编程辅导 LGP modeling (1)

LGP modeling (1)
• Graph modeling diverges from traditional relational modeling
• Once the domain has been captured
• Relational: definition of schemas

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• Graph: enrich the domain with information needed by applications (entities as nodes, roles as labels, connections to neighboring entities as relationships , attributes as properties)
• Two main goals
• data-centric designàcorrect labels an properties to nodes
• semantic contextànamed and directed relationships between nodes

LGP modeling (2)
• Graphs are naturally addic1ve
• New kinds of rela1onships, nodes, labels, and subgraphs can be added to an exis1ng structure without impac1ng exis1ng queries and applica1on func1onali1es
• Structures and schemas can emerge incrementally with the understanding of the problem space
• Data are connected as the domain dictates

LGP modeling (3)
• Applica’on needs always in mind
• Example:
AS A reader who likes a book, I WANT to know which books other readers who like the same book have liked, SO THAT I can find other books to read.

LGP modeling (4)
• There is not a unique set of rules, but generally
• En55es of interest: nodes (can be labeled and grouped)
• Connec5ons between en55es and seman5c context of each en5ty: rela5onships
• They give structure to the domain
• Clarifica5on of the seman5cs of a rela5onship: direc5on and proper5es of the rela5onship
• For bidirec5onal rela5onships, a rela5onship exists for each direc5on
• En5ty aCributes and metadata: node proper5es
• Timestamps, version numbers…
• Strength, weight, or quality of a rela5onship and metadata: rela5onship proper5es

Granularity of relationships
• Relationships can be defined at different granularity levels
• Example: relationships between a user, her delivery address and her billing
address can be represented via • Two relationships only
• One relationship address specified by properties only • Both solutions

Emerging facts
• When two or more domain en..es interact for a period of .me, a fact can emerge
• It is possible to represent it as a separate node with connec.ons to each of the en..es engaged in that fact
• The addi.onal node can represent the result of the interac.on among the en..es

Analyzing LPGs
• Graph analy*cs: use of any graph-based approach to analyze connected data
• Graph algorithms use the rela*onships between nodes to infer the organiza*on and dynamics of complex systems
• Pa=ern–based querying is used for local data analysis • Rela*onships in a graph naturally form paths
• Querying or traversing the graph involves following paths

Pattern-based queries: Cypher (1)
• Declara’ve query language for graphs
• Idea: query for pa7ers represen’ng what should be returned
(emil)<-[:KNOWS]-(jim)-[:KNOWS]->(ian)-[:KNOWS]->(emil)
• This pattern describes in ASCII a path that connects nodes • Basic pattern idea: (node)-[relationship]->(node) •emil,jimandianareuse asidentifiers

Pa#ern-based queries: Cypher (2)
• Declara’ve query language for graphs
• Idea: query for pa7ers represen’ng what should be returned
(emil:Person {name:’Emil’})
<-[:KNOWS]- (jim:Person {name:'Jim'}) -[:KNOWS]->(ian:Person {name:’Ian’}) -[:KNOWS]->(emil)
• Iden’fiers now bounded to graph data through their • Name property
• personlabel

Pattern-based queries: Cypher (3)
• A Cypher query
• Anchors one or more parts of a pa3ern to specific loca7ons in a graph using
predicates
• Flexes the unanchored parts around to find local matches
• The anchor points in the real graph are determined based on the labels and property predicates in the query.
• Metainforma7on about exis7ng indexes, constraints, and predicates can be used
• A Cypher query is composed of clauses

Pattern-based queries: Cypher (4)
• The match clause represents nodes and relationships we are interested into
• specification by example
MATCH (a:Person{name:’Jim’})-[:KNOWS]->(b)-[:KNOWS]->(c), (a)-[:KNOWS]->(c)
RETURN b, c
• Nodes are represented with parenthesis
• Node labels prefixed by colon
• Rela:onships are represented with arrows
• Rela:onship names prefixed by colon and within squared brackets • Key-value pair proper:es are specified within curly brackets

Pattern-based queries: Cypher (5)
• WHERE: specifies filter for pa.ern matching results
• CREATE [UNIQUE]: creates of nodes and rela5onships
• MERGE: returns a pa.ern or adds it to the graph it does not exist
• DELETE: removes nodes, rela5onships, proper5es
• SET: sets property values
• FOREACH: executes an update opera5on for all provided elements • UNION: merges results from queries

Pattern-based queries: Cypher (examples – 1)
(shakespeare:Author {firstname:’William’, lastname:’Shakespeare’}),
(juliusCaesar:Play {title:’ ’}),
(shakespeare)-[:WROTE_PLAY{year:1599}]-> (juliusCaesar),

Pattern-based queries: Cypher (examples – 2)
• MATCH (n) RETURN n
• MATCH (p:Play) RETURN p
• MATCH (p:Play) RETURN p.title
• MATCH (a:Author)-[w:WROTE_PLAY]-> (p:Play{title:” “})
• MATCH (a:Author)-[w:WROTE_PLAY]->(p:Play) WHERE p.title = ” ”
RETURN a,w,p
RETURN a,w,p
(theater:Venue {name:”Theatre Royal”}), (newcastle:City {name:”Newcastle”}), (bard:Author {lastname:”Shakespeare”}), (newcastle)<-[:STREET|CITY*1..2]-(theater)<- [:VENUE]-()-[:PERFORMANCE_OF]->() -[:PRODUCTION_OF]->(play)<-[:WROTE_PLAY] -(bard) RETURN DISTINCT play.title AS play Graph algorithms • Besides pa(ern-based local queries, graphs can be analyzed for more general analysis • Examples: • Pathfinding • Community detecAon • Centrality • To this end, graph DBMSs can be equipped with libraries that support advanced analyAcs, typically based on graph theory • BFS, DFS, single source/all sources shortest path, minimum cost spanning tree, random walks Neo4J: Transactions • Transac'ons are supported by Neo4j • Writes occur within a transac'on context, write locks on any nodes and rela'onships involved in the transac'on • On successful comple'on of a transac'on, changes are flushed to disk and the write locks released • On failure, writes are discarded and the write locks releasedàconsistent state of graph • If two transac'ons aCempt to write same graph elements concurrently, Neo4j detect a poten'al deadlock and serialize the transac'ons • Writes within a single transac'onal context are not visible to other transac'ons • Neo4j is ACID-compliant Graph databases: use cases Suitable for: • Rou/ng, dispatch, and loca/on-based services • Financial services, fraud detec/on • Social domains • Recommenda/on engines • Contact tracing Not suitable for: • Whenever the graph model does not represent well the data • Resource Description Framework • Goal: provide a framework for describing resources • Main elements: • Resources to be describedàanything that can have a unique identifier (URI) • Properties associated with resources • Classes used to bucket resources • RDF data composed as triples