Information Ecosystem Reengineering

• Information Ecosystem Reengineering is a comprehensive approach that redesigns the relationships among digital resources, services, users, and governance to achieve native interoperability, traceability, and decision support.
• It utilizes semantic ontologies, knowledge graphs, and ecosystem-oriented architectures—such as blockchain integrations and personalized portals—to formalize policies and enable machine-actionable processes.
• IER transforms digital governance by unifying technical, organizational, and human factors, fostering resilient, adaptive, and transparent ecosystems across diverse application domains.

Information Ecosystem Reengineering (IER) denotes “the technological reconditioning of information sources, services, and systems within a complex information ecosystem” and is described as a foundational challenge in the digital transformation of public sector services and smart governance platforms (Bagchi, 21 Aug 2025). In the cited literature, IER encompasses semantic portals for academic management, sustainable Ecosystem-Oriented Architectures, converged science data ecosystems, integrated search frameworks, blockchain-based information ecosystems, and knowledge-graph-based ecosystem analysis. The common concern is not only the replacement of isolated components, but the redesign of the relations among information resources, services, users, governance structures, and analytic procedures so that interoperability, traceability, adaptability, and decision support become native properties of the ecosystem rather than after-the-fact additions.

1. Conceptual foundations

IER is grounded in an ecosystem view of information systems. One formulation states that information retrieval is “a key component of the digital ecosystems, where both information providers and information consumers collaboratively address their problems with the use of technologies,” and that the organization and design of such ecosystems drives particular social impact for all the players involved (Semenets et al., 2021). Another formulation extends conventional Service-Oriented Architecture by proposing Ecosystem-Oriented Architecture (EOA), whose ecosystem layer is intended to deliver “universal interoperability, transparent communication, automated management, self-integration, self-adaptation, and security” to interconnected services, components, and devices (Bassil, 2012).

The ecosystem concept also appears at the personal scale. “Personal information ecosystems” describe multiple devices and services as interdependent “organisms,” with information “flows” and an “equilibrium” state in which the user’s information environment is stable; the design implication is that devices should be designed “as part of a complete ecosystem, not as independent devices that simply share data replicated across them” [cs/0612081]. At the organizational scale, the sociotechnical literature defines IT as hardware plus software, and IS as “IT + People + procedures,” making explicit that the information ecosystem includes technical artifacts, organizational routines, and human actors rather than technology alone (Balloni et al., 2012).

These formulations converge on a shared premise: isolated optimization is insufficient. The sociotechnical governance model states that “any organizational systems will maximize performance only if the interdependency of these systems is explicitly recognized,” and the proposed objective is “mutual optimization of all systems” (Balloni et al., 2012). This directly opposes a device-centric, application-centric, or system-centric view of reengineering.

2. Semantic and ontological foundations

A major strand of IER is ontology-driven conceptual modeling. In the public-sector knowledge-representation literature, the central obstacle is “representation entanglement”: multilayered complexity spanning perception, language, ontological distinctions, hierarchies, and attribute-level specifics. The proposed response, “Representation Disentanglement,” unfolds over five layers—Perception, Labeling, Ontology, Hierarchy, and Intensionality—to resolve perceptual, linguistic, ontological, hierarchical, and intensional ambiguity (Bagchi, 21 Aug 2025). The hierarchy is modeled as a directed acyclic graph, and the final output can be a machine-processable ontology such as OWL. The theoretical basis explicitly draws on ontology-driven conceptual modeling, OntoClean, and Telos (Bagchi, 21 Aug 2025).

The university semantic portal known as the TRUST Portal operationalizes this semantic approach in an academic setting. All information resources are modeled as nodes in a semantic graph structured under a domain ontology; in addition, personal and organizational value ontologies capture quality preferences, and a service ontology wraps analytical and information retrieval services as semantic resources (Semenets et al., 2021). The stated effect is a “unified semantic space” in which academic resources, analytical services, and user profiles coexist, and in which analytics and rankings are natively value-driven and configurable (Semenets et al., 2021).

A related but broader knowledge-engineering approach appears in the Knowledge-based Tantra Social Information Management Framework for the Indian Agricultural Ecosystem. Tantra combines extensions to the Zachman Framework with concepts from Unified Foundational Ontology and implements ecosystem representations as knowledge graphs in Neo4J. Its analysis spans “descriptive, normative, and transformative viewpoints,” and it adds explicit treatment of relators, relationships, and separations, so that barriers such as informational, spatial, temporal, financial, capability, intellectual, and sociopolitical separations become first-class modeling objects (Prabhu et al., 2021).

The DICE group’s work on iRODS frames extensibility itself as knowledge capture. It argues that three extensibility mechanisms are sufficient for interaction with domain resources: “computer actionable rules that control management policies,” “computer executable micro-services that encapsulate operations or interaction protocols,” and “middleware servers that apply standard operations at remote locations” (Moore, 2013). Within IER, this is significant because reengineering is not only a matter of data remapping; it is also a matter of making policies, procedures, and interoperability knowledge explicit and machine-actionable.

3. Architectural patterns and infrastructure mechanisms

EOA presents one of the clearest architectural templates for IER. It extends the classical SOA model with a four-layer stack—Presentation Layer, Ecosystem Layer, Service Layer, and Data Layer—and locates sustainability functions in the ecosystem layer (Bassil, 2012). That layer contains six operational units: EMB (Ecosystem Management Bus), ECU (Ecosystem Communication Unit), EML (Ecosystem Management Language), EIU (Ecosystem Integration Unit), EWSU (Ecosystem WMI Scripting Unit), and ESU (Ecosystem Security Unit). Their roles are respectively data-path and messaging middleware, XML-based communication through Ecosystem Communication Language, automated management, self-integration and service registry, WMI-based self-adaptation, and centralized security management (Bassil, 2012). The access-control formalism is represented as

$R: S \times O \to \{\text{permissions}\}.$

Cybercosm proposes a different architectural response for scientific data ecosystems. Its core mechanism is a “minimally sufficient hypervisor” or “spanning layer” called the transvisor, which exposes a universal buffer API and separates a restricted data plane from a higher-level control plane (Asch et al., 2021). The design is explicitly intended to support “dramatically improved resource sharing, workflow portability and composability, and data ecosystem convergence” by avoiding storage, networking, and computing silos (Asch et al., 2021). Control-plane services then build higher-level abstractions such as file systems, federation policies, and workflow orchestration on top of the buffer substrate.

Blockchain-based Information Ecosystems (BBIEs) introduce yet another architectural decomposition. The proposed design separates on-chain shared logic and certification from off-chain enterprise systems and storage, and integrates blockchain services through a BaaS layer rather than replacing the existing information system wholesale (Salzano et al., 2024). The architecture includes validators, an Identity Management System, permissioned/private blockchain services, smart contracts, anchoring to an external public blockchain, and standardized API/ABI interfaces. The paper’s conclusion is explicit that the blockchain-based applications are integrated “with the existing information system as a module of the ecosystem” (Salzano et al., 2024).

Across these architectures, a recurring pattern is visible: reengineering proceeds by inserting a mediating layer that standardizes communication, integration, management, and policy enforcement while preserving heterogeneity below and configurability above. This suggests that IER is structurally closer to ecosystem-layer design than to monolithic system replacement.

4. Retrieval, analytics, and adaptive dynamics

IER is closely tied to information retrieval and analytic configuration. In the TRUST Portal, personalized executable queries incorporate a user’s value system directly into analytic functions and ranking criteria at query time. The personalized score is expressed as

$\text{Score}_u(r) = \sum_{i=1}^n v_i \cdot a_i,$

where $v_i$ are user-supplied weights and $a_i$ are normalized resource metrics (Semenets et al., 2021). A collective value system was at times approximated as

$CSV = \frac{1}{m} \sum_{j=1}^m PSV_j,$

but the portal’s evaluation reports pitfalls of averaging because the “wisdom of the crowd” can be undermined by social bias; a “League System” with Premier, First, and Second Leagues was introduced to address those distortions (Semenets et al., 2021).

The Integrated Search Framework (ISF) shows how ecosystem-level retrieval can combine crawling strategies, web search technologies, and traditional database search methods into “comprehensive, dynamic, personalized, and organization-oriented information retrieval services” across Internet, extranet, intranet, and personal desktop (Zhu et al., 2020). It uses prioritized seed URLs, PageRank-based crawling, ontology-based categorization, clustering, query expansion, user-profile weighting, and fine-grained access control. Its reported precision was 65.2%, compared with Google 49%, Yahoo 50%, Bing 62%, AOL.com 54%, and Baidu 59%; against Yahoo in the first experiment, P@5 improved from 46.7% to 77.5% and P@10 from 44.4% to 72.4% (Zhu et al., 2020).

At the ecosystem-dynamics level, empirical work on Twitter user–hashtag bipartite networks identifies “structural elasticity”: resting states are modular, extraordinary events induce rapid reorganization toward nestedness, and post-event recovery returns the network to modularity (Palazzi et al., 2020). The key empirical statement is that modularity $Q$ and nestedness $\mathcal{N}$ are anti-correlated, and simulations further predict the emergence of self-similar nested arrangements (Palazzi et al., 2020). For IER, the implication is that architecture and governance can be tuned not only for retrieval quality but also for resilience, diversity, and recovery under perturbation.

5. Governance, assessment, and sociotechnical change

IER is not reducible to technical integration. The university semantic portal is explicitly evaluated for its impact on “a collective mindset of the academic community of its users” (Semenets et al., 2021). Its reported effects include transparency and traceability in academic assessment, explicit articulation of personal value systems, bottom-up academic governance, public recognition, and competitive incentive schemes such as bonuses for top-ranked researchers (Semenets et al., 2021). The same portal was used over four years at Kharkiv National University of Radioelectronics and also in agency representative elections, making it an example of IER as a reconfiguration of decision procedures as well as information flows (Semenets et al., 2021).

Institutional modernization at IRD offers a detailed case of governance-centered reengineering. A legacy Excel-based annual activity report was replaced by an online form-based system co-designed through more than 25 workshops, and the resulting data were embedded into the broader information system through shared referentials and alignment with the Schéma Directeur du Numérique (Tostain, 2023). Multidimensional dashboards such as the “Azimut” interface provided pluriannual, filterable indicators, while FAIR principles, repeatability, traceability, and PDCA-based continuous improvement structured the change process (Tostain, 2023). Researchers were explicitly repositioned as “clients and co-owners of their data,” not only as data providers (Tostain, 2023).

The sociotechnical governance model makes this broader point in abstract form. Governance is positioned at the intersection of economic performance, environmental balance, and society needs, and the ecosystem information architecture integrates SCM, CRM, KM, BAM, XM, hardware, software, database, networks, and a corporate portal (Balloni et al., 2012). The claim is not merely that information systems support governance, but that governance is the organizing principle through which ecosystem sustainability is managed (Balloni et al., 2012).

Business reengineering cases in Metro Manila show a similar pattern at smaller organizational scale. Nineteen economic sectors were serviced by pre-industry system developers; 64 companies received Transaction Processing Systems in 2011–2012, and 85 companies received Management Information Systems in 2012–2013 (Jr, 2014). All projects followed a seven-phase SDLC, and the most common systems included Human Resources Management System, Payroll Management System, Enrolment Management System, Academic & Grading Management System, and Order Management System (Jr, 2014). In these cases, information systems functioned as “business driver and facilitator,” with reported effects on process improvement, business process reengineering, and customer satisfaction (Jr, 2014).

6. Domains, controversies, and open directions

IER has been instantiated across markedly different domains. Public-sector knowledge representation treats it as a requirement for explainability, traceability, semantic transparency, and auditable decision workflows in AI-driven governance ecosystems (Bagchi, 21 Aug 2025). The TRUST Portal situates it within academic information retrieval and assessment (Semenets et al., 2021). Tantra applies it to Indian agriculture through knowledge graphs and analysis of interventions, relators, and separations (Prabhu et al., 2021). Cybercosm reframes it for converged scientific data ecosystems (Asch et al., 2021). The Moorea IDEA project applies it to an “AI ecosystem avatar” using multimodal data and Deep Stacking Networks within a common computational framework (Barriot et al., 2021). BBIEs adapt it to decentralized, multi-organizational business models with permissioned blockchain, anchoring, and off-chain/on-chain decomposition (Salzano et al., 2024).

Several controversies and limitations recur. First, the literature disputes naive aggregation. In academic ranking, averaging personal value systems was found to reject standout individuals, and the “League System” was introduced precisely because “crowd wisdom requires careful modeling” (Semenets et al., 2021). Second, decentralization is presented as modular integration rather than total displacement: the blockchain architecture explicitly preserves legacy systems and uses blockchain “as a module of the ecosystem” (Salzano et al., 2024). Third, some proposals remain conceptual. Representation Disentanglement is stated to be “currently at a conceptual stage,” with detailed technical treatments and large-scale case studies planned for future work (Bagchi, 21 Aug 2025). Fourth, identity technologies remain unsettled: the BBIE study notes the promise of Self-Sovereign Identity while also stating that industrial adoption is currently limited by interoperability challenges (Salzano et al., 2024). Finally, EOA itself leaves open a future extension toward “computational intelligence to help in decision making and problem solving” (Bassil, 2012).

A common misconception is that IER is equivalent to interface modernization or platform migration. The cited literature consistently presents a broader object: semantic modeling, policy formalization, interoperability layers, adaptive analytics, governance redesign, sociotechnical fit, and, in several cases, explicit cultural change. In that sense, IER names not a single architecture or methodology, but a family of reengineering practices for ecosystems in which information resources, analytic services, organizational rules, and users must inhabit a coordinated and inspectable whole.