Process Navigator: Integrated Analysis

• Process Navigator is an integrated methodological strategy that facilitates systematic exploration, comparative analysis, and optimization of processes using techniques like behavioral modeling and narrative transformation.
• It employs quantitative metrics, simulation tools, and interactive interfaces to diagnose bottlenecks and drive improvements in domains such as ecommerce, ERP systems, and robotic navigation.
• These frameworks offer practical applications by supporting evidence-based design and adaptive systems across diverse technical ecosystems, enhancing user experience and operational efficiency.

A Process Navigator refers to an integrated methodological or technological approach that enables users—whether process owners, analysts, or autonomous systems—to systematically explore, compare, or optimize processes by guiding navigation, understanding structure and behavior, and supporting targeted intervention. In contemporary research and applications, Process Navigators encompass frameworks for behavioral modeling, interactive process mining, comparative analysis, automated process identification, robot navigation, and narrative transformation of process models.

1. Behavioral-Based Process Navigation in Information Networks

Process navigation, in the context of user behavior across information networks, is exemplified by behavioral-based stochastic modeling methodologies as applied to electronic commerce environments (Kumbaroska et al., 2017). User navigation patterns, systematically extracted from log data (e.g., in an electronic bookstore), reveal recurrent sequences such as 40% of users starting with category “A” pages and nearly half revisiting them, or characteristic transitions between personalized (“C”) and purchasing (“D”) pages occurring with 32%–62% probabilities. Such analysis informs redesign efforts to prolong user engagement and drive sales.

The core of this behavioral-based Process Navigator is a Generalized Stochastic Petri Net (GSPN) model, where web pages act as places (A–D for product, information, user, and purchase categories) and transitions—parameterized by firing rates—capture user actions. Session logs are clustered into homogeneous groups reflecting navigation behavior, forming the basis for interface personalizations. The dynamic solution space of GSPNs maps onto Continuous Time Markov Chains (CTMCs), enabling computation of key performance measures:

• Average sojourn time in a transient state:

$ST_i = \frac{1}{\sum_{j \in E(M_i)} \text{rate}_j}$

• Expected time spent before absorption:

$X_{QN} = - T^{(0)} Q_N^{-1}$

• Total number of visits:

$NE(I - IIE) = T^{(0)}$

• Cumulative sojourn time in a transient marking:

$O_i = NE_i \cdot ST_i$

These quantitative metrics provide the foundation for data-driven user experience improvements and adaptive process modifications, illustrating how Process Navigators can maximize system efficacy and business objectives.

2. Interactive Process Mining and Simulation

Process Navigators manifest as interactive platforms that combine process mining and simulation for organizational process improvement. The SIMPT tool (Pourbafrani et al., 2021), a Python/Django-based web application, automatically extracts process structures and performance parameters from event logs, representing them as process trees with sequence, parallel, loop, and XOR operators. This tree abstraction is favored over Petri nets for usability.

Users interactively modify process parameters such as activity durations, resource configurations, and business hours, and can re-simulate using discrete event simulation (SimPy). The simulation generates new event logs capturing projected process behavior under altered conditions, enabling “what-if” scenario evaluation. Conformance checking (e.g., Earth Mover’s Distance, EMD=0.34) assesses simulation reliability relative to historical data.

This framework empowers evidence-based process redesign, facilitating the identification and elimination of bottlenecks, optimizing throughput and resource utilization, and supporting iterative improvement cycles. Domains of application include manufacturing (IoP projects), production floor reconfiguration, and automotive process analysis.

3. Comparative and Differential Process Analysis

A Process Navigator framework can embody differential process mining tools that enable the comparative analysis of process variants (Narayana et al., 2022). Utilizing Python/Django and the PM4Py process mining library, these tools accept event logs, apply user-defined filters, and produce Directly-Follows Graphs (DFGs) to visualize process flow dynamics.

The interface affords simultaneous display and side-by-side comparison of DFGs under distinct constraints, automatically highlighting unique activities and transitions (red coloring for discrepancies) to pinpoint process differences. Statistical summaries such as trace counts and average running times facilitate diagnostic interpretation, while export features enable further external analytics.

A formal description employs:

• For each event pair $(a, b)$ in traces $\sigma_i$:

$N(a, b) += 1$

• Resulting DFG:

$$DFG = (A, E) \quad \text{with} \quad E = \{(a, b) \mid N(a, b) > 0\}$$

• Model comparison:

$$\Delta(A, E) = \left( DFG_{L_1} \setminus DFG_{L_2} \right) \cup \left( DFG_{L_2} \setminus DFG_{L_1} \right)$$

This approach supports navigation across process states and structures under varying operating conditions, aiding root cause analysis and optimization.

4. Automated Process Identification in Large Enterprise Systems

Process Navigators extend to automated process identification in complex ERP environments, such as SAP (Weber et al., 2022). The Interactive SAP Explorer encodes the relational structure of SAP ECC instances into Neo4j-based labeled property graphs, where nodes represent tables or document types and edges denote relationships (e.g., foreign keys).

Users select a core process (e.g., purchase orders), and the system expands relevant table connectivity via graph traversal:

$G = (V, E, L, P)$

where $V$ is nodes, $E$ is edges, $L$ labels (e.g., “SAP_Table”), and $P$ are properties. Advanced query support enables extraction of process-relevant subgraphs for event log assembly. The subsequent in-memory extraction, composition, and assembly output event logs in OCEL format, suitable for process mining. Example use cases include mapping full Procure-to-Pay or Order-to-Cash process lifecycles.

Current limitations include support for only SAP ECC/Oracle, memory-dependent extraction scalability, and basic activity/timestamp definitions. Future enhancements target wider system compatibility, improved data handling, and refined semantics for greater process granularity.

5. Visual Robot Process Navigation via Pareto-Optimal Mapless Methods

In the domain of autonomous robotic navigation, Process Navigator methodologies are realized by Pareto-optimal mapless frameworks such as POVNav (Pushp et al., 2023). POVNav enables robotic agents to navigate environments using only a monocular camera, eschewing reliance on prior maps.

The system segments incoming images into navigable ($\Omega_N$) and non-navigable ($\Omega_{NN}$) classes, producing a binary navigability mask $I_t^b$. It then computes the “Visual Horizon” $\mathcal{H}_t$ as the boundary between these classes, formalized as:

$$\mathcal{H}_t \triangleq \{ p(\psi_t(y), y) : \forall y \in [-w/2, w/2] \}$$

Where navigable pixels below and obstacles above meet for each column. Pareto-optimal sub-goal selection balances angular deviation (heading alignment with projected Peripheral Optic Goal $p^g$) and Euclidean distance from current robot state $p^s$:

$p^* = \arg\min_p [w_1 f_1(p) + w_2 \|p - p^g\|]$

Visual servo control transforms dynamic path features—proximity ($\lambda$) and alignment ($\varphi$)—into velocity commands $(v, \omega)$, yielding collision-free navigation:

$$e(t) = s(I^n_t, p^*) - s^* = [\lambda, \varphi] - [\lambda_0, 0]$$

Flexible definition of navigability via $\Omega$ enables selective terrain navigation, as demonstrated in real-world trials on robots in indoor corridors, forest trails, and roads with varying snow coverage. The entire processing pipeline is computationally efficient (0.04s per action), supporting rapid deployment on resource-constrained platforms.

6. Interactive Narrative-Based Process Navigation

Narrative transformation tools, such as the Scripting Your Process (SYP) method (Ferreira et al., 25 Mar 2024), provide a Process Navigator that reinterprets process models (BPMN) for accessibility and training. The method extracts process steps as structured sentences via grammar rules, with events, activities, and gateways mapped into narrative elements described in Table 1 of the source.

Completeness ($MQ_1$) and correctness ($MQ_2$) metrics are defined as ratios of extracted or correct sentences to expected sentences:

• Completeness:

$$MQ_1 = \frac{\text{Number of Extracted Sentences}}{\text{Number of Expected Sentences}}$$

• Correctness:

$$MQ_2 = \frac{\text{Number of Correct Sentences}}{\text{Number of Expected Sentences}}$$

A quasi-experimental paper indicated high levels of completeness (98%) and correctness (86%) in model-to-narrative translation by game design students (non-experts in BPMN). The system supports output in Ink for web-based interactive experiences, bridging formal process documentation and narrative-driven engagement, with potential applications in training, citizen engagement, and serious games.

7. Synthesis and Impact of Process Navigator Paradigms

Process Navigators present a spectrum of solutions—behavioral modeling, interactive simulation, comparative analytics, automated identification, robotics, and narrative conversion—addressing diverse technical ecosystems and analytical requirements. Common principles include multi-level abstraction (from log data to formal models), support for variant analysis, simulation capabilities, adaptive definition of process states, and accessibility through user-centered interfaces or narrative scaffolding.

These approaches collectively drive actionable insights, process optimization, adaptive system design, and stakeholder collaboration. Scalability, integration into heterogeneous environments, and advancement in algorithms and user experience are the primary vectors for future innovations in Process Navigator technologies.