Enterprise Information Integration using Semantic Web Technologies:

Transcription

1 Enterprise Information Integration using Semantic Web Technologies: RDF as the Lingua Franca David Booth, Ph.D. HP Software Semantic Technology Conference 20-May-2008 In collaboration with Steve Battle, HP Labs Latest version of these slides: Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice

2 Disclaimer This work reflects research and is presented for discussion purposes only. No product commitment whatsoever is expressed or implied. Furthermore, views expressed herein are those of the author and do not necessarily reflect those of HP. 2

3 Outline PART 0: The problem PART 1: RDF: The lingua franca for information exchange Why 1. Focus on semantics 2. Easier data integration 3. Easier to bridge other formats/models 4. Looser coupling How 1. RDF message semantics 2. REST-based SPARQL endpoints 3. XML with GRDDL transformations 4. Aggregators PART 2: POC: A SPARQL adaptor for UCMDB What is UCMDB SPARQL adaptor 3

4 PART 0 The problem 4

5 Problem 1: Integration complexity Multiple producers/consumers need to share data Tight coupling hampers independent versioning Discovery Provisioning Compliance Management Incident Management Release Management Change Management Release Managers Compliance Managers Operation Centers Source Control Monitoring Networking Engineers Unix System Administrators Storage Administrators Networking Administrators Windows System Administrators Ticketing 5

6 Problem 2: Babelization Proliferation of data models (XML schemas, etc.) Parsing issues influence data models No consistent semantics Data chaos Tower of Babel, Abel Grimmer ( ) 6

7 PART 1 RDF: The lingua franca for information exchange 7

8 Why? Four reasons... 8

9 Why? 1. Focus on semantics XML: Schema is focused on how to serialize Constrains more than the model Parent/child and sibling relationships are not named RDF: Are their semantics documented? E.g., does sibling order matter? One URI per concept Syntax independent Who cares about syntax? 9

10 Why? 2. Easier data integration 10

11 Why? 2. Easier data integration Blue App has model 11

12 Why? 2. Easier data integration Red App has model Need to integrate Red & Blue models 12

13 Why? 2. Easier data integration Step 1: Merge RDF Same nodes (URIs) join automatically 13

14 Why? 2. Easier data integration Step 2: Add relationships and rules (Relationships are also RDF) 14

15 Why? 2. Easier data integration Step 3: Define Green model (Making use of Red & Blue models) 15

16 Why? 2. Easier data integration What the Blue app sees: No difference! 16

17 Why? 2. Easier data integration What the Red app sees No difference! 17

18 Why? 3. RDF helps bridge other formats/models Producers and consumers may use different formats/models Rules can specify transformations Inference engine finds path to desired result model X A1 A2 Y RDF Model Transform A3 B1 B2 C1 18 Z Ontologies Ontologies Ontologies Rules & Rules Rules C2

19 Why? 4. Looser coupling Without breaking consumers: Ontologies can be mixed and extended Triples can be added Producer & consumer can be versioned more independently 19

20 Example of looser coupling RedCust and GreenCust ontologies added Blue app is not affected (Blue app) Consumer Producer 20

21 How? Four ways... 21

22 How? 1. RDF message semantics Interface contract specifies RDF, regardless of serialization RDF pins the semantics Consumer RDF Producer 22

23 How? 2. REST-based SPARQL endpoints Consumer RDF HTTP SPARQL Producer 23

24 REST-based SPARQL endpoints Why REST: HTTP is ubiquitous Simpler than SOAP-based Web services (WS*) Looser process coupling 24

25 REST-based SPARQL endpoints Why SPARQL: One endpoint supports multiple data needs Each consumer gets what it wants Insulates consumers from internal model changes Inferencing transforms data to consumer's desired model Looser data coupling 25

26 How? 3. XML with GRDDL transformations GRDDL is a W3C standard GRDDL permits RDF to be "gleaned" from XML XML document or schema specifies desired GRDDL transformation GRDDL transformation produces RDF from XML document Mostly intended for getting microformat and other data/metadata from HTML pages 26

27 Using GRDDL for XML document semantics Each XML format can be viewed as a custom serialization of RDF! GRDDL transformation produces semantics of the XML document Helps bridge XML and RDF worlds Same XML document can be consumed by: Legacy XML app RDF app App interface contract can specify RDF Serializations can vary Semantics are pinned by RDF 27

28 Using GRDDL for XML document semantics Service Client Normalize to RDF Serialize as XML/other/RDF Core App Processing RDF Engine / Store See: 28

29 How? 4. Aggregators Gets data from multiple sources Provides data to consumers X A1 A2 A3 Y SPARQL Aggregator B1 B2 C1 Z Ontologies Ontologies Rules & Rules Rules C2 29

30 Aggregator Conceptual component Not necessarily a separate physical service Handles mechanics of getting data Different adaptors for different sources REST, WS*, Relational, XML, etc. Diverse data models Might do caching and query distribution (federation) Provides model transformation Plug in ontologies and inference rules as needed 30

31 PART 2 Proof-of-Concept: A SPARQL adaptor for UCMDB 31

32 IT Service Management (ITSM) Manage IT environment Configuration Management Data Base (CMDB) is central 32

33 The HP Universal CMDB (UCMDB) Goal: Maintain a comprehensive and current record of all configuration items (CIs) and their relationships CMDB : Configuration Management DB 33

34 Example: host information Host properties One particular host machine 34

35 SPARQL adaptor Uses existing SOAP interface to UCMDB Enables SPARQL queries Results can be RDF No model transformation (yet) SPARQL adaptor SOAP interface HP UCMDB 35

36 Architecture of SPARQL adaptor SPARQL adaptor Run Time SOAP interface SPARQL compile TQL submit HP UCMDB RDF lift lift XML Database CMDB metadata Design Time export OWL 36

37 UCMDB ontology The HP UCMDB ontology defines CI types and relationship hierarchies. Derived automatically from HP UCMDB metadata. 37

38 Jena based implementation Jena, ARQ, Joseki developed at HP Labs*. Jena : Semantic Web toolkit ARQ : Query Engine Joseki : SPARQL server RDF, OWL, inference SPARQL query algebra, evaluation SPARQL protocol * 38

39 Query returning a table Select the names of host servers on the network with addresses from SELECT?host_name WHERE { [ a object:network ] attr:network_netaddr " " ; link:member [ a object:host ; attr:host_dnsname?host_name ] } host_name "ILDTRD129" ^^< "JONI" ^^< "MBADIR-IL" ^^< 39

40 Query returning an RDF subgraph Describe a network ( ) with host servers containing a DB. 40

41 Example RDF result set Database Host Host 41

42 Outline PART 0: The problem PART 1: RDF: The lingua franca for information exchange Why 1. Focus on semantics 2. Easier data integration 3. Easier to bridge other formats/models 4. Looser coupling How 1. RDF message semantics 2. REST-based SPARQL endpoints 3. XML with GRDDL transformations 4. Aggregators PART 2: POC: A SPARQL adaptor for UCMDB What is UCMDB SPARQL adaptor 42

43 2008 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice Questions?