical medical procedure. Also, this sample size would strengthen the results of further statistical analyses.

After monitoring the patients by the localization system, several log files were collected by the location engine. These log files were extracted and recorded in the information system for generating robust event logs. The Transformed event logs contain multiple columns as ID, Timestamp Start, Timestamp End, and Activity Macro which represents the details of the activities related to the presence of patients in an identified zone. Figure 2 shows a summary of the event log for the first day of the experiment.

Fig. 2. The summary of the transformed event log

This event log could be used for process mining to map out the patients' pathways. For such analyses, Celonis 4 (https://www.celonis.com/) process mining application could be a powerful tool to provide the process models and basic statistics related to the execution of processes. Figure 3 presents the general process model for representing the patients' pathways. By the first glance of the process model, one could observe that most of the paths are heading to the "Waiting Room 3" of the department, and it could be seen as the hot zone in this process (with the frequency value of 21 times). Additionally, it is visible that one case after finishing its treatment (at the "Exit the Consultation Box" section) took an unnecessary step by going back to the "Waiting Room 3" which added up the length of stay of the patient in the hospital by an undesired activity. The same scenario happened for another patient after finalizing the administrative works in the "check out office".

Fig. 3. The process model which represents the patients' pathways

This control flow perspective could go through several processes diagnosing activities to discover the reasons behind such errors in the process. In this experiment, we observed that patients made a lot of mistakes while following processes' phases. It could be inferred that it happened because of the misleading signs in the facility.

Moreover, overall statistical analyses of processes provide additional knowledge on the processes. For instance, these analyses indicate that among all the activities in the event log, the "Registration Waiting Room" has the maximum average duration. Patients on average spent one hour and seven minutes in this section. Table . 1. Bottleneck analyses for the slowest connection between activities in the department and the percentage of affected cases by these activities.

One of the objectives of this paper is to enrich the performance evaluation perspective by the application of statistical process control for analyzing the distance of patients' pathways. SPC is being used in the context of analyzing healthcare data [START_REF] Thor | Application of statistical process control in healthcare improvement: systematic review[END_REF], but not in correspondence with the location data and the patients' pathways. In following, the application of SPC in this work will be explained.

It is extremely important to decrease the level of variabilities in the processes' outcome in order to improve the quality.

Evaluating the performance of a process either in service or in the manufacturing sector is oriented towards analyzing the capability of processes to execute around a target or nominal dimensions of the quality characteristics of the process. SPC is a powerful collection of problem-solving tools useful in achieving process stability and improving capability through the reduction of variabilities [START_REF] Montgomery | Introduction to statistical quality control[END_REF]. The Shewhart control chart is one of the most sophisticated techniques of SPC. The goal of applying SPC is to monitor and analyze the processes in a way to ensure that the clients' needs are satisfied and the process is not executing with high variabilities.

Typical control charts have three indications, which are known as center-line (CL), upper control limit (UCL), and lower control limit (LCL). These limits are applied horizontally in the control charts and indicate the stability of processes. As long as all the samples of a data set fall between these two lines, it could be inferred that the process is in control and no actions are needed to modify the process' structure. However, if a sample falls beyond these limits or the chart shows a trend in process' results to be unstable, then the control chart indicates that the process is out of control and necessary diagnoses actions should take in place.

There are several control charts used for analyzing quantitative and qualitative outcomes of the process. In this paper, R and x ̅ -charts are applied to measure the performance of designed patients' pathways based on the distances that patients take to receive their health care. To set up these analyses, it's important, to begin with, the R-chart. R-charts would analyze the process variabilities. These analyses will help to set up the x ̅ -chart [START_REF] Montgomery | Introduction to statistical quality control[END_REF]. The R-chart shown in figure 4 plots the subgroup ranges. Each subgroup represents one day run of the experiment. Consequently, the horizontal axis presents the days (samples) and the vertical axis shows the statistical summary in which the average range of each sample could variate. The CL is the average of all the subgroups' ranges. The upper and lower control limits are set at a distance of three standard deviations above and below the center line. They define the limits for expected variations in the subgroups ranges.

This chart analyzes the range of distances for the patients' pathways. It shows that variations of ranges for the distances are statistically in control. This indicates we may now construct the x ̅ -chart to analyze the stability of the process.

Figure 5 shows the x ̅ -chart which displays the stability level of the average distance taken by ninety patients. It shows that on average patients walk 96.4 meters in the facility. The red points which are beyond UCL and LCL imply that two of the subgroups are including some assignable causes which are affecting the stability of the process.

Fig. 4. The R control chart for analyzing the variations of pathways' distances

This chart presents that the distances of patients' pathways usually were following a similar pattern, however, in two days some abnormality happened that should be investigated.

For instance, on the last day of the experiment patients skipped one of the steps in their processes which was the "blood collection". After observing this problem, it was pointed out that this section in the facility was closed for a couple of hours on that day. This caused for the patients to postponed their treatments to another day and skipped one activity of their processes. These types of causes could affect the quality of services in a hospital and decrease the satisfaction level of the patients. This analysis could be applied in a long run and to observe the behavior of the patients in different time periods.

Fig. 5. The x ̅ control chart for analyzing the stability of pathways' distance

It is feasible to bring the process back to the stability by diagnosing the problems during the first day and the last days of the experiment. However, explaining the diagnosing function of our approach is out of the scope of this paper.

CONCLUSIONS

This article aimed at presenting the advantages and possibility of monitoring and analyzing the patients' pathways by the application of two emerging tools known as I-RTLS and process mining in the context of smart healthcare. It should be mentioned that real-time localization of patients could pave the way for accurate analyses of clinical processes especially patients' pathways.

The experience gained from the mentioned study case showed that healthcare experts don't have to spend unnecessary time for gathering and designing a model for patients' processes. In addition, they have appreciated the visibility of the processes and the information related to the patients' pathways. The association of process mining and I-RTLS offers a great potential for visualizing and analyzing healthcare processes. On the other hand, the location and process awareness within information system could increase the security of the facility and boost the efficiency level of processes. Finally, by the addition of SPC, we were able to extend the quantitative analysis of the processes. The application of control charts in this paper provides more visibility on the circulation of patients in the hospital's facility. This method could be used to monitor the clinical pathways to detect even small but clinically important changes in the patients' processes. This analysis could be applied for the duration of activities, the occupancy rate of different sectors in the hospital, and for monitoring the staff performance level as well. The major value of this paper is on these analyses which are not being used in similar approaches in the literature. Control charts provide robust analyses which could help experts to dive into the enhancement aspect of process mining.

Regarding the obstacles in this approach, there is a social challenge of tracking the staff that should be completely expounded for them in the upcoming studies.

Further research works could be focused on performing diagnosing and prognosing actions to highlight the causes of shortcomings in the processes and to generate the simulation models to extract different improvement opportunities.

  Recently, Rojas et al applied process mining in ER (emergency rooms) domain and tried to answer the two challenges. First, they have tried to indicate what are the major activities and stages in ER and then the relationships among those different stages have been defined through the application of PALIA tool. Second, they have identified that what is the highest destination for the patients after arriving in ER, which in their case it was the hospitalization (Alvarez et al., 2018).

  Table1presents the analyses based on the throughput time between two activities. This table presents only four of the slowest steps in the process. For example, 44 percent of cases are affected by the throughput time between registration waiting room and registration desk (Priorities). By improving these connections, process throughput time could increase significantly.

	Order	From	To	Cases affected
	1	Registration Waiting Room	Registration Desk	44 %
			(PRIORITIES)
	2	Registration Waiting Room	Registration Desk	56 %
			(NORMAL)
	3	Doctor's Office	Waiting Room 3	44 %
	4	Doctor's Office	Waiting Line for checkout	56%


Sina Namaki Araghi

Franck Fontanili

Elyes Lamine

Ludovic Tancerel

Frederick Benaben

Applying Process Mining and RTLS for modeling, and analyzing patients' pathways

Keywords: Business Process Management, Process Mining, Real Time Location Systems

Purpose: This paper aims at introducing a generic approach for visualizing, analyzing and diagnosing patients' pathways. This approach could be categorized as a business intelligence approach to extract knowledge for decision makers in healthcare organizations. The analyses provided by this approach are based on the location data which are being recorded in the information system (IS) by indoor-Real-Time Location Systems (RTLS). Findings: Healthcare organizations are getting more eager to learn from the execution of their processes. They seek different tools and approaches to analyze the processes and visualize the problems. This paper presents one of the possibilities to provide more understanding of process executions and it is based on the locations of the patients inside the organization. Approach: Business intelligence approaches provide new technical and technological solutions for business analysts to improve the quality of products and services within organizations. The approach of this work helps to visualize patients' pathways and analyze them by associating real-time localization and process mining. This approach consists of four phases in which several functionalities have been defined. These phases are Data, Information, Awareness, and Governance (DIAG). Additionally, a case study has been designed to illustrate the DIAG approach.

INTRODUCTION

The clinical pathways or patients' pathways consist of several activities and different steps with the objective of receiving a service which is the health treatment. A clinical pathway is a complex intervention for the mutual decision-making and organization of care processes for a well-defined group of patients during a well-defined period [START_REF] Vanhaecht | The impact of care pathways for exacerbation of Chronic Obstructive Pulmonary Disease: rationale and design of a cluster randomized controlled trial[END_REF]. It is desirable to visualize these pathways as the business processes, which have the objectives and consist of different tasks and actors to reach those objectives.

Many initiatives have been introduced within the last decade to improve the management of clinical processes. The clinical pathways aim at enhancing the quality of healthcare services by improving the riskadjusted patients' outcomes, promoting patient safety, increasing patient satisfaction, and optimizing the use of resources. These organizational enhancements can be only justified on the basis of a relevant diagnosis of the reality behind the processes' executions and their management on the field. Hence, equipping decision-makers with tools and methods to facilitate and support their actions could beneficiate the organization and its clients. This paper aims at describing a study case which illustrates a developing approach named as DIAG. This method investigates different aspects of the patients' pathways and uses multiple sets of techniques to visualize and analyze the performance level of each pathway. DIAG has four different phases; Data, Information, Awareness, and Governance. This paper focuses on the "information" phase to highlight the core of this method. It describes how a proper set of location data from patients' movements could beneficiate healthcare organizations by acquiring qualitative and quantitative analysis.

In the following, section 2 presents the features of two main tools and techniques in DIAG method. The third section explains the DIAG through exhibiting a summary of a study case, which has been performed at a hospital in France. Finally, the last section shows the summary of the works and the perspective for the future works.

STATE OF THE ART

This section presents the notion of designing business process models, the functionality of indoor-RTLS and process mining.

To analyze the performance level of the operational processes, organizations are trying to move from the "data-aware" information systems to "process-aware" information systems [START_REF] Aalst | Business Process Management Demystified: A Tutorial on Models, Systems and Standards for Workflow Management[END_REF]. This motivates organizations to use business process management approaches, which consists of modeling processes and performing quantitative analysis on top of the process models.

To discover and analyze the business processes several actions must be taken. To clarify, one should understand the "life cycle of process modeling". In order to design business processes, different activities should be taken into consideration such as gathering information, clarification of data and designing the process flows. After acquiring such models experts should define criteria in accordance with their strategies, then a performance measurement should be defined [START_REF] Looy | Business process performance measurement: a structured literature review of indicators, measures and metrics[END_REF].

Currently, to gather data related to the process executions an interview-based process discovery approach is being used (Marlon Dumas, 2013.). To succeed in such approach, it necessitates to carry out a huge load of works to do the on-field observations and perform interviews with processes' actors. Eventually, the quality of the gathered data is in doubt and it is dependent on the experience of the interviewer and the process analysts.

The evidence-based process discovery is another approach which helps to automatically perform process discovery from the information systems. However, there are several inconsistencies in the case of gathered data in the hospital information systems. For example in the case of patients' pathways, one could extract the data from hospital information system to observe the amount of time that each patient has spent in the hospital sectors. Since people are in charge to record the patients' data, there are lots of inaccuracies with the registered data.

This research work seeks for an efficient way of gathering accurate data for visualizing the patients' pathways. The final results should be a process diagnosing platform which is able to visualize and analyze the activities within patients' pathways. In addition to the process awareness, the platform could provide an awareness of the location of each event. This platform could support the transformation of current information systems to process and locationaware information system. The approach in this research work aims at supporting this transformation of the information systems by the application of I-RTLS and process mining. In the following, these two fields (indoor localization and process mining) will be explained.

Indoor Localization Systems

The objective of these systems is to track objects inside indoor environments. Their functionality is similar to Global Positioning Systems (GPS) [START_REF] Drawil | GPS Localization Accuracy Classification: A Context-Based Approach[END_REF]. GPS works by using NAVSTAR satellites. The GPS functionality does not suit the indoor localization and the corresponding signals are not capable of finding an object inside buildings with accurate coordinates (x, y, z). Therefore, indoor localization technology is being used for this matter. These systems consist of two main parts. First, mobile nodes or tags which are attached to objects that need to be located. Second, sensors or readers which find the position of the tags or mobile nodes.

Multiple communication technologies are being used for indoor positioning of objects in healthcare and industry. RFID (Radio Frequency Identification), and Wireless Local Area Networks (Wi-Fi, ZigBee, Z-Wave, Bluetooth, etc.) are playing a huge role in these organizations. RFID is an electronic identification technology used for tracking goods and people [START_REF] Curran | An Evaluation of Indoor Location Determination Technologies[END_REF]. This technology uses radio frequency waves to transfer data between tags and readers. RFID tags function in three different states; active, semi-passive, and passive. Active tags are using an internal battery, or sometimes they are connected to an external power source. These tags offer a wide range of hundred feet or more. Passive tags do not have any energy source, and their accuracy and readability are limited [START_REF] Want | An introduction to RFID technology[END_REF]. On the other hand, semi-passive tags use batteries and they need an external power source to communicate with readers [START_REF] Lai | Vibration Powered Battery-Assisted Passive RFID Tag[END_REF]. The Readers have the role of transferring radio frequency signals to the tags and receiving them back in regulated version [START_REF] Jin | Efficient Complex Event Processing over RFID Data Stream[END_REF]. Thanks to the semantic event processing, the system could translate the signals of the tags to the location data [START_REF] Bok | RFID Based Indoor Positioning System Using Event Filtering[END_REF].

In the context of locating objects, RFID is being used mainly in manufacturing sectors [START_REF] Lu | RFID enabled manufacturing: fundamentals, methodology and applications[END_REF]. In the case of healthcare organizations, RFID could not satisfy all the needs. It could identify the tagged objects which are passing nearby the different stations, but the tags should be moving through a very well structured operation. Also, RFID systems do not necessarily display the real-time location data. The real-time information could be very useful in manufacturing and hospitals, especially in emergency situations. Consequently, in some cases, researchers have integrated RFID with WLAN to acquire realtime data (Adame et al., 2016).

The other localization systems are classified as Indoor-Real Time Localization Systems. These systems have three main parts. Tags, which are attached to the objects. These tags are active and they emit signals continuously. Sensors or beacons; these devices would receive the signals and send them to the third part of the system which is the location engine. This part would use different algorithms and techniques to calculate the exact locations of the tags. Some of these algorithms are Triangulation, Trilateration, Angle of Arrival (AOA), Time Difference of Arrival (TDOA), and Received Signal Strength Indicator (RSSI) [START_REF] Luo | Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites[END_REF]. Multiple wireless local area networks have been introduced in markets for indoor-RTLS, some of them are Wi-Fi, and ZigBee which has been seen in previous works that this technology could be applicable in service sections. It has low energy consumption and multichannel control systems, alarm systems, and lighting control [START_REF] Cheng | Using ZigBee and Room-Based Location Technology to Constructing an Indoor Location-Based Service Platform[END_REF]. Ultra-Wide Band (UWB), these systems have short period pulses as a result UWB is suitable for indoor positioning technologies. UWB performance seems to be better than other technologies as well as in terms of accuracy (less than 30 cm) and of course the range which is up to 200 meters within an open space. The weakness of this system is inside buildings with thick walls and full metal structures, due to the fraction and reflection of signals in those types of buildings. Bluetooth or Bluetooth Low Emission (BLE) is the other technology which is being used in similar approaches and it has a very low consumption level. Some aspects of indoor localization have been discussed in this section. The next part will introduce process mining and highlights its association with I-RTLS in the context of healthcare.

Process Mining

It is obvious that quality of services in hospitals could directly influence the lives of people. Thus, it is inevitable that hospitals should plan and act on the improvement of their processes. The quality of healthcare processes is highly dependent upon the way they are being executed. This idea is motivating researchers to provide healthcare organizations with new approaches such as process mining to illustrate the execution of the processes. In addition, execution of medical and non-medical processes within the healthcare organizations would result in numerous recorded events within information systems. These data would be displayed as a log file. A log file would consist of a multiple information such as case id, timestamp start, timestamp end, details of activities, resources, and other types of information. One of the challenges for organizations is to extract meaningful information from these log files [START_REF] Mans | Process Mining in Healthcare: Data Challenges When Answering Frequently Posed Questions[END_REF].

Process mining is a set of techniques and methods which are subjected to discover the business processes from event logs. Process mining consists of three main activities: (i) process discovery, (ii) conformance checking, (iii) enhancement [START_REF] Aalst | Process Mining: Data Science in Action[END_REF]. By using this method information systems are capable of translating raw log files into usable sets of information. This information could be the process models, general statistical analysis of processes, bottleneck analysis, analysis of a variety of cases in event logs and etc. There are multiple advantages for organizations in applying process mining techniques.

In the context of healthcare, study cases show that process mining could beneficiate directors by providing several perspectives for decision-making [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF]. A control-flow perspective could show the execution of the processes. A performance perspective highlights the problems and bottlenecks while executing the processes. Currently, these analyses are basically related to analyzing the timeoriented data [START_REF] Fernandez-Llatas | Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems[END_REF]. Additionally, conformance checking would help organizations to see where the deviations are in a process. The resource usage analysis is the other perspective offered by process mining [START_REF] Caron | Monitoring care processes in the gynecologic oncology department[END_REF].

Process mining techniques and algorithms are being developed in many tools. ProM (http://www.processmining.org/) , Disco (https://fluxicon.com), QPR (https://www.qpr.com) , Celonis (https://www.qpr.com) are front-runners in the market. Most of the healthcare cases are being developed by ProM [START_REF] Rojas | Process mining in healthcare: A literature review[END_REF], which is extremely powerful in applying multiple techniques on event logs [START_REF] Dongen | The ProM Framework: A New Era in Process Mining Tool Support[END_REF]. Disco is a userfriendly and powerful tool, however, it does not contain all the analyzing techniques like ProM. On the other hand, Celonis and QPR are two tools which are known and being used mainly in manufacturing industries.

Applying process mining and indoor localization systems did not receive sufficient amount of attention, however, it shows promising results in healthcare. For instance, Fernandez-Llatas et al used this idea in order to monitor the behavior of the patients in a nursing home by analyzing the pathways of the patients [START_REF] Fernández-Llatas | Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes[END_REF]. In another case, they presented an approach for applying RTLS and process mining in a hospital in Valencia which also shows intriguing outcomes in case of observing the movement of patients and to analyze the time aspect of the process execution [START_REF] Fernandez-Llatas | Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems[END_REF]. In both cases, they have used PALIA ILS suits which is a web-based process mining tool to discover the process maps. PALIA stands for Parallel Activity-based Log Inference Algorithm and is able to infer workflows from activity log samples. And ILS is for Indoor Localization Systems. Also, they have introduced a methodology which supports their application. This methodology consists of several steps as ILS installation, ILS data gathering, a semantic aggrupation of areas, process filtering, process discovery, process conformance, process enhancement, and process improvement. In their methodology the difference between process enhancement and process improvement is vague.

In this paper, the process improvement actions are outside the application of process mining and indoor localization systems because the resolution to improve and change a process is only feasible by decisions coming from human knowledge.

In modern businesses, due to the challenges of designing and monitoring business processes, there is a necessity of using suitable analysis techniques [START_REF] Aalst | The Application of Petri Nets to Workflow Management[END_REF]. Also, it has been inferred from other works that analyzing business processes with exclusive criteria is necessary in order to classify the business processes' attributes, identify the bottlenecks, and compare process variants [START_REF] Vergidis | Business Process Analysis and Optimization: Beyond Reengineering[END_REF]. Additionally, Vergidis et al have classified process analysis into three groups of diagrammatic models, business process languages, and mathematical models. Needless to say, their approach is based on the primary analysis of processes which is the method one could use to visualize the processes.

It has been seen in the work of Zakarian in [START_REF] Zakarian | Analysis of Process Models: A Fuzzy Logic Approach[END_REF]) that using diagrammatic process modeling techniques provide a qualitative notation on the process executions. However, these techniques suffer from the lack of tools which show the quantitative analysis and performance level of processes. Furthermore, generating business process models is impractical without diagnosis based on relevant key performance indicators. This analysis should be in line with the main objective, which is improving the performance of physical processes. Improving business processes is only feasible by understanding the features of each activity and events in the process in accordance with customer and process owner's perspectives. For example, a task such as recording the history of patient's treatment could be a non-value added activity from patient's perspective. But from the hospital's point of view, this task should be identified as a value-added activity. As Dumas et al define in (Marlon Dumas, 2013) we could have three types of activities, valueadded, non-value added, and business value-added activities. This research work aims at analyzing business processes based on this classification of activities. However, the question is how to discover the activities in the business processes and how to distinguish them.

DETAILED PROPOSAL

It has been shown that business intelligence (BI) approaches in healthcare present new solutions for business analysts within the organizations especially the healthcare sector for improving the quality of medical care and patient's quality of life [START_REF] Machado | Applying Business Intelligence to Clinical and Healthcare Organizations[END_REF]. It could be inferred that BI approaches encompass the strategies and technologies used by enterprises to help business analysts to understand and improve the quality level of business processes [START_REF] Dedić | Measuring the Success of Changes to Existing Business Intelligence Solutions to Improve Business Intelligence Reporting[END_REF]). It's been proven that BI tools are working efficiently with healthcare data and they are able to generate real-time information and knowledge relevant to the success of healthcare organizations. Moreover, BI approaches profit healthcare professionals in making vital decisions inside hospitals, clinics, paramedics' circulation and management of the administrative works.

This paper aims at suggesting DIAG approach which is embracing the BI notion and tries to generate knowledge from the location data for decision makers in hospitals. DIAG stands for four levels which could transform the raw data into knowledge and awareness for healthcare experts. These levels are Data, Information, Awareness, and Governance.

The IDEF0 model presented in figure1 gives an overview of the DIAG approach. In the first phase, one function has been defined as gathering. This function is mainly concerned with monitoring the movements of the objects and receiving the primary data come from localization systems. As it has been explained before, these systems use the positioning algorithms to locate the tags. The log refining function executes different data mining techniques in the context of RIO-DIAG platform. This function cleans and transforms the collected data in the first function into the proper event logs which suit the process mining techniques in the following function Figure 1. The DIAG methodology modeling. The modeling function uses the process discovery algorithms and illustrates the execution of a processes based on the qualitative process analysis. Fourth, in analyzing function, different techniques are being used in order to provide a quantitative analysis for users. The performance level of processes will be evaluated in this function, and one could see and comprehend the way patients are circulating in the organization to receive their treatment.

In the diagnosing function, multiple sets of techniques will be used to highlight the real cause behind the problems and weaknesses in the performance of processes. Last but not least, in the prognosing by using queuing theory and other simulation techniques different scenarios and improvement actions will be suggested. RIO-DIAG is the platform which is working based on these functions, and by fulfilling four different phases of DIAG approach provides opportunities to apply BI on monitoring patients' pathways. Describing all the functionalities is out of the scope of this paper, however, modeling and analyzing, two of the main functions of this approach will be illustrated.

To clarify this method a study case has been developed to show how one could visualize and analyze the patients' pathways. In this experiment, an indoor-RTLS had been used to collect the location data. After installing the sensors and calibrating the location systems, patients received the tags. These tags communicate with sensors by UWB technology.

The transmitted signals would be calculated in the location engine by using TDOA and AOA measurement techniques. In this experiment, fourteen patients have been monitored. Each patient has a case ID based on the tag identification. In this case, there was no return of the same patient to the hospital. Twelve different activities have been identified based on the name of the zones in the hospitals. Each time a patient went inside or outside a section an event had been registered in the primary log file. The entities in the log file are related to the type of indoor-RTLS.

In this experiment, the log file consists of patients' ID, activities' details, timestamp start, and timestamp end. Figure 2 shows a part of the event log related to the patient 171. The primary event log consists of one hundred events, and it is transformed and prepared for the modeling function. As it has been shown, the first and last events related to the case 171 have the duration of "00:00". This indicates that these events are only instants while the process is being executed and they have been seen as a "passage" in the facility.

Figure 3 shows that the longest event for the patient 171 was staying in the "Consultation Box 1" to meet with the doctor. In addition to this data, the distance of the whole pathway was two hundred and sixty-two meters. In addition, it has been shown that the patient has spent approximately 54 minutes in the facility. Ability to reach such analysis is the result of applying RTLS and acquiring the accurate data related to the processes' executions. Now, the question is the value classification of these activities and which share of the process duration was value-added for the patients. This knowledge of the value class of activities is being provided by the healthcare experts. Furthermore, there is a need to select the data about one specific patient's pathway and to analyze it separately. To analyze the patient's pathways as the business processes, it would be formidable to acquire a qualitative analysis by displaying the process map and a quantitative analysis based on different aspects of the performance of the process. Thanks to process mining, figure 4 shows the process map related to the patient 171. Such process map provides a care-flow perspective on the patient's pathways. Explanations regarding the discovery algorithms are broader than the scope of this paper since the core and possible outcomes of DIAG approach is being illustrated in this article.

To acquire a performance perspective on process' execution, a Critical To Quality (CTQ) [START_REF] Montgomery | Introduction to statistical quality control[END_REF] has been defined for this experiment as "Process Efficiency" in the hospital. A quantitative indicator has been proposed as "Length of Stay" or duration to analyze the performance level of the processes based on the CTQ. The analysis related to the indicator has been shown in table 1.

These analyses are based on the value-class of the activities and the data coming from localization systems. By looking at the process efficiency, one could observe that the 85% of the process duration was related to executing the value-added activities and close to 15% of process duration was associated with the waiting time and staying in the queue. The decision-making actions are now dependent on the knowledge of the healthcare experts to determine how they can improve the non-value added parts of the process. Although, the simulation step could be a compatible solution to see several options for enhancing the situation. Indeed, one could infer that a logical decision could be made only by receiving the proper knowledge which is the aim of this approach. As it has been presented, this approach could support the quality of the decisions by providing the care-flow and performance perspectives on the processes.

CONCLUSION

This paper aimed at presenting the possibilities and advantages of applying process mining on location data to provide more awareness regarding the patients' pathways. This paper identified the DIAG approach which uses the indoor-RTLS, process mining, and business process management techniques to visualize, analyze and diagnose the patients' pathways. This approach embraces the hierarchy of data, information, awareness, and knowledge which extract the location data from the movements of the patients and transform these data to the knowledge. The real-time location data provide more accuracy in tracking patients' activities in the hospitals and process mining permits to model those activities as business process models. The advantage in this approach is in providing the quantitative and qualitative analysis based on the value class of the activities in the organizations.

To illustrate the possible outcomes of this approach an experiment has been performed at a hospital in France. Additionally, an overview has been provided for the major techniques and technologies in this approach.

To evolve this research work, the upcoming works are oriented towards refining this approach and providing the details about the rules and regulations for transforming the location data into prepared event logs for process mining tools.

Figure 2 .

 2 Figure 2. The event log for the case 171

Figure 4 .

 4 Figure 4. The process map for patient 171.

Table 1 .

 1 Process efficiency analysis based on Length of Stay.

	Activities (patient	Duration	VA/ NVA	Process
	171)			Efficiency
	Consultation	0	PASSAGE
	Department
	Waiting Room	4:39	NVA
	Registration Desk	9:02	VA
	Waiting Room 3	1:11	NVA
	Pre-Consultation Box	6:01	VA
				85.2 %
	Waiting Room 3	0	PASSAGE
	Consultation Box1	29:17	VA
	Registration Desk	2:37	VA
	Gate	2:19	NVA
	Exit area	0	PASSAGE