MARKLOGIC SERVER

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interest? Alerting gives you that. How about when an earthquake occurs in a certain city? Define the city as a geospatial query in the reverse index and check for matching latitude/ longitude data when earthquakes occur. Without reverse query indexing, for each new document or set of documents, you'd have to loop over all of your queries to see which ones match. As the number of queries increases, this simplistic approach becomes increasingly inefficient. You can also use reverse queries for rule-based classification. MarkLogic includes an SVM (support vector machine) classifier. Details on the SVM classifier are beyond the scope of this book, but suffice to say it's based on document training sets and finding similarity between document term vectors. Reverse queries provide a rule-based alternative to training-based classifiers. You define each classification group as a cts:query. That query must be satisfied for membership. With reverse query, each new or modified document can be placed quickly into the right classification group or groups. Perhaps the most interesting and mind-bending use case for reverse queries is for matchmaking. You can match for carpools (driver/rider), employment (job/resume), medication (patient/drug), search security (document/user), love (man/woman or a mixed pool), or even battle (target/shooter). For matchmaking you represent each entity as a document. Within that document, you define the facts about the entity itself and that entity's preferences about other documents that should match it, serialized as a cts:query. With a reverse query and a forward query used in combination, you can do an efficient bi-directional match, finding pairs of entities that match each other's criteria. A REVERSE QUERY CARPOOL MATCH Let's use the carpool example to make the idea concrete. You have a driver, a nonsmoking woman driving from San Ramon to San Carlos, leaving at 8:00am, who listens to rock, pop, and hip-hop, and wants $10 for gas. She requires a female passenger within five miles of her start and end points. You have a passenger, a woman who will pay up to $20. Starting at "3001 Summit View Drive, San Ramon, CA 94582" and traveling to "400 Concourse Drive, Belmont, CA 94002." She requires a non-smoking car and won't listen to country music. A matchmaking query can match these two women to each other, as well as any other matches across potentially millions of people, in sub-second time. 84
This XQuery code inserts the definition of the driver—her attributes and her preferences: let $from := cts:point(37.751658,-121.898387) (: San Ramon :) let $to := cts:point(37.507363, -122.247119) (: San Carlos :) return xdmp:document-insert( "/driver.xml", {$from} {$to} 2010-01-20T08:00:00-08:00 female no rock, pop, hip-hop 10 { cts:and-query(( cts:element-value-query( xs:QName("gender"), "female"), cts:element-geospatial-query(xs:QName("from"), cts:circle(5, $from)), cts:element-geospatial-query(xs:QName("to"), cts:circle(5, $to)) )) } ) 85
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Inside MARKLOGIC SERVER Jason Hunte
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You can find the full set of API do
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CHAPTER 1 WHAT IS MARKLOGIC SERVER?
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enforced, such as that no two docum
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You can even use MarkLogic to enfor
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instance, all the way up to (in 201
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Doc1 Doc 2 Doc 3 Doc 4 a blue car t
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INDEXING LONGER PHRASES What happen
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INDEXING VALUES Now what if we want
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The indexes don't know if they're t
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for $result in cts:search( /article
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DIRECTORY INDEXES MarkLogic include
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Every fragment acts as its own self
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4. Perform optimized order by calcu
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constraint (term lists are of no us
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Performance of range index operatio
Page 33 and 34: Fields are another way to pinpoint
Page 35 and 36: DATABASE MyDocuments FOREST MyFores
Page 37 and 38: stands. Merges tend to be CPU- and
Page 39 and 40: When doing point-in-time queries, y
Page 41 and 42: Isolating an Update When a request
Page 43 and 44: timestamp to make the fragment live
Page 45 and 46: if the global commit happened or no
Page 47 and 48: CLUSTERING AND CACHING As your data
Page 49 and 50: Expanded Tree Cache Each time a D-n
Page 51 and 52: In the regular heartbeat communicat
Page 53 and 54: QUERY QUERY LIFECYCLE 7 RESULT SET
Page 55 and 56: Figure 9: During a commit involving
Page 57 and 58: other transactions as the documents
Page 59 and 60: MODULES AND DEPLOYMENT XQuery, XSLT
Page 61 and 62: REST API FOR MULTI-TIER DEVELOPMENT
Page 63 and 64: SEARCH AND JSEARCH APIS The Search
Page 65 and 66: SQL/ODBC ACCESS FOR BUSINESS INTELL
Page 67 and 68: CHAPTER 3 ADVANCED TOPICS ADVANCED
Page 69 and 70: MarkLogic provides basic language s
Page 71 and 72: as a space removed from all text, s
Page 73 and 74: If instead of matching documents th
Page 75 and 76: You can watch as the server does th
Page 77 and 78: MORE WITH FIELDS Fields also provid
Page 79 and 80: The cts:register() call returns an
Page 81 and 82: value but a latitude and longitude
Page 83: produces this XML: dog name Ch
Page 87 and 88: It searches across passengers, requ
Page 89 and 90: five.xml (doc ID 5): { cts:and-quer
Page 91 and 92: Valid Start: 2016-01-01 End: ∞ Sy
Page 93 and 94: BITEMPORAL QUERIES Querying on bite
Page 95 and 96: A key aspect of semantic data is no
Page 97 and 98: Triple Type Index Object values in
Page 99 and 100: estimates the efficiency of that pl
Page 101 and 102: MANAGING BACKUPS MarkLogic supports
Page 103 and 104: esult of a code bug, modifies data
Page 105 and 106: and Local-Disk Failover. Failover w
Page 107 and 108: andwidth. Local-Disk is faster when
Page 109 and 110: Contemporaneous vs. Non-Blocking Ea
Page 111 and 112: database allowed to have most of it
Page 113 and 114: threshold needs to be reached among
Page 115 and 116: TIERED STORAGE All storage media ar
Page 117 and 118: storage media but still query those
Page 119 and 120: LOW-LEVEL SYSTEM CONTROL When scali
Page 121 and 122: OUTSIDE THE CORE That completes our
Page 123 and 124: CONNECTOR FOR SHAREPOINT Microsoft
Page 125 and 126: Sublime Text Plug-in An add-on to t
Page 127: 999 Skyway Road, Suite 200 San Carl
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