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Invited Talk University of Athens October 21, 2008

Invited Talk University of Athens October 21, 2008. MashQL. Towards Data Mashups and Pipes. Dr. Mustafa Jarrar mjarrar@cs.ucy.ac.cy HPCLab, University of Cyprus.

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Invited Talk University of Athens October 21, 2008

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  1. Invited Talk University of Athens October 21, 2008 MashQL Towards Data Mashups and Pipes Dr. Mustafa Jarrar mjarrar@cs.ucy.ac.cy HPCLab, University of Cyprus Reading: Mustafa Jarrar and Marios D. Dikaiakos: MashQL: A Query-by-Diagram Topping SPARQL -Towards Semantic Data Mashups. In ONISW’08 workshop, part of the CiKM'08 confernce, ACM. 2008 http://www.jarrar.info/publications/JD08.pdf

  2. Imagine We are in 3008. The internet is a database Information about every little thing Structured, granular data Semantics, linked data (oracle?) How we will yahoo/google this knowledge !!?

  3. Outline • The Data Web and the role of Mashups • Mashup Challenges • MashQL (A new Mashup Language) • Conclusions and Discussion Jarrar-University of Cyprus

  4. Web 2.0 and the phenomena of APIs API Jarrar-University of Cyprus

  5. Web 2.0 and the phenomena of APIs Wikipedia in RDF API

  6. Web 2.0 and the phenomena of APIs API

  7. Web 2.0 and the phenomena of APIs API

  8. Web 2.0 and the phenomena of APIs Also supports microformats/RDFa API

  9. Web 2.0 and the phenomena of APIs API

  10. Web 2.0 and the phenomena of APIs API

  11. Web 2.0 and the phenomena of APIs And many, many others APIs API

  12. Web 2.0 and the phenomena of APIs • Moving to the Data Web, in parallel to the web of documents. Jarrar-University of Cyprus

  13. Mashups An application that combines data from multiple sources (APIs). AthensTruismPortal SOA SOA Map Event Tours Jarrar-University of Cyprus

  14. Mashups An application that combines data from multiple sources (APIs). (A puzzle of APIs) (API1+ API2) + API3 = money Jarrar-University of Cyprus

  15. Mashups (Example) Combines Google maps with real-estate databases Google Maps Real-estate

  16. Mashups (Example) A unified and comprehensive view of the current global state of infectious diseases and their effect on human and animal health Google News ProMED World Health Organization

  17. Sign up for a developer token http://aws.amazon.com/ http://www.google.com/apis/maps/ http://api.search.yahoo.com/webservices/re How can I build a mashup? What do you want to do? Which data you need? APIs/RSS available? How is your programming skills? Semi-Technical Skills Geek • Use mashup editors Microsoft Popfly Yahoo! Pipes QEDWiki by IBM Google Mashup Editor (Coming) Serena Business Mashups Dapper JackBe Presto Wires Start coding Start Configuring

  18. Mashup Editors

  19. Mashup Editors

  20. Mashup Editors

  21. Mashup Editors

  22. Mashup Editors

  23. Mashup Editors Limitations • Focus only on providing encapsulated access to (some) public APIs and feeds (rather than querying data sources). • Still require programming skills. • Cannot play the role of a general-purpose data retrieval, as mashups are sophisticated applications. • Lacks a formal framework for pipelining mashups.

  24. Vision and Challenges • Instead of accessing a method in an API in a programmatic style, can these APIs act as query end-points over http (i.e. a URL is a query). • Regard the internet as a database, where a data source is seen as a table, and a mashup is a query. • A Mashup can be a simple inquiry (e.g., Hacker’s articles after 2000). • In short, allow (casual users) to search and consume the Data Web intuitively, like we use search engines (or at least the “advance search” in search engines). • But the problem then is: users need to know the schema and technical details of the data sources they want to query. Jarrar-University of Cyprus

  25. Vision and Challenges How a user can query a source without knowing its schema, structure, and vocabulary? DateSources SELECT S.Title FROM Google.Scholar S Where (S.Author=‘Hacker’) Union SELECT P.PattentTitle FROM Ggoogle.Patent P Where (P.Inventor =‘Hacker’) Union SELECT A.Title FROM Citeseer A Where (P.Author =‘Hacker’) Jarrar-University of Cyprus

  26. Vision and Challenges How a user can query a source without knowing its schema, structure, and vocabulary? DateSources SELECT S.Title FROM Google.Scholar S Where (S.Author=‘Hacker’) Union SELECT P.PattentTitle FROM Ggoogle.Patent P Where (P.Inventor =‘Hacker’) Union SELECT A.Title FROM Citeseer A Where (P.Author =‘Hacker’) Jarrar-University of Cyprus

  27. Vision and Challenges http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Some data sources may come without a schema at all, as: http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” PREFIX S1: <http://site1.com/rdf> PREFIX S2: <http://site1.com/rdf> SELECT ? ArticleTitle FROM <http://site1.com/rdf> FROM <http://site2.com/rdf> WHERE { {{?X S1:Title ?ArticleTitle}UNION {?X S2:Title ?ArticleTitle}} {?X S1:Author ?X1} UNION {?X S2:Author ?X1} {?X S1:PubYear ?X2} UNION {?X S2:Year ?X2} FILTER regex(?X1, “^Hacker”) FILTER (?X2 > 2000)} Hacker’s articles after 2000 Programmers usually explore such sources by eyes, and remember the vocabulary and structure…!! (Casual users?)

  28. MashQL Jarrar-University of Cyprus

  29. MashQL • A simple query language for the Data Web, in a mashup style. • MashQL allows querying a dataspace(s) without any prior knowledge about its schema, vocabulary or technical details (a source may not have a schema al all). Explore unknown graph • Does not assume any knowledge about RDF, SPARQL, XML, or any technology, to get started. • Users only use drop-lists to formulate queries. • (query-by-diagram/interaction). Jarrar-University of Cyprus

  30. MashQL Example 1 http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Hacker’s Articles after 2000? http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” RDF Input From: • http://www.site1.com/rdf • http://www.site2.com/rdf MashQL • Everything • TitleArticleTitle • Author “^Hacker” • Year\PubYear > 2000 Jarrar-University of Cyprus

  31. MashQL Example 1 http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Hacker’s Articles after 2000? http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” RDF Input a1 a2 4 5 From: • http://www.site1.com/rdf • http://www.site2.com/rdf Types Instances Interactive query formulation MashQL • Everything Everything Jarrar-University of Cyprus

  32. MashQL Example 1 http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Author Cites Publisher PubYear Title Year Hacker’s Articles after 2000? http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” RDF Input From: • http://www.site1.com/rdf • http://www.site2.com/rdf MashQL • Everything Title ArticleTitle Jarrar-University of Cyprus

  33. MashQL Example 1 http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Author Cites Publisher PubYear Title Year Equals Contains OneOf Not Between LessThan MoreThan Hacker’s Articles after 2000? http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” RDF Input From: • http://www.site1.com/rdf • http://www.site2.com/rdf MashQL • Everything • Title Article title Con Author Hacker Jarrar-University of Cyprus

  34. MashQL Example 1 http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Publisher PubYear Title Year OneOf Not Between LessThan MoreThan Hacker’s Articles after 2000? http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” RDF Input From: • http://www.site1.com/rdf • http://www.site2.com/rdf MashQL • Everything • Title Article title • Author “^Hacker” mor Year \ PubYe 2000 Jarrar-University of Cyprus

  35. MashQL Example 1 http:www.site1.com/rdf <:a1> <:Title> “Web 2.0” <:a1> <:Author> “Hacker B.” <:a1> <:Year> 2007 <:a1> <:Publisher> “Springer” <:a2> <:Title> “Web 3.0” <:a2> <:Author> “Smith B.” <:a2> <:Cites> <:a1> Hacker’s Articles after 2000? http:www.site2.com/rdf <:4> <:Title> “Semantic Web” <:4> <:Author> “Tom Lara” <:4> <:PubYear> 2005 <:5> <:Title> “Web Services” <:5> <:Author> “Bob Hacker” RDF Input From: • http://www.site1.com/rdf • http://www.site2.com/rdf PREFIX S1: <http://site1.com/rdf> PREFIX S2: <http://site1.com/rdf> SELECT ? ArticleTitle FROM <http://site1.com/rdf> FROM <http://site2.com/rdf> WHERE { {{?X S1:Title ?ArticleTitle}UNION {?X S2:Title ?ArticleTitle}} {?X S1:Author ?X1} UNION {?X S2:Author ?X1} {?X S1:PubYear ?X2} UNION {?X S2:Year ?X2} FILTER regex(?X1, “^Hacker”) FILTER (?X2 > 2000)} MashQL • Everything • Title Article title • Author “^Hacker” • Year/PubYear > 2000

  36. MashQL Example 2 RDF Input URL: • http://www4.wiwiss.fu-berlin.de/dblp/ MashQL The recent articles from Cyprus • Article • Title ArticleTitle • Author • Address • Country “Cyprus” • Year > 2008  Retrieve every Article that has a title, written by an author, who has an address, this address has a country called Cyprus, and the article published after 2008. Jarrar-University of Cyprus

  37. The Intuition of MashQL RDF Input URL: • http://www4.wiwiss.fu-berlin.de/dblp/ • A query is a tree • The root is called the query subject. • Each branch is a restriction. • Branches can be expanded, • (information path) • Object value filters MashQL Article • Article • Title ArticleTitle • Author • Address • Country “Cyprus” • Year > 2008 Title ?ArticleTitle Address ?X11 Author ?X1 Country ?X111 = “Cyprus” Year ?X2 < 2008 Def.A Query Q with a subject S, denoted by Q(S), is a set of restrictions on S. Q(S) = R1 AND … AND Rn. Dif. A Subject S  (I  V), where I is an identifier and V is a variable. Dif. A Restriction R =<Rx , P, Of>, where Rx is an optional restriction prefix that can be (maybe | without), P is a predicate (P  I  V), and Of is an object filter.

  38. The Intuition of MashQL RDF Input URL: • http://www4.wiwiss.fu-berlin.de/dblp/ • An Object filter is one of : • Equals • Contains • MoreThan • LessThan • Between • one of • Not(f) • Information Path (sub query) MashQL • Article • Title ArticleTitle • Author • Address • Country “Cyprus” • Year > 2008 • Def.An object filter Of= <O, f>, where O is an object and f is a filtering function one of : • Of = <O>, where O is an object, O  V  I. • Of = <O, Equals(X, T, Lt)>, where X can be a variable or a constant, T is a datatype, and Lt is a language tag. • Of = <O, Contains(X, T, Lt)>, where O is an object variable, X is a regex literal, T is a data type, and Lt is a language. • Of = <O, MoreThan(X, T)>, where O is an object variable, X is a variable or a constant, T is a datatype. • Of = <O, LessThan(X, T)>, where O is an object variable, X is a variable or a constant, T is a datatype identifier. • Of = <O, Between(X, Y, T)>, where X and Y are variables or constants, T is a datatype identifier. • Of = <O, OneOf(V)>, where O is an object variable, and V is a set of values {v1, ... , vn}, vi is a variable or constant. • Of = <O, Not(f)>, where f is one of the functions defined above. • Of = <O, Qi(O)>, where O is an object (O  V  I), and Qi(O) is a sub-query with O being the query subject. Jarrar-University of Cyprus

  39. More MashQL Constructs • Resection Operators {Required, Maybe, or Without} • All restriction are required (i.e. AND), unless they are prefixed with • “maybe” or “without” SELECT ?PersonName, ?University WHERE { ?Person :Name ?PersonName. ?Person :WorkFor :Yahoo. OPTIONAL{?Person :StudyAt ?University} OPTIONAL{?Person :Salary ?X1} FILTER (!Bound(?X1))} } Jarrar-University of Cyprus

  40. More MashQL Constructs • Union operator (denoted as “\”) between Objects, Predicates, Subjects and Queries SELECT ?Person WHERE { ?Person :WorkFor :Google UNION ?Person WorkFor :Yahoo} SELECT ?FName WHERE { ?Person :Surname ?FName UNION ?Person :Firstname ?FName} SELECT ?AgentName, ?AgentPhone WHERE { {?Person rdf:type :Person. ?Person :Name ?AgentName. ?Person :Phone ?AgentPhone} UNION {?Company rdf:type :Company. ?Company :Name ?AgentName. ?Company :Phone ?AgentPhone}} SELECT ?CustName, WHERE { ?Person :Name ?CustName. UNION {?Company :Title ?CustName. ?Company :City ?X1. FILTER regex(?X1, “Paris”)}}

  41. More MashQL Constructs • And several other constructs, including: • Types and Reverse Predicates • Datatypes and Language Tags • …. Jarrar-University of Cyprus

  42. Formal Syntax and Semantics Def.1 (Dataset): A dataset D is a set of triples, each triple t is formed as <S, P, O>, where S  I, P  I, and O  I  L. Def.2 (Typed Literals): Every object literal must have a datatype D: If O  L then O  D. Def.3 (Language Tags): An object literal (O  L) may have a language tag Lt. Def. 4 (Query): A Query Q with a subject S, denoted by Q(S), is a set of restrictions on S. Q(S) = R1 AND … AND Rn. Def. 5 (Subject): A subject S  (I  V), where I is an identifier and V is a variable. Def. 6 (Restriction): A restriction R =<Rx , P, Of>, where Rx is an optional restriction prefix that can be (maybe | without), P is a predicate (P  I  V), and Of is an object. Def.7 (Object Filter): An object filter Of= <O, f>, where O is an object and f is a filtering function. An object filter can have one of the following nine forms: Of = <O>, where O is an object, O  V  I. This is the simplest object filter, i.e., it does not add any restriction on the object value of the retrieved triples. Of = <O, Equals(X, T, Lt)>, where X can be a variable or a constant, T is a datatype, and Lt is a language tag. This filter restricts the retrieved results, such that, the object value O should be equal to X, with datatype T, and with language Lt. Of = <O, Contains(X, T, Lt)>, where O is an object variable, X is a regex literal, T is a data type, and Lt is a language. This filter restricts the retrieved results, such that, the object value O should be equal to regex(X), with datatype T, and with languageLt. A regex literal is a literal that contains a regular expression matching pattern. Of = <O, MoreThan(X, T)>, where O is an object variable, X is a variable or a constant, T is a datatype. This filter restricts the retrieved results, such that, the object value O should be more than X and with datatype T. Of = <O, LessThan(X, T)>, where O is an object variable, X is a variable or a constant, T is a datatype identifier. This filter restricts the retrieved results, such that, the object value O should be less than X and with datatype T (see rule-9). Of = <O, Between(X, Y, T)>, where X and Y are variables or constants, T is a datatype identifier. This filter restricts the retrieved results, such that, the object value O should be more than or equals X, less than or equals Y, and with datatype T. Of = <O, OneOf(V)>, where O is an object variable, and V is a set of values {v1, ... , vn}, vi is a variable or constant. This filter restricts the retrieved results, such that, the object value O should be equal to one of the values in V. Of = <O, Not(f)>, where f is one of the functions defined above. This filter extends all of the above functions with simple negation. The filter is same as the Equals filter but with negation, i.e., Not Equal. Of = <O, Qi(O)>, where O is an object (O  V  I), and Qi(O) is a sub-query with O being the query subject. The restrictions defined in the sub-query Qi(O) should be satisfied as well. Notice that this definition is recursive; however, this does not mean the query itself is recursive. Def.8 (Types): A subject (S  I) or an object (O  I) can be prefixed with “a” or “an” to mean the instances of this subject/object type, instead of the subject/object itself. Def.9 (Union): A union can be declared between objects, predicates, subjects and/or queries, in the following forms: On = <O1\O2 \ . . . \On>, to indicate unions between objects, where Oi I. Pn = <P1\P2 \ . . . \Pn>, toindicate unions between predicates, where Pi I. Sn = <S1\S2 \ . . . \Sn>, toindicate unions between subjects, where Si I. Qn = <Q1\Q2\ . . . \Qn>, to indicate unions between queries. Def.10 (Reverse): <~P> indicates the reverse of the predicate P. Let R1 be a restriction on S such that <S P O>, and R2 be <O ~P S>, R1 and R2 have the same meaning. Jarrar-University of Cyprus

  43. MashQL Queries • In the background, MashQL queries are translated into and executed as SPARQL queries. • At the moment, we focus on RDF (/RDFa) as a data format, and SPARQL (/Oracle’s SPARQL) as a backend query language. However, MashQL can be easily mappable to other query languages. • MashQL is not merely a user interface, by also a query language with its intuition (it focuses on path pattern, rather than triple pattern). Jarrar-University of Cyprus

  44. MashQL-SPARQL Mapping Rules Rule-1: The symbol  before a variable means that it will be returned in the results; i.e., included in the SELECT part of in SPARQL. If the output of the query is input to another, use “CONSTRUCT *”. Rule-2: In any of the following rules, if a subject, predicate, or object is italicized: it is seen as a SPARQL variable, i.e. prefixed with “?”. Rule-3: If S is a subject and R = < , P, Of>, the mapping is: {S P O}. Rule-4: If S is a subject and R = <maybe, P, Of>, the mapping is: {OPTIONAL{S P O}}. Rule-5: If S is a subject and R = < without, P, Of>, the mapping is: {S P O. FILTER (!bound(?O))}. Rule 6. If Of = <O, Equals(X, T, Lt)>: Append the mapping with: FILTER(?O = X) If T Null: Append the mapping with: FILTER(datatype(?O)=T) If Lt Null: Append the mapping with: FILTER(lang(?O) = Lt) Rule 7. If Of = Contains(X, T, Lt)>: Append the mapping with: FILTER regex(?O, X) If T Null: Append the mapping with: FILTER(datatype(?O)=T) If Lt Null: Append the mapping with: FILTER(lang(?O) = Lt) Rule 8. If Of = <O, MoreThan(X, T)>: Append the mapping with: FILTER(?O > X) If T Null: Append the mapping with: FILTER(datatype(?O=T) Rule 9. If Of = <O, LessThan(X, T)>: Append the mapping with: FILTER(?O < X) If T  Null: Append the mapping with: FILTER(datatype(?O=T) Rule 10. If Of = <O, Between(X, Y, T)>: Append the mapping with: FILTER(?O >=X)&& FILTER(?O<=Y) If T Null: Append the mapping with: FILTER(datatype(?O)=T) Rule 11. If Of = <O, OneOf (V)>: Append the mapping with: {FILTER(?O = V1)|| . . . || FILTER(?O = Vn)} If Vi is a regex-ed literal, the ith filter above should be replaced with: FILTER Regex(?O, Vi) Rule 12. If Of = <O, Not(f)>: The f filter will be generated as above, but with a negation. Rule 13. If Of = <O, Qi(O)>: Repeat all mapping rules to generate Qi(O). Rule 14. If a subject S is prefixed with “a” or “an”: Append the mapping with: {?S rdf:type :S} Rule 15. If an object O is prefixed with “a” or “an”: Append the mapping with: {?O rdf:type :O} Rule 16. Given On , If n >1 and Oi I : The mapping in rules 3-4 will be:{{S P :O1} UNION . . . UNION {S P :On}} Rule 17. Given Pn , If n >1 and Pi I : The mapping in rules 3-4 will be: {{S :P1 O} UNION . . . UNION {S :Pn O}} Rule 18. Given Sn , If n >1 and Si I : Regenerate the query n times, each time with Si as a root, and with a UNION between the queries. Rule 19. Given Qn , If n >1 : Add UNION between the n queries. Rule 20. If S is a subject and R = <~P, O>, the mapping is: {O P S}. Also mapped into SQL and Oracle’s SPARQL Jarrar-University of Cyprus

  45. MashQL Compilation • MashQL Markup: an XML Schema to represent pipes in XML. • The reference grammar (Technical specification). Jarrar-University of Cyprus

  46. MashQL Compilation • Depending on the pipeline structure, MashQL generates either SELECT or CONSTRUCT queries: • SELECT returns the results in a tabular form • (e.g. ArticleTitle, Author) • CONSTRUCT returns the results in a triple form • (e.g. Subject, Predicate, Object). … CONSTRUCT * WHERE{?Job :JobIndustry ?X1. ?Job :Type ?X2. ?Job :Currency ?X3. ?Job :Salary ?X4. FILTER(?X1=“Education”|| ?X1=“HealthCare”) FILTER(?X2=“Full-Time”|| ?X2=“Fulltime”)|| ?X2=“Contract”) FILTER(?X3=“^Euro”|| ?X3=“^€”) FILTER(?X4>=75000|| ?X4<=120000)} … SELECT ?Job ?Firm WHERE {?Job :Location ?X1. ?X1 :Country ?X2. FILTER (?X2=“Italy”||?X2=“Spain”)|| ?X2=“Greece”||?X2=“Cyprus”)} OPTIONAL{{?job :Organization ?Firm} UNION {?job :Employer ?Firm}} Jarrar-University of Cyprus

  47. System Model (Online Mashup Editor)  Site1 Client Mashup Server  DataSources(AJAX)  Download(http) Loader Query Site2 B.Query(AJAX)   RunQuery(http)   Bulk-load  Results Render Results(http)   Site3 (Wikipedia Titles, 28 MB zip, 316 MB nt, 2.7 M triples): Download (37 s, 600KB/s) Bulk-Load Oracle-RDF (70 Sec, 40K triples per Sec). Query (one/few Sec.) • The output of a mashup can be an input to another. • (Enabling people to collaborate and innovate, build of each others’ results) Jarrar-University of Cyprus

  48. MashQL Editor Under Construction Jarrar-University of Cyprus

  49. MashQL Firefox Add-On (Light-mashups @ your browser)

  50. Use Case: Job Seeking A mashup of job vacancies based on Google Base and on Jobs.ac.uk. … CONSTRUCT * WHERE { {{?Job :Category :Health}UNION {?Job :Category :Medicine}} ?Job :Role ?X1. ?Job :Salary ?X2. ?X2 :Currency :UPK. ?X2 :Minimun ?X3. FILTER(?X1=“Research” || ?X1=”Academic”) FILTER (?X3 > 50000) } … CONSTRUCT * WHERE{?Job :JobIndustry ?X1. ?Job :Type ?X2. ?Job :Currency ?X3. ?Job :Salary ?X4. FILTER(?X1=“Education”|| ?X1=“HealthCare”) FILTER(?X2=“Full-Time”|| ?X2=“Fulltime”)|| ?X2=“Contract”) FILTER(?X3=“^Euro”|| ?X3=“^€”) FILTER(?X4>=75000|| ?X4<=120000)} … SELECT ?Job ?Firm WHERE {?Job :Location ?X1. ?X1 :Country ?X2. FILTER (?X2=“Italy”||?X2=“Spain”)|| ?X2=“Greece”||?X2=“Cyprus”)} OPTIONAL{{?job :Organization ?Firm} UNION {?job :Employer ?Firm}} Jarrar-University of Cyprus

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