System Design: Building Data Intensive Applications Lesson

Hello Everyone,
Here is this is System Design: Building Data Intensive Applications Lesson #016- Triple Store & SPARQL:
Timestamps:
Concept: 0:00
Concept Example: 3:27
Practice Problem 1: 9:58
Practice Problem 2: 12:41
If have any questions below feel free to put them in the comment section. Thank you!
Best,
Gunnar
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[@GChief117]

Practice Problem 1: An Academic Research Network Let's conjure an academic research network as our practice canvas. Here, researchers, papers, and citations are subjects, predicates are the relationships such as "authored by," "cites," or "reviews," and objects could be other researchers, papers, or affiliations. Step 1: Establishing the Network We begin by mapping researchers to their works and citations, employing triple-stores to represent each discrete fact - a paper, its author, and its influences. Step 2: Querying Connections Using SPARQL, we can craft queries to reveal the most influential papers in a field, trace the evolution of a theory, or even identify potential peer reviewers based on their expertise and previous citations. Step 3: Interpreting the Network Dynamics The resulting web of connections offers us a bird's-eye view of the academic landscape, highlighting key players and pivotal works, much as we would map out a city's landmarks and thoroughfares. # Step 1: Establishing the Network PREFIX dc: PREFIX foaf: CONSTRUCT { ?paper dc:creator ?author . ?paper dc:title ?title . ?author foaf:name ?name . } WHERE { ?paper dc:creator ?author . ?author foaf:name ?name . ?paper dc:title ?title . } # Step 2: Querying Connections SELECT ?paper (COUNT(?cites) AS ?numCitations) WHERE { ?paper dc:creator ?author . ?cites dc:references ?paper . } GROUP BY ?paper ORDER BY DESC(?numCitations) Practice Problem 2: Ecological Interaction Network Now, consider an ecological interaction network where species (subjects) are connected by interactions (predicates) like "preys on," "pollinates," or "competes with," and the objects may be other species or environmental features. Step 1: Mapping the Ecosystem We construct a triple-store that encapsulates each interaction, forming a complex web of life within our data model. Step 2: Querying Interactions SPARQL enables us to ask which species are keystone to a habitat, understand the flow of energy through food webs, or assess the impact of an invasive species. Step 3: Understanding the Ecosystem's Balance The insights we gain from these queries help us comprehend the delicate balance of ecosystems, guiding conservation efforts and policy-making to preserve biodiversity. PREFIX eco: # Step 1: Mapping the Ecosystem CONSTRUCT { ?species eco:interactsWith ?otherSpecies . } WHERE { ?species eco:interactsWith ?otherSpecies . } # Step 2: Querying Interactions SELECT ?species (COUNT(?interactsWith) AS ?importance) WHERE { ?species eco:interactsWith ?interactsWith . } GROUP BY ?species ORDER BY DESC(?importance) # Step 3: Understanding the Ecosystem's Balance # This query might look for species without interactions, which could indicate an issue in the ecosystem. SELECT ?species WHERE { FILTER NOT EXISTS { ?species eco:interactsWith ?other . } FILTER NOT EXISTS { ?other eco:interactsWith ?species . } }

[@elijah8684]

Promo sm 😃