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Mid‑Career Shift In the late 1990s, he moved to the research department of the V Institute in M, focusing on automation and robotics. He led a team that developed an automated inspection system for automotive assembly lines. Later Years & Retirement After 15 years at V, he became head of the Robotics Laboratory. He published several papers in journals such as IEEE Transactions on Automation. Retired in 2018, now enjoys gardening and volunteer work at a local community center. 3.2.5. Handling Data Quality Issues Missing Fields: If certain fields (e.g., middle name) are absent, we set the attribute to `None` or an empty string. This avoids erroneous values. Inconsistent Naming: We normalize names by converting to uppercase and stripping whitespace. For multi‑word surnames ("de la Cruz"), we preserve them as is but treat them consistently across records. 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Example: Representing a Person John Doe 30 Main St. Anytown 12345 5. JSON (JavaScript Object Notation) 5.1. Overview JSON is a lightweight, language‑agnostic data interchange format that originated from JavaScript object literal syntax. It has become ubiquitous in web services and APIs due to its simplicity and compatibility with many programming languages. 5.2. Why Use JSON? Readability: Human‑readable text with minimal syntactic overhead. Language Interoperability: Native support or libraries available in virtually all major languages. Compactness: No need for tags; data is represented as key/value pairs, arrays, and nested objects. Performance: Faster parsing and serialization compared to XML or RDF. 5.3. 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The Imperative of Standardized Identification for Scholarly Visibility The adoption of unique identifiers—whether through an established system or a custom solution—offers tangible benefits for scholars who wish to increase their visibility in the academic ecosystem. Consider the following scenarios: Search Engine Optimization (SEO) for Academic Profiles: By ensuring that all scholarly outputs are linked via consistent identifiers, researchers can improve the discoverability of their work by search engines and institutional repositories. Cross-Institutional Collaboration: When multiple institutions adopt a common identifier system, it becomes easier to track joint publications, grant contributions, and shared datasets, fostering transparent collaboration metrics. Citation Tracking and Impact Assessment: Accurate author identification allows for precise citation counts and h-index calculations, reducing the likelihood of misattributed work or inflated metrics due to name ambiguity. Thus, adopting a standardized author identifier is not merely an administrative convenience; it is a strategic investment in scholarly visibility and integrity. (b) The Role of Digital Object Identifiers (DOIs) and Other Identifier Schemes In the realm of scholarly publishing, Digital Object Identifiers (DOIs) have emerged as the de facto standard for uniquely identifying digital content—articles, datasets, conference proceedings. A DOI is a persistent alphanumeric string that resolves to the current location of the object via the DOI system’s registry and resolver services. Its key properties include: Uniqueness: Each DOI corresponds to exactly one resource. Permanence: The DOI remains constant even if the underlying URL changes, ensuring long‑term discoverability. Metadata Integration: DOIs are typically associated with rich metadata (title, authorship, publication venue) facilitating discovery and citation. While DOIs excel at identifying content, they do not encode identity in a way that distinguishes between different individuals who may share the same name. Consequently, in bibliographic databases, multiple researchers named "J. Wang" could be conflated under a single DOI‑based identifier if only publication data is considered. This underscores the need for an identity‑centric system that explicitly models personhood, attributes, and relationships. 3. Ontology Design 3.1 High‑Level Conceptualization We propose a lightweight ontology tailored to representing researchers, their professional affiliations, and scholarly contributions. The core entities (classes) include: Person: The central class representing an individual researcher. Institution: Academic or research organizations (universities, laboratories). Position: Employment or academic roles (Professor, Post‑Doc, Research Associate). ResearchGroup: Collaborative clusters within institutions. Publication: Scholarly outputs (journal articles, conference papers). FieldOfStudy: Domains of expertise (Computer Science, Mathematics, Biology). Relationships capture the dynamic associations between these entities: `hasAffiliation`: Links a Person to an Institution or ResearchGroup. `holdsPosition`: Connects a Person to a Position within an Institution. `contributedTo`: Associates a Person with a Publication. `belongsToField`: Relates a Person or Publication to a FieldOfStudy. This schema aligns with established data models such as the Scholarly Graph and adheres to semantic web principles via RDF triples, facilitating interoperability and reasoning across heterogeneous datasets. 3. Data Acquisition Pipeline The acquisition strategy is a multi‑stage process designed to aggregate high‑quality, up‑to‑date information from diverse sources while respecting legal and ethical boundaries. 3.1 Source Identification and Prioritization Rank Source Type Example Justification 1 Institutional repositories (IR) arXiv, institutional archives Primary data, often open access, high authority 2 Bibliographic databases CrossRef, PubMed, Web of Science Structured metadata, DOIs, citation links 3 Researcher‑controlled platforms Google Scholar profiles, ORCID records Direct researcher identifiers, affiliation updates 4 News & press releases university news portals Contextual events (funding, awards) 5 Social media / blogs Twitter accounts of researchers Real‑time updates, informal communication The extraction pipeline will prioritize institutional repositories and bibliographic databases to ensure authoritative data. 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BeautifulSoup (Python), lxml, json module Normalizer Cleans whitespace, normalizes URLs, removes fragments, standardizes date/time formats. regex, datetime library Validator Checks field types, requiredness, value ranges; logs violations with severity levels. Custom validation functions Storage Layer Persists validated documents to a NoSQL database or document store; maintains indexes on key fields. MongoDB, Elasticsearch, DynamoDB Error Handling & Logging Emits structured logs for each error, includes source record ID, field path, and context. Logback, Winston, CloudWatch Monitoring Dashboard Visualizes ingestion metrics: throughput, error rates, field-specific violations over time. Grafana, Kibana --- 2.4 Handling "What‑If" Scenarios Scenario A: Sudden Surge in Input Volume Observation: The system receives a tenfold increase in documents per second due to a marketing campaign. Impact on Validation Flow: - Parsing Bottleneck: JSON parser may become CPU bound; memory usage spikes. - Thread Pool Saturation: Worker threads may queue, increasing latency. - Error Queue Overflow: Temporary buffer for malformed records could be exhausted. Mitigation Strategies: - Autoscaling Workers: Dynamically spawn additional parsing workers based on CPU utilization thresholds. - Back‑pressure Mechanisms: Implement flow control to slow ingestion from upstream sources when downstream cannot keep up. - Priority Queues: Separate high‑priority (valid) and low‑priority (invalid) records to prevent stalled processing of critical data. --- 5. Failure Modes, Recovery Strategies, and Testing 5.1 Potential Failure Scenarios Scenario Description Impact Network Partition Loss of connectivity between ingestion nodes and storage cluster. Ingestion stalls; potential data loss if buffering insufficient. Storage Node Crash One or more database replicas fail. 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Health Checks and Auto‑Healing - Expose `/health` endpoints; orchestrate restarts of unhealthy pods automatically via Kubernetes liveness probes. Graceful Shutdown Hooks - On SIGTERM, stop accepting new requests, flush in‑memory buffers to Kafka, wait for all messages to be acknowledged before exiting. 4. Summary of Key Design Choices Decision Rationale Event‑driven architecture Decouples request handling from persistence; enables buffering and retries without blocking user requests. Kafka as message bus Provides durability, ordering, scalability, and a retry mechanism via consumer offsets. MongoDB for storage Offers flexible schema, built‑in indexing on `userId`, efficient retrieval of events by user. Consumer group Allows horizontal scaling of event processing; each consumer processes a partition. Retry queue with exponential backoff Prevents overwhelming the system during transient failures and avoids tight loops. 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